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Андрей Россия 46.146.38.* (12 августа 2018 | 23:11)
При переводе на карту возникли трудности, банк отвергал платеж. Обратился в поддержку, в течение 15 минут вопрос был решен, перевели на другую мою карту. Оперативная техподдержка, удобный сервис, спасибо за вашу работу!!!
Galina Россия 5.166.149.* (12 августа 2018 | 21:01)
Перевод был произведен супер быстро! А если добавите еще Сбербанк, чтобы комиссия поменьше, лучшего о не пожелаешь! Так держать!
Влад Россия 46.42.42.* (12 августа 2018 | 10:18)
Выводил эксмо рубли на тинькофф - процедура заняла порядка 5 минут, с 25тыс заплатил комиссию 7,5 рублей.
Результатом доволен на все 146%
Егор Нидерланды 192.42.116.* (9 августа 2018 | 18:40)
Очень быстрыы и оперативные, я сам накосячил при вводе но ребята быстро помогли 10 из 10
Андрей Россия 213.87.135.* (8 августа 2018 | 19:27)
Как всегда быстро и качественно, СПС.
Андрей Россия 176.195.75.* (8 августа 2018 | 11:21)
Обменивал с карты ВТБ на эфир, транзакацая заняла меньше минуты, оператор отвечал очень быстро, определенно годный обменник, будем пользоваться
Леха Россия 93.81.174.* (6 августа 2018 | 11:19)
Все супер как и всегда
A team of fast. The main training methods we used details below are: Four months ago, fast. We previously wrote about the approaches we used in this project. Before this project, training ImageNet on the public cloud generally took a few days to complete. We particularly liked the headline from The Verge: However, lots of people asked us — what would happen if you trained on multiple publicly available machines. We were encouraged to see that recently AWS had managed to train Imagenet in just 47 minutes , and in their conclusion said: We believe we can further lower the time-to-train across a distributed configuration by applying similar techniques. Some of the more interesting design decisions in the systems included:. Independently, DIU faced a similar set of challenges and developed a cluster framework, with analogous motivation and design choices, providing the ability to run many large scale training experiments in parallel. The set of tools developed by fast. Andrew Shaw merged parts of the fast. The first official release of nexus-scheduler, including the features merged from the fast. Or a very slow alternative widely used is to pick 5 crops top and bottom left and right, plus center and average the predictions. Which leaves the obvious question: A lot of people mistakenly believe that convolutional neural networks CNNs can only work with one fixed image size, and that that must be rectangular. The fastai library automatically converts fixed-size models to dynamically sized models. So Andrew went away and figured out how to make it work with fastai and Pytorch for predictions. You can see a comparison of the different approaches in this notebook , and compare the accuracy of them in this notebook. One of our main advances in DAWNBench was to introduce progressive image resizing for classification — using small images at the start of training, and gradually increasing size as training progresses. That way, when the model is very inaccurate early on, it can quickly see lots of images and make rapid progress, and later in training it can see larger images to learn about more fine-grained distinctions. In this new work, we additionally used larger batch sizes for some of the intermediate epochs — this allowed us to better utilize the GPU RAM and avoid network latency. That allowed us to trim another couple of epochs from our training time. Unfortunately, big companies using big compute tend to get far more than their fair share of publicity. This can lead to AI commentators coming to the conclusion that only big companies can compete in the most important AI research. Very few of the interesting ideas we use today were created thanks to people with the biggest computers. And today, anyone can access massive compute infrastructure on demand, and pay for just what they need. Making deep learning more accessible has a far higher impact than focusing on enabling the largest organizations - because then we can use the combined smarts of millions of people all over the world, rather than being limited to a small homogeneous group clustered in a couple of geographic centers. The article focuses on the fact that humans make very biased decisions which is true , yet ignores many important related issues, including:. The media often frames advances in AI through a lens of humans vs. This framework is both inaccurate as to how most algorithms are used, as well as a very limited way to think about AI. In all cases, algorithms have a human component, in terms of who gathers the data and what biases they have , which design decisions are made, how they are implemented, how results are used to make decisions, the understanding various stakeholders have of correct uses and limitations of the algorithm, and so on. Most people working on medical applications of AI are not trying to replace doctors; they are trying to create tools that will allow doctors to be more accurate and more efficient, improving quality of care. The article does not ask the question, how can we develop less biased ways to make decisions perhaps using some combination of humans and algorithms? Algorithms are often used at a larger scale, mass-producing identical biases, and assumed to be error-proof or objective. The wealthy, by contrast, often benefit from personal input. A white-shoe law firm or an exclusive prep school will lean far more on recommendations and face-to-face interviews