22 Burst results for "Pramod"

"pramod" Discussed on Software People Stories

Software People Stories

03:00 min | 3 months ago

"pramod" Discussed on Software People Stories

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What To Do When a Patient Feels Worse on an Antidepressant

The Carlat Psychiatry Podcast

01:34 min | 3 months ago

What To Do When a Patient Feels Worse on an Antidepressant

"When a patient says they feel worse on an antidepressant. The first thing to do is to rule out physical side effects like nausea fatigue and insomnia one to pay attention to is academia as patients might have difficulty describing this inner sense of restlessness which is more often associated with anti psychotics but academia can occur on antidepressants as well particularly serota. Energetic ones and agatha can cause anxiety insomnia even suicide -ality so it might be the reason that they're mood is worse on an antidepressant dot in how concert energetic antidepressants calls academia. I thought it was caused. By dopamine blockade it is thought that the inhibitory effects of serotonin have indirect effects on the dopamine system and that it can lead to dopamine antagonism there in the stratum another side effect. that's related to academia. You might see on sarah. Synergetic antidepressants is restless. Legs syndrome which is almost like academia night. If that happens you could add gabba. Penton or pramod. Pack saul both of which treat restless legs in academia and both of which have psychiatric benefits gabba. Penton helps sleep an anxiety and promo pack saul helps depression but another option would be the switch to be appropriate wellbutrin in a randomized controlled. Trial bupropion treated restless leg syndrome. Even when it was dosed in the morning perhaps through its dopaminergic

Nausea Fatigue Serota Insomnia Legs Syndrome Penton Pramod Sarah Academia Depression
What To Do When a Patient Gets Worse on an Antidepressant

The Carlat Psychiatry Podcast

01:34 min | 3 months ago

What To Do When a Patient Gets Worse on an Antidepressant

"When a patient says they feel worse on an antidepressant. The first thing to do is to rule out physical side effects like nausea fatigue and insomnia one to pay attention to is academia as patients might have difficulty describing this inner sense of restlessness which is more often associated with anti psychotics but academia can occur on antidepressants as well particularly serota. Energetic ones and agatha can cause anxiety insomnia even suicide -ality so it might be the reason that they're mood is worse on an antidepressant dot in how concert energetic antidepressants calls academia. I thought it was caused. By dopamine blockade it is thought that the inhibitory effects of serotonin have indirect effects on the dopamine system and that it can lead to dopamine antagonism there in the stratum another side effect. that's related to academia. You might see on sarah. Synergetic antidepressants is restless. Legs syndrome which is almost like academia night. If that happens you could add gabba. Penton or pramod. Pack saul both of which treat restless legs in academia and both of which have psychiatric benefits gabba. Penton helps sleep an anxiety and promo pack saul helps depression but another option would be the switch to be appropriate wellbutrin in a randomized controlled. Trial bupropion treated restless leg syndrome. Even when it was dosed in the morning perhaps

Nausea Fatigue Serota Insomnia Legs Syndrome Penton Pramod Sarah Academia Depression
"pramod" Discussed on Telecom Reseller

Telecom Reseller

03:20 min | 7 months ago

"pramod" Discussed on Telecom Reseller

"But again, looks is missing around. For analog devices and some other industries. And it seems to me also that in addition to features and functions that come. Down to the end user wherever they are. There's probably enterprise wide things like maybe public announcements and other things maybe even security on the. Integrated with PBX. Yes. Yes you're correct. So I can give you some real example from one of Pasta Migration again. Additions well, no for crafter misuse or tool to migrate factories and the Fox News connected look devices to their put not directly related to me and some of them that doesn't a the to do related to telephony. So when you do that For example, you mentioned interviewing. So event, there was an ways to announce emergencies for all the employees in strip in a location in the. Way. Too they connected devices to the telephone system for athletics by to flash when an incoming call because there was lots of noise around in the factory in. So many features this are used In manufacturer organizations are currently this beyond when you move to cloud and you and they got, it doesn't mean that you cannot migrated customers to cloud, but you need to come up with the right solutions and there are solutions to overcome these limitations. So you know you've been talking a little bit about how a lot of companies anticipated only one type of worker information worker when we sorta rolled out the cloud. I'm also wondering if that's true for migrations in other words, I think everyone kind of imagines. We'll just move the whole company all at once from Prem Cloud. Are there occasions when that doesn't happen when the migration happens in such a way that season maybe side-by-side, Pramod Cloud. Yes opposed to. Most of the enterprise which. ARE BUILDING PHYS way. So you. And we're talking about enterprises which are usually spend around mini sized and maybe many geographies maybe Around the globe. So you typically migrate maybe side by side, even building by building and in some cases slow by slow So you. Actually. they st to challenges when you are doing as ration- First of all, you've identify which users the book fuses can be migrated. Rated.

Pramod Cloud Prem Cloud Fox News
"pramod" Discussed on Podcasts – Telecom Reseller

Podcasts – Telecom Reseller

02:47 min | 7 months ago

"pramod" Discussed on Podcasts – Telecom Reseller

"Maybe even industry specific he's not just inflammation walkers Ten ignored but most more than clouds. However. I do see in recent months is some in nouncement around features for frontliners and things like that. But again, looks is missing around. For analog devices and some other industries. And it seems to me also that in addition to features and functions that come. Down to the end user wherever they are. There's probably enterprise wide things like maybe public announcements and other things maybe even security on the. Integrated with PBX. Yes. Yes you're correct. So I can give you some real example from one of Pasta. My patients again, my dishes, one of for crafter misuse or tool to migrate factories and the Fox News connected look devices to their put not directly related to me and some of them that doesn't a the to do related to telephony. So when you do this For example, you mentioned interviewing so been, there was an ways to announce emergencies for all the employees in strip in a location in the. Was Way. They connected in devices to the telephone system for athletics vice to flash when there is an incoming call because there was lots of noise around in the factory in. So many features this are used In manufacturer organizations are currently this beyond when you move to cloud and you and they got, it doesn't mean that you cannot migrated customers to the cloud, but you need to come up with the right solutions and there are solutions to overcome these limitations. So you know you've been talking a little bit about how a lot of companies anticipated only one type of worker information worker when we sorta rolled out the cloud. I'm also wondering if that's true for migrations in other words, I think everyone kind of imagines we'll just move the whole company all at once from Prem cloud. Are there occasions when that doesn't happen when the migration happens in such a way that season maybe side-by-side Pramod Cloud..

