Computer Literacy & Computer Science


Learn #MachineLearning Coding Basics in a weekend – a new approach to coding for #AI

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The first book is posted on data science central here, and the community group is here. Please join the community so you can also access the other'In a weekend' books It is also associated with a diverse range of people including Golf (Ben Hogan), Shaolin Monks, Benjamin Franklin etc. This means we don't need any installation (it's completely web-based) We will guide you through two end-to-end machine learning problems that can be taken over one weekend. We will introduce you to important machine learning concepts, such as machine learning workflow, defining the problem statement, pre-processing and understanding our data, building baseline and more sophisticated models, and evaluating models. We will also introduce to keep machine learning libraries in python and demonstrate code that can be used on your own problems.


Transfer Learning Made Easy: Coding a Powerful Technique - KDnuggets

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Fig: The model summary of the second network showing the fixed and trainable weights. The fixed weights are transferred directly from the first network. Now we train the second model and observe how it takes less overall time and still gets equal or higher performance. The accuracy of the second model is even higher than the first model, although this may not be the case all the time, and depends on the model architecture and dataset. Fig: Validation set accuracy over epochs while training the second network.


The Birth of Venus: Building a Deep Learning Computer From Scratch - Mihail Eric

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In this post we are going to learn about Venus, my deep learning computer, and how I built it. Along the way, I will explain at a high-level what each hardware component of a computer does and how I navigated the landscape of selecting parts for a functional build. I'll also describe how I installed relevant software for the machine and include some benchmarks showing the superior performance of a GPU system over a pure CPU system. WARNING: this is a pretty long post that functions as a complete tutorial for building a deep learning computer literally from scratch, no assumptions made. But…since it's long I highly encourage you to peruse and skip any sections depending on your interest. While there are numerous build descriptions out there showing how people constructed their own deep learning rigs, as I went about consulting some of them, I often felt there was some crucial component missing. As you start on your build journey, it's easy to get mired in the weeds of hardware terminology. Should I pick an M.2 SSD or will SATA suffice? Can I get away with HDD? How many PCIe x16 slots do I need? Should I pick DDR4-3000 or DDR4-2400 memory? All this lingo can be very overwhelming especially for newcomers to hardware. But before we start shamelessly name-dropping so that we sound smart, let's go back to the fundamentals.


What happens when we teach a computer how to learn?

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Technologist Jeremy Howard shares some surprising new developments in the fast-moving field of deep learning, a technique that can give computers the ability to learn Chinese, or to recognize objects in photos, or to help think through a medical diagnosis. Get caught up on a field that will change the way the computers around you behave ... sooner than you probably think. This talk was presented to a local audience at TEDxBrussels, an independent event.


Machine Learning / Computer Vision Engineer

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Are you an experienced Machine Learning / Computer Vision Engineer looking for a new opportunity? Vave Health is a startup based in the heart of Silicon Valley. Our mission is to provide the world with connected and personal tools that will help deliver better care, improve patient experience, and drive healthcare efficiency. Our next generation wireless connected device enables faster diagnosis and treatment at the point of care resulting in better patient outcomes. Vave Health is at the forefront of medical imaging and digital health.


What is Machine Learning on Code? - KDnuggets

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As IT organizations grow, so does the size of their codebases and the complexity of their ever-changing developer toolchain. Engineering leaders have very limited visibility into the state of their codebases, software development processes, and teams. By applying modern data science and machine learning techniques to software development, large enterprises have the opportunity to significantly improve their software delivery performance and engineering effectiveness. In the last few years, a number of large companies such as Google, Microsoft, Facebook and smaller companies such as Jetbrains and source{d} have been collaborating with academic researchers to lay the foundation for Machine Learning on Code. Machine Learning on Code (MLonCode) is a new interdisciplinary field of research related to Natural Language Processing, Programming Language Structure, and Social and History analysis such contributions graphs and commit time series.


Pssst.... build your own machine learning computer, it's cheaper and even faster than using GPUs on cloud

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If you've been thinking about building your own deep learning computer for a while but haven't quite got'round to it, here's another reminder. Not only is it cheaper to do so, but the subsequent build can also be faster at training neural networks than renting GPUs on cloud platforms. When you start trying small side projects like, say, building little autonomous drones or crafting a bot to spit out random snippets of poetry, you begin to realise how much compute power is really needed to get interesting results. So you can either fork out money to rent hardware via cloud services like AWS or Google Compute Platform or build your own server. Jeff Chen, an AI engineer and entrepreneur, drew up a handy shopping list for all the different parts needed to craft your own deep learning rig.


Facebook is betting the next big interface is conversation

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Back in 2015, chatbots were big. And one of the most hyped ones was Facebook's M, which the company meant to be a flexible, general-purpose bot that could do lots of different things such as purchase items, arrange gift deliveries, reserve restaurant tables, and plan travel. But the buzz was far bigger than the bot. When Facebook tested M with a group of 2,500 people in the Bay Area, the software failed to carry out most of the tasks it was asked to do. After the initial burst of enthusiasm for M and other chatbots ("bots are the new apps," Microsoft CEO Satya Nadella proclaimed), a wave of disappointment followed.


Machine learning in agriculture: Scientists are teaching computers to diagnose soybean stress

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Iowa State University scientists are working toward a future in which farmers can use unmanned aircraft to spot, and even predict, disease and stress in their crops. Their vision relies on machine learning, an automated process in which technology can help farmers respond to plant stress more efficiently. Arti Singh, an adjunct assistant professor of agronomy, is leading a multi-disciplinary research team that recently received a three-year, $499,845 grant from the U.S Department of Agriculture's National Institute of Food and Agriculture to develop machine learning technology that could automate the ability of farmers to diagnose a range of major stresses in soybeans. The technology under development would make use of cameras attached to unmanned aerial vehicles, or UAVs, to gather birds-eye images of soybean fields. A computer application would automatically analyze the images and alert the farmer of trouble spots.


Machine learning in agriculture: scientists are teaching computers to diagnose soybean stress

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AMES, Iowa - Iowa State University scientists are working toward a future in which farmers can use unmanned aircraft to spot, and even predict, disease and stress in their crops. Their vision relies on machine learning, an automated process in which technology can help farmers respond to plant stress more efficiently. Arti Singh, an adjunct assistant professor of agronomy, is leading a multi-disciplinary research team that recently received a three-year, $499,845 grant from the U.S Department of Agriculture's National Institute of Food and Agriculture to develop machine learning technology that could automate the ability of farmers to diagnose a range of major stresses in soybeans. The technology under development would make use of cameras attached to unmanned aerial vehicles, or UAVs, to gather birds-eye images of soybean fields. A computer application would automatically analyze the images and alert the farmer of trouble spots.