than will a fast-food chain or a cash-strapped urban school district. Every store he applied to was using the same pyschometric evaluation software to screen candidates, and he was rejected from every store. This captures another danger of algorithms: Many people will put more trust in algorithmic decisions than they might in human decisions. While the researchers designing the algorithms may have a good grasp on probability and confidence intervals, often the general public using them will not. Even if people are given the power to override algorithmic decisions, it is crucial to understand if they will feel comfortable doing so in practice. This seems to be a particular trend amongst algorithmic decision making systems, perhaps since people mistakenly assume algorithms are objective, they believe there is no need for appeals. Also, as explained above, algorithmic decision making systems are often used as a cost-cutting device, and allowing appeals would be more expensive. She is never able to get an answer as to why she was fired. Stories like this would be somewhat less disturbing if there had been a relatively quick and simple way for her to appeal the decision, or even to know for sure what factors it was related to. The Verge investigated software used in over half of U. After its implemention in Arkansas, people many with severe disabilities drastically had their healthcare cut. For instance, Tammy Dobbs, a woman with cerebral palsy who needs an aid to help her to get out of bed, to go to the bathroom, to get food, and more, had her hours of help suddenly reduced by 20 hours a week. Eventually, a court case revealed that there were mistakes in the software implementation of the algorithm, negatively impacting people with diabetes or cerebral palsy. However, Dobbs and many other people reliant on these health care benefits live in fear that their benefits could again be cut suddenly and inexplicably. I should also probably dust under my bed. For a separate computer system used in Colorado to determine public benefits in the mids, it was discovered that more than incorrect rules had been coded into the system, resulting in problems like pregnant women being denied Medicaid. It is often hard for lawyers to even discover these flaws, since the inner-workings of the algorithms are typically protected as trade secrets. Many of the most chilling stories of algorithmic decision making would not be nearly as concerning if there had been an easy way to appeal and correct faulty decisions. When we think about AI, we need to think about complicated, real-world systems. The studies in the HBR article treat decision making as an isolated action, without taking into account that this decision-making happens within complicated real-world systems. A decision about whether someone is likely to commit another crime is not an isolated decision: We have a responsibility to understand the real-world systems with which our work will interact, and to not lose sight of the actual people who will be impacted. Later research found that COMPAS which uses inputs in a black-box algorithm was no more accurate than a simple linear equation on two variables. Kristian Lum, statistics PhD and lead data scientist at the Human Rights Digital Analysis Group, organized a workshop together with Elizabeth Bender, a staff attorney for the NY Legal Aid Society and former public defender, and Terrence Wilkerson, an innocent man who had been arrested and could not afford bail. Together, they shared first hand experience about the obstacles and inefficiencies that occur in the legal system, providing valuable context to the debate around COMPAS. Again, all this is for people that have not even faced a trial yet! This panel was an excellent way to illuminate the real-world systems and educate about the first-hand impact. I hope more statisticians and computer scientists will follow this example. As this example shows, algorithms can often exacerbate underlying societal problems. We have a responsibility to understand the systems and underlying problems our algorithms may interact with. Most critics of biased algorithms are opposed to unjust bias; they are not people who hate algorithms. I spent a great deal of time researching and writing about studies of human bias particularly as to how they pertain to the tech industry , long before I began writing about bias in machine learning. When I tweet or share about biased or unethical algorithms, I frequently encounter push-back that I must be anti-algorithms or opposed to tech. Just check out some of the speakers from the Fairness Accountability and Transparency Conference and watch their talks! One such example is Arvind Narayanan , a computer science professor at Princeton, winner of the Kaggle Social Network Challenge , teacher of a popular cryptocurrency course , and also speaks out against algorithmic bias. I hope that the popular discussion of biased algorithms can move beyond unnuanced rebuttals and more deeply engage with the issues involved. This is part 3 in a series. Part 1 is here and Part 2 is here. We hope AutoML will take an ability that a few PhDs have today and will make it possible in three to five years for hundreds of thousands of developers to design new neural nets for their particular needs. This raises a number of questions: Can large amounts of computational power really replace machine learning expertise? If true, we may all need to purchase Google products. Although the field of AutoML has been around for years including open-source AutoML libraries , workshops , research , and competitions , in May Google co-opted the term AutoML for its neural architecture search. I applied for access to it over 2 months ago, but I have not heard back from Google yet. Transfer learning is a powerful technique that lets people with smaller datasets or less computational power achieve state-of-the-art results, by taking advantage of pre-trained models that have been trained on similar, larger data sets. Transfer learning is a core technique that we use throughout our free Practical Deep Learning for Coders course— and that our students have been applying in production in everything from their own startups to Fortune companies. The underlying idea of transfer learning is that neural net architectures will generalize for similar types of problems: In contrast, the underlying idea of promoting neural architecture search for every problem is the opposite: When neural architecture search discovers a new architecture, you must learn weights for that architecture from scratch, while with transfer learning, you begin with existing weights from a pre-trained model. Of course, you can apply transfer learning to an architecture learned through neural architecture search which I think is a good idea! This requires only that a few researchers use neural architecture search and open-source the models that they find. It is not necessary for all machine learning practitioners to be using neural architecture search themselves on all problems when they can instead use transfer learning. Neural architecture search is good for finding new architectures! Since neural architecture search requires a larger training set, this would particularly be an issue for smaller data sets. Jeff Dean is an author on the ENAS paper, which proposed a technique that is x less computationally expensive, which seems inconsistent with his emphasis at the TF DevSummit one month later on using approaches that are x more computationally expensive. I think there are a few explanations:. There is a temptation to try to build products around interesting academic research, without assessing if they fulfill an actual need. This is also the story of many AI start-ups, such as MetaMind or Geometric Intelligence, that end up as acquihires without ever having produced a product. My advice for startup founders is to avoid productionizing your PhD thesis and to avoid hiring only academic researchers. Google excels at marketing. Google has a vested interest in convincing us that the key to effective use of deep learning is more computational power , because this is an area where they clearly beat the rest of us. While engineers and the media often drool over bare-metal power and anything bigger , history has shown that innovation is often birthed instead by constraint and creativity. Google works on the biggest data possible using the most expensive computers possible; how well can this really generalize to the problems that the rest of us face living in a constrained world of limited resources? Innovation comes from doing things differently, not from doing things bigger. The recent success of fast. Innovation come from doings things differently, not doing things bigger. To return to the issue that Jeff Dean raised in his TensorFlow DevSummit keynote about the global shortage of machine learning practitioners, a different approach is possible. We can remove the biggest obstacles to using deep learning in several ways by:. Research to make deep learning easier to use has a huge impact, making it faster and simpler to train better networks. Examples of exciting discoveries that have now become standard practice are:. None of the above discoveries involve bare-metal power; instead, all of them were creative ideas of ways to do things differently. For the vast majority of people I talk with, the barriers to entry for deep learning are far lower than they expected: In some countries, rules about banking and credit cards can make it difficult for students to use services like AWS, even when they have the money. Google Colab notebooks are a solution! Colab notebooks provide a Jupyter notebook environment that requires no setup to use, runs entirely in the cloud, and gives users access to a free GPU although long-running GPU use is not allowed. They can also be used to create documentation that contains working code samples running in an interactive environment. What is it that machine learning practitioners do? This is part 2 in a series. Check out part 1 here and part 3 here. Researchers from CMU and DeepMind recently released an interesting new paper, called Differentiable Architecture Search DARTS , offering an alternative approach to neural architecture search , a very hot area of machine learning right now. During his keynote starts around He gave computationally expensive neural architecture search as a primary example the only example he gave of why we need x computational power in order to make ML accessible to more people. What is neural architecture search? Is it the key to making machine learning available to non-machine learning experts? Neural architecture search is a part of a broader field called AutoML , which has also been receiving a lot of hype and which we will consider first. These methods exist for many types of algorithms, such as random forests, gradient boosting machines, neural networks, and more. Beginners often feel like they are just guessing as they test out different hyperparameters for a model, and automating the process could make this piece of the machine learning pipeline easier, as well as speeding things up even for experienced machine learning