Pramod Cloud Prem cloud Fox News
"pramod" Discussed on Learning Machines 101

Learning Machines 101

03:27 min | 9 months ago

"pramod" Discussed on Learning Machines 101

"An. . Important key idea for many machine learning algorithms is the concept of gradient descent. . The basic idea is that suppose we have a machine learning algorithm whose performance can be measured in terms of performance function. . An example of a performance function might the prediction error of the learning machine? ? Learning machines prediction error depends upon the training stimuli that we used to train the learning machine, , but it also depends upon the parameters of the learning machine. . As previously noted, , the parameters of learning machine are justed during the learning process with the goal of improving the learning machines performance. . Note that the prediction era of the learning machine is essentially a function of its parameter values. . If the learning machine has a good set of parameter values than it will have a low prediction error if learning machine has a bad set of prominent values that will have a high prediction error. . So, , this is where the concept of great sent comes in. . Our goal is to come up with a learning algorithm which adjust the parameters of learning machine at each time. . The learning machine has an experienced in its environment in such a way that the. . Prediction error of the learning machine decreases a little bit. . And this is done by an approach called gradient descent. . It, , turns out you can prove the following important key theorem. . Suppose that we make a very small perturbation to all of the learning machine parameters and specifically suppose this perturbation. . That we add to a particular parameter, , value is defined as some negative number multiplied by the derivative. . Of. . Mode applied by the derivative of the prediction error with respect to that parameter value. . It can be shown that this method of updating the parameters has the property that the prediction error for the. . Parameter values will always be a little smaller than the prediction errors for the current parameter values. . This is called the method of gradient descent. . and. . This method of updating the parameters of a learning machine so that it's prediction error performance improves is a key idea for many machine learning algorithms. . The concept of great upset was first introduced in episode sixty, five , of learning machines WANNA. . One. . Now at episode twenty, , three of learning machines went to when we introduce the concept of deep learning neural networks in a feet forward deep learning neural network. . A unit is essentially a function which computes estate value from its pramod values and the states of the units which is connected. . Each day value is represented as a number. . Intuitively, we , can kind of think of the state of a unit as analogous to the degree of activity of neurons, , perhaps inspiring frequency and the parameters specify the degree to which the activity of one on influences another. . Of course, , the next challenge is the method for how do we compute these derivatives to implement a gradient set our them in many machine learning applications. . The prediction error is a very complicated function of the parameter values. . So it's not immediately obvious how to compute the necessary derivatives. . In fact, , even computing, , just one of the derivatives can be extremely challenging. . Yet, , we often need to compute many such derivatives. .

intern pramod
How to Use Calculus to Design Learning Machines

Learning Machines 101

03:27 min | 9 months ago

How to Use Calculus to Design Learning Machines

"An. Important key idea for many machine learning algorithms is the concept of gradient descent. The basic idea is that suppose we have a machine learning algorithm whose performance can be measured in terms of performance function. An example of a performance function might the prediction error of the learning machine? Learning machines prediction error depends upon the training stimuli that we used to train the learning machine, but it also depends upon the parameters of the learning machine. As previously noted, the parameters of learning machine are justed during the learning process with the goal of improving the learning machines performance. Note that the prediction era of the learning machine is essentially a function of its parameter values. If the learning machine has a good set of parameter values than it will have a low prediction error if learning machine has a bad set of prominent values that will have a high prediction error. So, this is where the concept of great sent comes in. Our goal is to come up with a learning algorithm which adjust the parameters of learning machine at each time. The learning machine has an experienced in its environment in such a way that the. Prediction error of the learning machine decreases a little bit. And this is done by an approach called gradient descent. It, turns out you can prove the following important key theorem. Suppose that we make a very small perturbation to all of the learning machine parameters and specifically suppose this perturbation. That we add to a particular parameter, value is defined as some negative number multiplied by the derivative. Of. Mode applied by the derivative of the prediction error with respect to that parameter value. It can be shown that this method of updating the parameters has the property that the prediction error for the. Parameter values will always be a little smaller than the prediction errors for the current parameter values. This is called the method of gradient descent. and. This method of updating the parameters of a learning machine so that it's prediction error performance improves is a key idea for many machine learning algorithms. The concept of great upset was first introduced in episode sixty, five of learning machines WANNA. One. Now at episode twenty, three of learning machines went to when we introduce the concept of deep learning neural networks in a feet forward deep learning neural network. A unit is essentially a function which computes estate value from its pramod values and the states of the units which is connected. Each day value is represented as a number. Intuitively, we can kind of think of the state of a unit as analogous to the degree of activity of neurons, perhaps inspiring frequency and the parameters specify the degree to which the activity of one on influences another. Of course, the next challenge is the method for how do we compute these derivatives to implement a gradient set our them in many machine learning applications. The prediction error is a very complicated function of the parameter values. So it's not immediately obvious how to compute the necessary derivatives. In fact, even computing, just one of the derivatives can be extremely challenging. Yet, we often need to compute many such derivatives.