practitioners. There are a number of AutoML libraries, the oldest of which is AutoWEKA , which was first released in and automatically chooses a model and selects hyperparameters. AutoML provides a way to select models and optimize hyper-parameters. It can also be useful in getting a baseline to know what level of performance is possible for a problem. So does this mean that data scientists can be replaced? Not yet, as we need to keep the context of what else it is that machine learning practitioners do. For many machine learning projects, choosing a model is just one piece of the complex process of building machine learning products. I thought of over 30 different steps that can be involved in the process. I highlighted two of the most time-consuming aspects of machine learning in particular, deep learning as cleaning data and yes, this is an inseparable part of machine learning and training models. While AutoML can help with selecting a model and choosing hyperparameters, it is important to keep perspective on what other data expertise is still needed and on the difficult problems remain. I will suggest some alternate approaches to AutoML for making machine learning practitioners more effective in the final section. This is useful because it allows us to discover architectures far more complicated than what humans may think to try, and these architectures can be optimized for particular goals. Neural architecture search is often very computationally expensive. The literature of academic papers on neural architecture search is extensive, so I will highlight just a few recent papers here:. This work searches for an architectural building block on a small data set Cifar10 and then builds an architecture for a large data set ImageNet. This research was very computationally intensive with it taking GPU days the equivalent of almost 5 years for 1 GPU to learn the architecture the team at Google used GPUs for 4 days! After incorporating advances from fast. This research was done using a single GPU for just 16 hours. DARTS assumes the space of candidate architectures is continuous, not discrete, and this allows it to use gradient-based aproaches, which are vastly more efficient than the inefficient black-box search used by most neural architecture search algorithms. This is a huge gain in efficiency! Although more exploration is needed, this is a promising research direction. Given how Google frequently equates neural architecture search with huge computational expense, efficient ways to do architecture search have most likely been under-explored. In his TensorFlow DevSummit keynote starts around This was the only step of machine learning that Dean highlighted in his short talk, and I was surprised by his emphasis. However, choosing a model is just one piece of the complex process of building machine learning products. In most cases, architecture selection is nowhere near the hardest, most time-consuming, or most significant part of the problem. Organizations like Google working on architecture design and sharing the architectures they discover with the rest of us are providing an important and helpful service. However, the underlying architecture search method is only needed for that tiny fraction of researchers that are working on foundational neural architecture design. The rest of us can just use the architectures they find via transfer learning. The field of AutoML , including neural architecture search , has been largely focused on the question: However, automation ignores the important role of human input. The focus of augmented ML is on figuring out how a human and machine can best work together to take advantage of their different strengths. The learning rate is a hyperparameter that can determine how quickly your model trains, or even whether it successfully trains at all. The learning rate finder allows a human to find a good learning rate in a single step, by looking at a generated chart. For example, a key benefit of random forests over gradient boosting machines GBMs is that random forests are more robust, whereas GBMs tend to be fairly sensitive to minor changes in hyperparameters. As a result, random forests are widely used in industry. Researching ways to effectively remove hyperparameters through smarter defaults, or through new models can have a huge impact. For instance, in the fast. This post is part 1 of a series. There are frequent media headlines about both the scarcity of machine learning talent see here , here , and here and about the promises of companies claiming their products automate machine learning and eliminate the need for ML expertise altogether see here , here , and here. I follow these issues closely since my work at fast. Any solution to the shortage of machine learning expertise requires answering this question: This post is the first in a 3-part series. While many academic machine learning sources focus almost exclusively on predictive modeling, that is just one piece of what machine learning practitioners do in the wild. The processes of appropriately framing a business problem, collecting and cleaning the data, building the model, implementing the result, and then monitoring for changes are interconnected in many ways that often make it hard to silo off just a single piece without at least being aware of what the other pieces entail. As Jeremy Howard et al. A team from Google, D. The High-Interest Credit Card of Technical Debt about the code complexity and technical debt often created when using machine learning in practice. The authors identify a number of system-level interactions, risks, and