Pramod
"pramod" Discussed on Learning Machines 101

Learning Machines 101

03:14 min | 9 months ago

"pramod" Discussed on Learning Machines 101

"Before beginning our discussion, , it would be helpful if you're not familiar with vectors matrices to review episode eighty eighty-two Learning Machines WanNa win. . An. . Important key idea for many machine learning algorithms is the concept of gradient descent. . The basic idea is that suppose we have a machine learning algorithm whose performance can be measured in terms of performance function. . An example of a performance function might the prediction error of the learning machine? ? Learning machines prediction error depends upon the training stimuli that we used to train the learning machine, , but it also depends upon the parameters of the learning machine. . As previously noted, , the parameters of learning machine are justed during the learning process with the goal of improving the learning machines performance. . Note that the prediction era of the learning machine is essentially a function of its parameter values. . If the learning machine has a good set of parameter values than it will have a low prediction error if learning machine has a bad set of prominent values that will have a high prediction error. . So, , this is where the concept of great sent comes in. . Our goal is to come up with a learning algorithm which adjust the parameters of learning machine at each time. . The learning machine has an experienced in its environment in such a way that the. . Prediction error of the learning machine decreases a little bit. . And this is done by an approach called gradient descent. . It, , turns out you can prove the following important key theorem. . Suppose that we make a very small perturbation to all of the learning machine parameters and specifically suppose this perturbation. . That we add to a particular parameter, , value is defined as some negative number multiplied by the derivative. . Of. . Mode applied by the derivative of the prediction error with respect to that parameter value. . It can be shown that this method of updating the parameters has the property that the prediction error for the. . Parameter values will always be a little smaller than the prediction errors for the current parameter values. . This is called the method of gradient descent. . and. . This method of updating the parameters of a learning machine so that it's prediction error performance improves is a key idea for many machine learning algorithms. . The concept of great upset was first introduced in episode sixty, five , of learning machines WANNA. . One. . Now at episode twenty, , three of learning machines went to when we introduce the concept of deep learning neural networks in a feet forward deep learning neural network. . A unit is essentially a function which computes estate value from its pramod values and the states of the units which is connected. . Each day value is represented as a number. . Intuitively, we , can kind of think of the state of a unit as analogous to the degree of activity of neurons, , perhaps inspiring frequency and the parameters specify the degree to which the activity of one on influences another. . Of course, , the next challenge is the method for how do we compute these derivatives to implement a gradient set our them

intern pramod
How to Use Calculus to Design Learning Machines

Learning Machines 101

03:14 min | 9 months ago

How to Use Calculus to Design Learning Machines

"Before beginning our discussion, it would be helpful if you're not familiar with vectors matrices to review episode eighty eighty-two Learning Machines WanNa win. An. Important key idea for many machine learning algorithms is the concept of gradient descent. The basic idea is that suppose we have a machine learning algorithm whose performance can be measured in terms of performance function. An example of a performance function might the prediction error of the learning machine? Learning machines prediction error depends upon the training stimuli that we used to train the learning machine, but it also depends upon the parameters of the learning machine. As previously noted, the parameters of learning machine are justed during the learning process with the goal of improving the learning machines performance. Note that the prediction era of the learning machine is essentially a function of its parameter values. If the learning machine has a good set of parameter values than it will have a low prediction error if learning machine has a bad set of prominent values that will have a high prediction error. So, this is where the concept of great sent comes in. Our goal is to come up with a learning algorithm which adjust the parameters of learning machine at each time. The learning machine has an experienced in its environment in such a way that the. Prediction error of the learning machine decreases a little bit. And this is done by an approach called gradient descent. It, turns out you can prove the following important key theorem. Suppose that we make a very small perturbation to all of the learning machine parameters and specifically suppose this perturbation. That we add to a particular parameter, value is defined as some negative number multiplied by the derivative. Of. Mode applied by the derivative of the prediction error with respect to that parameter value. It can be shown that this method of updating the parameters has the property that the prediction error for the. Parameter values will always be a little smaller than the prediction errors for the current parameter values. This is called the method of gradient descent. and. This method of updating the parameters of a learning machine so that it's prediction error performance improves is a key idea for many machine learning algorithms. The concept of great upset was first introduced in episode sixty, five of learning machines WANNA. One. Now at episode twenty, three of learning machines went to when we introduce the concept of deep learning neural networks in a feet forward deep learning neural network. A unit is essentially a function which computes estate value from its pramod values and the states of the units which is connected. Each day value is represented as a number. Intuitively, we can kind of think of the state of a unit as analogous to the degree of activity of neurons, perhaps inspiring frequency and the parameters specify the degree to which the activity of one on influences another. Of course, the next challenge is the method for how do we compute these derivatives to implement a gradient set our them