anti-patterns, including:. In a previous post , I identified some failure modes in which machine learning projects are not effective in the workplace:. I framed these as organizational failures in my original post, but they can also be described as various participants being overly focused on just one slice of the complex system that makes up a full data product. These are failures of communication and goal alignment between different parts of the data product pipeline. As suggested above, building a machine learning product is a multi-faceted and complex task. Here are some of the things that machine learning practitioners may need to do during the process:. Certainly, not every machine learning practitioner needs to do all of the above steps, but components of this process will be a part of many machine learning applications. Even if you are working on just a subset of these steps, a familiarity with the rest of the process will help ensure that you are not overlooking considerations that would keep your project from being successful! For myself and many others I know, I would highlight two of the most time-consuming and frustrating aspects of machine learning in particular, deep learning as:. Dealing with data formatting, inconsistencies, and errors is often a messy and tedious process. People will sometimes describe machine learning as separate from data science, as though for machine learning, you can just begin with your nicely cleaned, formatted data set. However, in my experience, the process of cleaning a data set and training a model are usually interwoven: I frequently find issues in the model training that cause me to go back and change the pre-processing for the input data. The difficulty of getting models to train deters many beginners, who often wind up feeling discouraged. Even experts frequently complain of how frustrating and fickle the training process can be. One AI researcher at Stanford told me , I taught a course on deep learning and had all the students do their own projects. It was so hard. Rahimi asked the audience of AI researchers, and many raised their hands. Rahimi continued, This happens to me about every 3 months. The fact that even AI experts sometimes have trouble training new models implies that the process has yet to be automated in a way where it could be incorporated into a general-purpose product. Some of the biggest advances in deep learning will come through discovering more robust training methods. We have already seen this some with advances like dropout, super convergence , and transfer learning, all of which make training easier. Through the power of transfer learning to be discussed in Part 3 training can be a robust process when defined for a narrow enough problem domain; however, we still have a ways to go in making training more robust in general. Even if you are working on theoretical machine learning research, it is useful to understand the process that machine learning practitioners working on practical problems go through, as that might provide insights on what the most relevant or high-impact areas of research are. As Googler engineers D. Research solutions that provide a tiny accuracy benefit at the cost of massive increases in system complexity are rarely wise practice … Paying down technical debt is not always as exciting as proving a new theorem, but it is a critical part of consistently strong innovation. And developing holistic, elegant solutions for complex machine learning systems is deeply rewarding work. Now that we have an overview of some of the tasks that machine learning practitioners do as part of their work, we are ready to evaluate attempts to automate this work. Be sure to check out Part 2 here , and stay tuned for Part 3! Our courses all are free and have no ads: Deep Learning Part 1: Online textbook and Videos fast. Background Four months ago, fast. Experiment infrastructure Iterating quickly required solving challenges such as: How to easily run multiple experiments across multiple machines, without having a large pool of expensive instances running constantly? Some of the more interesting design decisions in the systems included: Not to use a configuration file, but instead configuring experiments using code leveraging a Python API. As a result, we were able to use loops, conditionals, etc to quickly design and run structured experiments, such as hyper-parameter searches Writing a Python API wrapper around tmux and ssh, and launching all setup and training tasks inside tmux sessions. This allowed us to later login to a machine and connect to the tmux session, to monitor its progress, fix problems, and so forth Keeping everything as simple as possible — avoiding container technologies like Docker, or distributed compute systems like Horovod. We did not use a complex cluster architecture with separate parameter servers, storage arrays, cluster management nodes, etc, but just a single instance type with regular EBS storage volumes. Using nexus-scheduler helped us iterate on distributed experiments, such as: Launching multiple machines for a single experiment, to allow distributed training. The machines for a distributed run are automatically put into a placement group , which results in faster network performance Providing monitoring through Tensorboard a system originally written for Tensorflow, but which now works with Pytorch and other libraries with event files and checkpoints stored on a region-wide file system. Various necessary resources for distributed training, like VPCs, security groups, and EFS are transparently created behind the