Pramod
"pramod" Discussed on The Python Podcast.__init__

The Python Podcast.__init__

08:15 min | 1 year ago

"pramod" Discussed on The Python Podcast.__init__

"Being able to provide a significant amount of power to the end user. That's exactly that's something we very much. Strive for and to keep it simple to have only a few concepts that are powerful and general and so you mentioned that some of the original work you were doing was on some of these advanced machine learning algorithms such as reinforcement learning and I was some of the initial use case that you retargeting curious now that has been publicly available for some time and it's been out in the wild. What are some of the other ways that it's being used which you didn't originally anticipate? Yeah one use case that we didn't initially anticipate was serving so inference putting machine learning models production but it turns out that Ray is actually a pretty natural fit for this use case. And and the powerful thing here. The exciting thing is is that when you have an A system that can support not just the serving and inference side of the equation but also the training side. Also you can build online learning applications. That do both where you have. Algorithms and applications that are continuously interacting with the real world and making decisions but also and learning from those interactions and updating themselves updating the models so you have applications that look like there are companies doing this in production where you have data streaming in your incrementally processing this data and a streaming frat fashion and training new recommendation models and then serving recommendations from those models back to the users which then affects the data that you're collecting and there's this tight loop normally when companies implement this kind of application this kind of online learning application. They're actually stitching together. Several different distributed systems that they'll take one for stream processing one for training machine learning models and then one for serving and here we've had companies that are able to take that kind of setup with several different distributed systems that are stitched together and rebuild it all on top of Ray and you know it becomes simpler because it's on top of one framework. They don't have to learn how to use a ton of different distributed systems. It can become faster because you aren't moving data between different systems. It sometimes be expensive and it. Generally you know it can get simpler because you. These different distributed systems are not usually not designed to interface with each other. So if you have if you're using Harvard or distributed tensor flow for training and you're using flink or or spark streaming for the stream processing a flink and horrified were not really designed to just compose together. But if you can do these things on top of rave and it's it can be as natural as just using python and importing different libraries like numb pie and pandas. And you can use all of these things together. Another project in the python ecosystem that is tackling the distributed computation need that. I'm also aware of his desk. And I know that it was originally built for more sort of scientific computation and research workloads. But I'm wondering what your thoughts are on some of the trade offs of when somebody might be looking to use desk versus ray or ways that they can work together. That's a great question and ask has some has some great libraries for data frames and sort of distributed array computation. Which which. We haven't been focused on so much. We've with Ray. We've been very focused on scalable machine learning everything from training machine learning models to reinforcement learning to hyper. Pramod Research to Serving and in the kinds of applications that you know we've been focused on a really benefit from the ability to do stateful computation with actors. I think that's you know in terms of the you know. The what tasks provides at a low level. It's very similar to the ray remote function. Api so the ability to take python functions and scale them up and But a lot of the the scalable machine learning libraries that we're building really require stateful computation. So that's there's some difference in emphasis there. And of course you know I think DASS could be began. Its life focused on large multi core machines and really optimizing for high performance on a single multi-core machine. And you know. We are focused on performance very much. In the large multi core machines setting as well as the cluster setting and really trying to match the performance of low level tools. Like if you were just using. Gpc or on top of Cooper Netease and things like that then using should ideally be Just as fast for the types of workloads that somebody might think of for ray water some of the limitations that they should be aware of or edge cases that they might run into as they're developing the application nor any of these specific design considerations that you found to be beneficial for a successful implementations there couple of different things to be aware of so one the Ray. Api can be pretty low level and so to do to implement all of the features and all of the things you want for your application. You may need to build quite a bit of stuff so for example. If you're looking to do distributed hyper parameter search. Well we provide a great library for scalable hyper parameter search. And so you can do that out of the box on top of Ray and it works well on the other hand if you want to do something like large-scale stream processing. We currently don't have a great library for stream processing and so while it should be possible in principle to do that on top of Ray doing so would require. I building your own stream processing library or building. Something like that or some subset of that before. You can really do that well. And if somebody is trying to convert an existing application to be able to scale out some subset of its functionality. What does the workflow look like for being able to do that? And how does it compare to somebody who's approaching this in a greenfield system? Yes so this is actually an area. Where ray really shines. And we've had the so with Ray. If you want to scale up a python application an existing python application that you already wrote you can often you know not always but often just add a few lines. Change a few lines at a few annotations. Take that code and run it you know anywhere from your laptop to a large cluster and the reason for this and you know the important thing here is not just the number of lines of code. It's nice if you only have to add seven lines of code but the important thing here is that you don't have to restructure your coat you don't have to re architect it and change the abstractions and change the interfaces To to use course it into using riot it should really be something you can do in place and the reason for this is that has to do with the abstractions that ray provides so if you WANNA skip for example if you want to scale up a an arbitrary python application using spark well the core abstraction that sparked provides is a large scale data set like a distributed data set and so if you have an application that is centered around where the the core abstraction is a large data set. And you're manipulating data. Set than spark is the perfect choice on the other hand. If you're trying to do something like Hyper Pramod research or you know Inference and serving where the main abstraction is not really a data set. It might be something like a machine learning experiments or it might be something like a neural network. That you're trying to deploy then coursing that into to scale that up with spark you have to you. Know course it into the state of set abstraction which is not a natural fit and that can lead you to having to re architect your application on the other hand with Ray you know we. Of course we have higher level libraries but at the core ray. Api is not introducing any new concepts. It's not introducing these higher level abstractions. It's just taking the existing concepts of python functions and classes and of course all application all python applications are pretty much built with functions and classes as taking those concepts and translating those into distributed setting. So you have a way of taking you know any application that's built with functions and classes and then ideally with a few just modifications running that in the distributed setting..

Ray Pramod Research Harvard Cooper Netease
Revolution within the haredi sector? Ultra-orthodox women on Instagram

Correspondents Report

07:59 min | 1 year ago

Revolution within the haredi sector? Ultra-orthodox women on Instagram

"On stories featuring ultra-orthodox rallies Middle East correspondent. Eric torture has encountered quantitative aversion to the media to but one story on the unlikeliest of topics gave Eric Harley Perspective on Israel's ultra-orthodox community we were meant to be meeting at a week shop. A boutique high end classy place. For the most discerning fashion-conscious Ultra Orthodox women in Tel Aviv. Instead we were standing outside a row of dingy apartments in the ultra Orthodox neighborhood of bonaire. Brac was this the right address wrecked and dirty prams rusting bikes and scooters crowded the steps. If H Building Ultra Orthodox men watch shirts black coats and lodge black hats rushed past not looking at ABC producer who had an eye waiting on the footpath except for one man who was pushing a baby in a pram. He conspicuously moved closer to US alarmed. By these two men wearing colored clothing amidst crowds of black and white he parked himself in his placid by be nixed staring then he pushed the Pramod Fouad making him move so he could position himself directly across from me all without speaking. I might I contact wondering if he wanted to talk but he just stood at the spice between us clicking a PUMPKIN BETWEEN HIS TEETH CLICK. Click click stay. I looked at flood. Who SHRUGGED THE STANDOFF? Continued with more clicking. I didn't mind this was actually better than some of my previous interactions with ultra Orthodox men when doing a story about men resisting. Israel's conscription lows some allowed us to film in the giant Bible College while others stridently resisted my attempts to film them demonstrating on the streets while filming another story in the same ultra Orthodox neighborhood of Tel Aviv. One man tried to fight me and I hadn't even been filming him one Friday evening. Crowds of Ultra Orthodox men and children blocked dot car on Jerusalem's main highway shouting Shabbat remonstration for breaking the Sabbath by driving police in a truck barreled in scattering them like skittles before blasting the few who remained with a water cannon so by comparison one man standing uncomfortably close to us in clicking Pumpkin. Sade between his teeth was a fairly mild experience. It ended with the arrival of Mary. Bilan stylist fashion blogger and social media personality. A six foot tall colorfully dressed thirty nine year. Old Mother of five who also happens to be ultra Orthodox even though Mary carefully follows the many roles of Ultra Orthodox stress. I didn't notice she was doing sir. Married Ultra Orthodox. Women are required to cover their heads in public. But I didn't know Mary's Long Brown hair was a whig abroad. Orange jump was at odds with the normally muted tones. Worn by many ultra-orthodox women. Her long flowing skirt was so fashionable. I wasn't paying attention. To the fact that also met the strict modesty demands of the ultra Orthodox world. Mary's might a name for herself. Dressing like this in publishing pictures and videos on the social media platform instagram. She calls it modest fashion down playing. It's religious origins. She says Kate Middleton now. The Duchess of Cambridge is an exemplar of this trend which also appeals to women who are not religious modest. She tells me now. Maine's Classy a researcher of Ultra Orthodox culture at Tel Aviv. University seem at celts. Berg told me the same thing. The increased number of ultra Orthodox women in the workplace was leading to some mixing of fashion. Id's Dr Salzburg. Now Kosheh closed. She saw her ultra-orthodox colleagues wearing to work and the religious women in her workplace now being more daring with the address most of the fashion nuance was admittedly lost on me. But Mary said it's allowed religious women to express themselves while avoiding criticism from this strict communities and she showed me a remarkable example of the religious fashion business in the basement below this dingy apartment block we threaded our way past the broken prams bikes and cheap toys past giant rubbish bins in the dark recess below. The building scattering strike cats. As we walked down some steps to a small door disappointingly. There was a buzzer. Instead of a secret knock but three excited female faces appeared and the women inside lead us into a shining Lee brought space. What wools pink panels and down lights gave a glamorous look to this basement workshop and styling studio of Benign Brex most sought after week? Mike is There was six ultra-orthodox women inside busy making wigs that cost from five to thirteen thousand Australian dollars. The hair is human comes from Russia and Sri Lanka Blonde and rid of the most expensive color and h wig is made specifically for the woman ordering it. The process of making a wig for each person takes two to three months and is a creative endeavor. It's off the wigmakers. Proudly told me not just anyone can do it. In the back of the workshop individual hairs are sewn threaded onto tot black nets giving the impression of thick lush hair. That's difficult to distinguish from someone's natural growth. I didn't realize all the women were wearing weeks. So expertly with their fringes threaded and halons disguised styling in fact wigs like this guilty of bank to good some ultra orthodox rabbis who set the rules of dress and behavior for their communities have banned them saying instead that women must shave and cover their heads with scarves knitted caps. This makes the wigmakers very angry. The Torah the Jewish Bible doesn't say women must be ugly. They told me only that they must cover their heads. These women are nothing. Like what I'd been told to expect from the Ultra Orthodox Women Mary. Young and families have an average of seven kids. Women also worked to support their families while many husband studied the Torah full-time the female employment rate for Ultra Orthodox women is the same as the mainstream Israeli population. While for men. It's only fifty percent early marriage. Nd heavy burdens of child rearing and work main ultra Orthodox. Women are often portrayed as victims of a repressive religious environment. That's run by men but these brought fallacious. Women were not victims. They were successful entrepreneurs soon to move out of the basement to a biggest studio. This place was filled with laughter and smiles as we took photos and had the week making process explained to us. Men Don't normally come in here especially not foreign men. They told us so visit was unusual and exciting. In fact I realized I'd had almost no interaction at all with Ultra Orthodox women before I see them everywhere but they don't normally speak to me. The experience was so different. My many interactions with Alterra Orthodox men some have been positive but the human tendency is to remember the violent negative ones much more strongly. I wish they were more opportunities like this to say the ultra-orthodox weld in such a funny friendly way. Many Israelis resent the for their extremism reliance on government allowances and refusal to serve in the army also for opposing sicklers. Riley's non observance of religious rules such as taking public transport on the Sabbath. We'll selling non kosher food. I understand all of that living as I do in Jerusalem. Israel's most religious city but now I understand that there's a lot of progress and a lot to like about the ultra Orthodox and to appreciated adjusted to look more closely at what women were wearing

Mary Tel Aviv Israel Jerusalem ABC United States Pramod Fouad Eric Harley Eric Torture Brac Bonaire Kate Middleton Producer Giant Bible College Dr Salzburg Sade Berg Riley
"pramod" Discussed on The Reboot Podcast

The Reboot Podcast

04:03 min | 2 years ago

"pramod" Discussed on The Reboot Podcast

"And it was it was, honestly, it was like a year and a half of that two years of that type of work, editing for him doing post production basically doing anything. He whatever he needed anything back. I've got a strong back open heart, right? Whatever you need, and I'll help you make your work better. And I have something that I can add or figure out or learn that entire time. I was I knew how to. Edit in final cut. But I didn't know how to do some of the editing that he required. But at the same time, I didn't really know many people that knew that type of editing. So I learned I just subscribed to Lynda dot com learned how to do after facts. Learn how to do the more advanced editing for post production in terms of how do we create really smooth seamless time, lapse as that allow me to control a whole bunch of Pramod, IRS, and factors and learning a different level of, of editing techniques that made the time lapses really, really smooth and seamless and kept editing for him kept making stuff creating value for him. And then it was it was a year and a half into that process where we finally agreed that we can go ahead and make a feature film out of it, it was never the plan at the beginning. The plan was committing. We knew he was doing something that was interesting. We knew this team was doing something that hadn't been done before, and we needed to document it and my role out in the field was as field assistant, and helping to install the cameras. But at the. Same time as videographer shooting all of it, and it was a split role of documenting what he and our team, we're doing as well as helping out and being part of the team. And it was it was well into that process before we said, you know what we've got an footage to actually make a feature film, and I hadn't done that before I've done a bunch of shorts, but never feature. I didn't know what I didn't know. And he's scared at that moment. I was so excited at the notion of doing a feature that there was no fear in it for me, there were unknowns. But there are people who had done that. And we, we brought on more producers, we brought on more people who had done it before to producers based here in boulder. Paulo prepayment, and Jerry Aronson both incredibly exceptionally talented, having done many, many films narrative, documentary Malla cross the board. I learned so so much from both of them over years of working on that film. They were mentors. They were family. They were friends. They were. It was beyond an advisor. They were teachers in a way that it wasn't that wasn't the explicit relationship that we had set up. They were on board with the project because of the vision of the project, but they taught me in countless ways, both professionally and personally both how to be a better person in filmmaker and how to make your films better. We brought on a great editor who had far more editing experience. We, we just kept building a team out that believed in this vision. It goes back to the same entrepreneurial mindset of higher better than you find people who have the skills that you don't have and that wasn't the explicit notion at the time it was like, oh, we need somebody to edit and I don't know how to do this that. Well, so who do we know that can edit really, really well, and our network kept introducing us to more, and more people, and we, we screened I was leading the edit for a while doing it all myself and spent two years, editing by myself before we brought more talented, editors on and during that process we kept screening it for other. Filmmakers to give us feedback. Here's where it's at right now. What do you think it was multiple years of artistic just like a knife to the chest of artistic criticism? Heavy open honest artistic criticism of wanting to lean into where the problems are. How can we make it better? And I know there are a lot of it's hard for filmmakers who put their vision into something in think this is the perfect thing. It's really hard to receive genuine criticism from people, but I'm not making the film for myself. I'm making a film for other people..