scenes. Analyzing network utilization using Tensorboard A simple new training trick: Snippet of the Jupyter Notebook comparing different cropping approaches. Progressive resizing, dynamic batch sizes, and more One of our main advances in DAWNBench was to introduce progressive image resizing for classification — using small images at the start of training, and gradually increasing size as training progresses. Organizations with large image libraries, such as radiology centers, car insurance companies, real estate listing services, and e-commerce sites, can now create their own customized models. Whilst with transfer learning using so many images is often overkill, for highly specialized image types or fine-grained classification as is common in medical imaging using larger volumes of data may give even better results Smaller research labs can experiment with different architectures, loss functions, optimizers, and so forth, and test on Imagenet, which many reviewers expect to see in published papers By allowing the use of standard public cloud infrastructure, no up-front capital expense is required to get started on cutting-edge deep learning research. Next steps Unfortunately, big companies using big compute tend to get far more than their fair share of publicity. The article focuses on the fact that humans make very biased decisions which is true , yet ignores many important related issues, including: Algorithms are often used differently than human decision makers Algorithms are often used at a larger scale, mass-producing identical biases, and assumed to be error-proof or objective. Complicated, real-world systems When we think about AI, we need to think about complicated, real-world systems. What is transfer learning? Neural architecture search vs. How can we address the shortage of machine learning expertise? Examples from Matthew Zeiler and Rob Fergus of 4 features learned by image classifiers: What Neural Architecture Search is good for Neural architecture search is good for finding new architectures! I think there are a few explanations: We can remove the biggest obstacles to using deep learning in several ways by: Examples of exciting discoveries that have now become standard practice are: Dropout allows training on smaller datasets without over-fitting. Batch normalization allows for faster training. Rectified linear units help avoid gradient explosions. Newer research to improve ease of use includes: The learning rate finder makes the training process more robust. Super convergence speeds up training, requiring fewer computational resources. In my talk at the MIT Technology Review Conference, I addressed 6 Myths that lead people to incorrectly believe that using deep learning is harder than it is. Part 2 table of contents: How useful is AutoML? How useful is Neural Architecture Search? How else could we make machine learning practitioners more effective? Diagram from Zoph et. On the left is the full neural network of stacked cells, and on the right is the inside structure of a cell The literature of academic papers on neural architecture search is extensive, so I will highlight just a few recent papers here: This work used reinforcement learning to find new architectures for the computer vision problem Cifar10 and the NLP problem Penn Tree Bank, and achieved similar results to existing architectures. Please be sure to check out Part 3 of this post next week! What do machine learning practitioners actually do? What do machine learning practitioners do? Building Data Products is Complex Work While many academic machine learning sources focus almost exclusively on predictive modeling, that is just one piece of what machine learning practitioners do in the wild. Wikimedia Commons A team from Google, D. The authors identify a number of system-level interactions, risks, and anti-patterns, including: The data science team builds really cool stuff that never gets used. There is a backlog with data scientists producing models much faster than there is engineering support to put them in production. The data infrastructure engineers are separate from the data scientists. They need a data scientist to gather some data that supports this decision. The data scientist feels like the PM is ignoring data that contradicts the decision; the PM feels that the data scientist is ignoring other business logic. The data science team interviews a candidate with impressive math modeling and engineering skills. Once hired, the candidate is embedded in a vertical product team that needs simple business analytics. The data scientist is bored and not utilizing their skills. So, what do machine learning practitioners do? Here are some of the things that machine learning practitioners may need to do during the process: Two of the hardest parts of Machine Learning For myself and many others I know, I would highlight two of the most time-consuming and frustrating aspects of machine learning in particular, deep learning as: Training deep learning models is a notoriously brittle process right now. Is cleaning data really part of ML? Dealing with messy and inconsistent data is necessary Training Deep Learning Models is Brittle and Finicky for now The difficulty of getting models to train deters many beginners, who often wind up feeling discouraged. For Academic Researchers Even if you are working on theoretical machine learning research, it is useful to understand the process that machine learning practitioners working on practical problems go through, as that might provide insights on what the most relevant or high-impact areas of research are.