Pramod boulder IRS advisor Paulo editor Jerry Aronson two years
"pramod" Discussed on The Hustle & Flowchart Podcast

The Hustle & Flowchart Podcast

04:02 min | 2 years ago

"pramod" Discussed on The Hustle & Flowchart Podcast

"They don't want to see what appears to be the same title and snip it over and over again. And if you're competing with yourself by syndicating, the same article on multiple sites or on a third party side and on your site. That's just going to create a a lot of pain on necessarily use the evil twin strategy instead and make those two articles unique. I love that. That is so easily done if you create the content yourself, you probably have someone on your team, you know, kind of they don't even have to be super knowledgeable. You did all the research. Like, you said you leverage your time and your research time. Now, you just pretty much swap. It you make the reverse. Yeah. With the same content. You also they'll be technical issues that will cause duplicate content to appear like within your own site. You might have multiple versions of your homepage. Because of tracking parameters is crazy. Like if you do a search on Google for in Earl colon. Maybe some sort of session ID or user ID kinda parameter even the the Google analytics UTM, medium and UTM source and all that you put those sorts of of Pramod IRS into a Google query. And you'll find tons of search results that have those tracking Pramod errors in them even ones that aren't supposed to be there because it's on it's Google's own product. It's Google analytics and tracking crowd Pramod or is let's say UTM underscore medium. You'll find tons and tons of searches autzen Google with UTM underscore medium in the URL in and it's like, that's duplicate content. Because also on doubtedly the version without that tracking parameter is indexed in Google as well now, you're competing with yourself or you have a WWW version of your site and non WWW version of your site. That's not redirecting. One to the other, and you don't have a canonical tag in place a canonical in. So you're not saying, hey, Google. The WWW w version is the canonical the definitive source version used that one and collapse all the Pedrag all the link equity to that version. No, you have two competing versions or you have HTTPS HTTP and they're both competing with each other. So make sure you got your ducks in a row, and that goes back to this concept of pay for an SEO audit. Have somebody really good? Do it. You know, doesn't have to be me. I'm I'm not inexpensive. You can find cheaper. But you get what you pay for to voter. Yes. Obviously, you are you are there. You don't take on a lot of clients. But for folks wondering like what are some good places? They could find someone who is good at this SEO audit. Then not some person. That's doing old school stuff is probably going to give a long. Yeah. There's so many SEO providers out. Out there. I I'm not going to point to like spit on her two of them because then they'll get overwhelmed and qualities or maybe where to find these folks. Yeah. So there's the MAs recommended list. As a great source. I'm on that list. Of course, because not only am I good at this. But I'm co-authors with rand fisck and who's the founder of MAs. That had something to do with it. But actually, I think it's a it's a committee that does the selection process for who ends up on that list, and who doesn't, but that's a great starting point. If you talk with somebody and you want to ascertain, whether they know their stuff or they're selling snake oil, and you don't know it. Download my white paper, or it's kind of like a checklist sort of document it I call it the SEO BS detector, and it has a bunch of trick questions that don't sound like trick questions..

Google Pramod Pramod IRS Earl colon rand fisck founder
"pramod" Discussed on KTRH

KTRH

02:31 min | 2 years ago

"pramod" Discussed on KTRH

"Only getting worse not better. So I recommend if you can at least once a month get away from the cellphone, go somewhere, quiet. You can do it in a room. But you can do it more exotic. Like those of us here in California out in the desert. I prefer the mountains and go into what I call deep. Silence. Deep quiet and just the calm just to think your thoughts. Maybe with another person. Maybe not it's not a form of meditation. It's just kind of resetting some of those hormonal and cognitive variables back to our hunter gatherer ancestry back to back to normal. Downside. I've seen with Georgia's. When you come off of it, and you come back to the world of stimulation and honking horns and jabbering into the smartphones. But we are over stimulated and a variety of avenues, including visually. But also, and it's only getting worse. Ken, some noise. No benefit you. That is an interesting answer is. Yes, that's an interesting property. We originally called that still cast resonance are SR, and it goes like this in the classical systems of engineering there will be called linear systems where the output directly proportional to the input. In in. Those systems noise is always bad noise. Always harms the system, you know, in radio, we talk about the signal to noise ratio. But when we talk about real world systems are not linear denominator, you can think about it. This way. Linear systems, like a sheet of paper it smooth. And if you crop up that paper, that's a nominee or system, and if you room in real close to the couple it looks kind of flat and sure enough amount a half a century or more in the twentieth century. We did that kind of thing it's called a linear approximation. We only recently saw knowing your systems in Ben in general benefit from a little bit of noise. And that includes what you're listening to now on the radio. That includes a lot of other things, you can actually improve how you perceive images by adding a little bit of noise sometimes and in many other things, and it looks as if this is true of every known model of a neuron, whether it's in your foot or your brain that there is some optimal level of noise. Why because neurons working on off way, my brain cells nerve cells, and so they're not linear it looks. As if nature has figured this out as it would have over a billion years of evolution to to. Sooner Pramod IRS to an optimal level of noise. Again, not a lot of noise, but even.

Pramod IRS Ken California Georgia Ben billion years
"pramod" Discussed on O'Reilly Data Show

O'Reilly Data Show

02:57 min | 2 years ago

"pramod" Discussed on O'Reilly Data Show

"Challenges around kind of reproducibility and machine learning. And part of that arises because people often don't put air bars in their paper. They only ever run the experiment. Once you know, there's, there's not really this culture yet of careful methodical experimentation and machine learning the way there isn't another sciences and their equivalent of hacking and machine learning. There's a lot of jacking. Exactly, exactly and accept that. No one at nips knows what upheaval us. So, yeah. Every Nurul network architecture that we see publish. I wonder how many of them these people try. Right, exactly. Well, and you know, you you, it's, that's that's kind of easy to figure out you figure out how many keep us they have and you figure how long since their last archive post and kind of divide by time. And that's the number right? But so the was well for a lot of these experiments. You know, why is it the case that if I have a figure and I wanna put air bars, I have all this existing code that ever ended wise. They're still so much friction involved than in changing the random CNN basically running the same experiment over and over again. And so that's the other place where we're pirate is really kind of shine has been in kind of taking existing scientific code and kind of late in the process with as little effort as possible kind of scaling up the types of experiments of the classes experiments you're running so that you're at the very least doing less Pia hacking. Right. I certainly not solving that problem, but it does make it easier to kind of do a lot of these sorts of. Ferrets without worrying about it at the outset. Right? So much of the scientific computing code is you're just hacking around, desperately trying to get something working, right? You're not. You don't enter a lot of these projects with kind of grand Embiid about how at the end, it's going to be this big distributed system. So head of that that's sort of transparent work. There is really been has really been the goal, and we found success with heard success from other people kind of across the internet for for all of those sorts of use cases. So you describe is you use case us here. Basically data processing an ET l. hyper Amador up and kind of this reproducibility in Vars. But let's Iran in the haunt of what an industrial data scientists is using today. So for example, can Pyrennes supercharge mice, Ike it learn or my panda's? Yes, very much. So let me precise what we mean here. I mean that pirate is not going to make any of the jaw. Any of the training that you're doing with psychic learn faster. Right. It is. It is still running cycle learns code under the hood, whatever, whatever the second learn authors in his, they're like, LASSO solver. Whatever is what's going to get run, which I what lets you do is run more of those models simultaneously. Right? So it's really targeted right now kind of embarrassingly parallel tasks where you wanna do kind of the same thing on a bunch of different pieces of data or on a bunch of different Pramod or settings. Right..

Ike CNN LASSO Pramod grand Embiid Iran Amador
"pramod" Discussed on BizTalk Radio

BizTalk Radio

03:50 min | 3 years ago

"pramod" Discussed on BizTalk Radio

"There now I think bone has one there's one. Called. Full Cal but, any liquid calcium if you just mix it up. In a watering can put it on your tomato plants it'll take care of the blossom and Dorado immediately yeah in for instance like the fertile own product is a calcium spray and so some people don't always recognize that as a blossom Android cure in John mention any calcium will, help? With that so people because aside from. Blossom, Enwright you, use calcium as a like a set spray so you can even use that on. Things like peppers and other plants right you help them kind, of. Set, fruit well you could but I don't think it's the calcium. That sets a if you use plain water it would, also to. The same Don't tell the chemical companies And then let's see? What do we got here Caroline mentioned something funny and I've heard this before but we were talking about rabbits and I. Was talking, about shooting, him with assault gun it she said, you know one of her friends has assault. Gun so she's going to, get back to us but They spend, time on artificial turf and I've heard this, before They'll go out, turf in such as evidence So drop it they've been there I've heard of people having. Rabbits eat their. Artificial turf Scott Really dumb rabbit We know the plastic because they'll eat sprinkler, lines drip tubing but I always felt that was because they were trying to get. To the water how did they? Know Waterson there because, they are smart A lot better sense this story before but You know just there's a lot of. People with my claim, that they eat plastic sometimes just for. Spite, we used to have a pet? Rabbit remember I told you the story. Right, and it would eat the electrical cords. Now there's no water in the electrical court. That was the remember the demon rabbits story I don't think so because we had this pet rabbit and we had the mistaken. Idea? The. House that you live in now right okay we could train, the rabbit to be an indoor rabbit like go go. To, the, restroom has certain area. And, everything? Yeah. Supposedly you can do, that oh really yeah I've heard that difficult but, we try it started eating the electrical cords and I. Would you know you for its own health you don't want. It? To bite into an electrical fire hazard, right so when I would, see doing that I, would yell at it stop, there It. Would look at me, and then I would go back to eating the, core course you gotta teach English I well I would. Yell again I would say I would say stop and would. Look? At me and this the second time, I said it would start. To shake And then it would go back to eating. And. If, I said I want through in one, more time hey stop that and then it just. Took off started chasing me chase you around the house to bite you really yeah this. Is a great story. Video this, is really so I. Just a big old house rabbit right oh I don't know how big it was but it was cute and it chased you around Red eyes I I know I don't like those white. White. Pramod wow rebe. Be wow there was a song white, rabbit shown longtime We're going to, take a break we've got. One more segment and we've, got, a, lot, of, comments, on, Facebook, okay back to your questions your comments on Facebook. Live thank you so,.

Blossom Facebook Caroline assault Pramod John Dorado
"pramod" Discussed on WLOB

WLOB

01:52 min | 3 years ago

"pramod" Discussed on WLOB

"That's that's amazing what he's hubby is raising goats another thing i would like to know about him mr boy i hate to change the septic why don't you take i understand i understand he covers you to speak up certainly does speak of me and the play i'm going to begin and i'd love you to be my leading them on you do you play the hero oklahoma tax oklahoma takes how can i the few tony small way in which to be my too yeah you'll pepe well come over to canada drive we were hurt high right that'd be there mary and by the way at my house you gotta call me greatly my husband always dead your husband you're married so don't mention how i used to your lap it's still pretty romantic looking but why does your husband grace instead of marie but he french run or we'll keep your promise to your father my promise oh god many the same business as your father pramod headwear so he's married to a wine medicine oh i can't believe it it's hard for me to what is your name again.

oklahoma marie canada pramod
"pramod" Discussed on Help! I Suck At Dating with Dean Unglert

Help! I Suck At Dating with Dean Unglert

02:00 min | 3 years ago

"pramod" Discussed on Help! I Suck At Dating with Dean Unglert

"Yeah dust one on one that see how goes to that person and then we could work something else after that but we really so how much do you do take that like into our account clients verses to focus okay this on is what i that think is one best person for you versus right meeting so to three you people know they at are once coming to us you and know and who they need how to take can you our really expertise invest so if i have so many want your in to mind time focus that in might getting on be that to a know bit one out that person of person their and see what happens aides if it but pramod split between or i'm or multiple their acetates location people eating pramod so turkey earlier i because i think there's the will always big thing let somebody them with know around modern i think the corner the dating person a apps is narrow great always i've is talking met them personally with them in noone getting really i attention think hold that you you accountable from would be somebody good to fit date for just this so one reason person it's in missiri you kind it's in of have a tricky free range and world today out there however with all many that people out you want dating but and if online you work with dating a matchmaker such as yourself you're only going to be introducing one person to to another person at a time in not you know multiple people which i think helps you focus on their relationship hands.

pramod
"pramod" Discussed on Bang the Book

Bang the Book

01:42 min | 3 years ago

"pramod" Discussed on Bang the Book

"True talent level i think stratton guy with a pretty high talent level as i've talked about and some of my recent write ups so many come in against time garcia that was toback bartolo cologne high she has allowed a ton of hard contact this year i made a mistake i streamed him in a fantasy league against texas on saturday then after the fact i went and looked i actually i'd already picked him up went and looked at the savant data to do my write up or to do my my dfs piece for saturday and i noticed all the hard contact and i still left him out there he got knocked around the exit lost the metrics are important especially if you look at the guys who have the highest exit velocities on fly balls and line drives those are the guys that are going to get hit hard gr heart highvelocity grumball's are one thing highvelocity balls hitting the air that can go for extrabase hits and home runs or another thing those are problematic that's something i've tried to look at it a lot my daily fantasy pc or lately and so i've been trying to mention a little bit more in my write ups if you go to savant go to the pitch leaderboard you know about fifty batted ball events is what you wanna put your search pramod or there if you sort the highest exit losses by fly ball online drive those guys have all been pretty bad here throughout the year and if they haven't been bad they're going to be bad very very soon better since guy up on that list for example so that's a pretty good easy indicator of looking at pictures that are going to struggle as over baseball savant dot com pitching leaderboard sort depature exit velocities by fly ball online line drive average at law city you'll find some guys they're in line for some tough outings here coming up so many on the braves on saturday and sunday again as i mentioned in that series against the phillies and thereby team.

garcia pramod braves phillies stratton texas
"pramod" Discussed on MyTalk 107.1

MyTalk 107.1

02:30 min | 3 years ago

"pramod" Discussed on MyTalk 107.1

"Nine eighty one on sunday north and another call your landau oh jeez cold and rainy but really when the last room available was the three bedroom suite i thought now this is not planning is starting to get expensive he had it because you've what you thought you were going to get some swanky place for you just i totally forgot about the carribean laurie being out of commission did i mean that's why i even last september or whenever that happened i've yet hawaii because i was just like this is going to crunch die called my traveling this is crunch everything because i really an east coast people love to go to the caribbean love dakota puerto rico so right i mean i don't know of bahamas it didn't really get affected there but we don't know but i know that us and british virgin islands and puerto rico you know there there's the hotels i just never even thought of that you were soon oh really i'm just saying i'm going to be a better planner do do it leads to happiness it may having something to look for it makes people happy the planning but it doesn't make you happy so if you just would call a travel agent of we know quite a number of what i should have done with this and you give them your pramod irs they can just take care of you but instead you were gonna be old sneaky julia and get a five star place for fifty bucks the night's sleep on someone somewhere he's working every angle sleep at all three beds just to use them maybe she's gonna party yeah because that's big i'm not even going to tell you member montana i really might not planning is starting to be outrageously expensive and it's gonna make me cut back on other things which is really a bummer hughes exactly shoes furniture everything travel college education bill right now all right listen when we come back it's we can't get enough of what is actually worth watching netflix hulu or amazon prime i'm paul maguire grimes regular on the colleen and bradley show and i do a podcast called all things streaming stranger things handmaid's tale we cover them all and talk about how streaming has changed everything.

dakota puerto rico bahamas pramod julia montana hulu irs netflix amazon paul maguire colleen bradley
"pramod" Discussed on Stuff They Don't Want You To Know Audio

Stuff They Don't Want You To Know Audio

01:57 min | 3 years ago

"pramod" Discussed on Stuff They Don't Want You To Know Audio

"But it was still a maybe skeleton it was yes a tiny scale if this got it so it was originally this skeleton oughta was thought to be ancient but this testing that they were doing at this time found founder the remains were only a few decades old and they were entirely human but there was some weird stuff that went on with this genetic testing because ten percent nine to ten percent of the dna came back as not human and but there's an explanation for that a it's kind of weird but because you think 10 percent nineto10 percent of this was not human what in the heck could that be but that just has to do with of with the dna has been said earlier it degrades over time and also there's a certain percentage of error the you're going to get we're doing dna testing and there i guess it's also has to do things that i don't understand the entire genome when it's being tested their certain parts that sometimes will fall off in a test the segment yeah it it gets a little weird and again beyond my pay grade but the results of ninety one percent roughly being human are well within the expected pramod irs for contamination and degradation of dna and so entirely human fetus but so tiny to be so formed in the way it is now i to most observers oughta appears to be fetus people would say may be died prematurely made was born prematurely and died shortly thereafter however to mats point uh these same scientific analysis says that there were mature teeth present in the mouth and the balloons were welldeveloped with a leg bone showing growth plates that you would expect to see in a child of the age between sixty eight and that's also from that i felt science article and it goes on.

founder genetic testing pramod ten percent ninety one percent 10 percent
"pramod" Discussed on Software Engineering Daily

Software Engineering Daily

02:33 min | 3 years ago

"pramod" Discussed on Software Engineering Daily

"Octopus deploy a friendly deployment automation tool for deploying applications like dot net apps java apps and more ask any developer and they'll tell you that it's never fund pushing code at five p m on a friday and then crossing your fingers hoping for the best we've all been there we've all done that and that's where octopus deploy comes into the picture octopus deploy is a friendly deployment automation tool taking over where you're build or see i server ends use octopus to promote releases on pram or to the cloud octopus integrates with your existing build pipeline tmfs and v s t s bamboo team city and jenkins it integrates with aws asher and on pramod viramontes he can reliably and repeatedly deploy your dot net and java apps and more if you can package it octopus can deploy it's quick and easy to install and you can just go to octopus dot com to trial octopus free for forty five days that's octopus dot com oh see top u s dot com justin guys the ceo of atrium elti s justin welcome to software engineering daily thanks for having me in the 1990s the biggest financial cost to starting a company was often servers today the servers are pretty cheap but something it remains expensive is legal what are the common ways that a start a pass to interact with the law firm well basically every company hass to interact with lawyers for every major financial transaction it's kind of the operating system of business in america and so you know you always have to interact with it whenever you do anything really big so if you raise money you generally pay lawyers if you sell your company pay lawyers if you do a major commercial transaction you want someone to review it to pay lawyers so pretty common and as i would say it's a very you know various it's an inescapable thing kinda like death and taxes the legal industry itself has not been transformed by technology in the same way that some other industries have why is that.

developer aws ceo law firm operating system asher pramod viramontes america forty five days