Scaling Kubernetes to 2,500 Nodes

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We've been running Kubernetes for deep learning research for over two years. While our largest-scale workloads manage bare cloud VMs directly, Kubernetes provides a fast iteration cycle, reasonable scalability, and a lack of boilerplate which makes it ideal for most of our experiments. We now operate several Kubernetes clusters (some in the cloud and some on physical hardware), the largest of which we've pushed to over 2,500 nodes. This cluster runs in Azure on a combination of D15v2 and NC24 VMs. On the path to this scale, many system components caused breakages, including etcd, the Kube masters, Docker image pulls, network, KubeDNS, and even our machines' ARP caches.


PyTorch, a year in....

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Today marks 1 year since PyTorch was released publicly. It's been a wild ride -- our quest to build a flexible deep learning research platform. Over the last year, we've seen an amazing community of people using, contributing to and evangelizing PyTorch -- thank you for the love. Looking back, we wanted to summarize PyTorch over the past year: the progress, the news and highlights from the community. We've been blessed with a strong organic community of researchers and engineers who fell in love with PyTorch.


Random Forest – StepUp Analytics

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However, both are equally important concepts of data science. Having said that, there are several dissimilarities between the two concepts also. In case of regression, as we all know the predicted outcome is a numeric variable and that too continuous. For a classification task, the predicted outcome is not numeric at all and represents categorical classes or factors i.e. the outcome variable in such a task has to be assuming limited number of values which may be binary in nature (dichotomous) or multinomial (having more than 2 classes). We in our analysis are motivated to work only on the'classification' scheme of tasks from a predictive analysis domain keeping our focus not on regression trees but only on classification trees, as the name suggests'Classification and Regression Trees'.


Overview of Artificial Neural Networks and its Applications

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The term'Neural' is derived from the human (animal) nervous system's basic functional unit'neuron' or nerve cells which are present in the brain and other parts of the human (animal) body. Dendrite -- It receives signals from other neurons. Soma (cell body) -- It sums all the incoming signals to generate input. Axon -- When the sum reaches a threshold value, neuron fires and the signal travels down the axon to the other neurons. The amount of signal transmitted depend upon the strength (synaptic weights) of the connections.


Microsoft's AI is a robo Van Gogh that could change how designers work

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Microsoft has developed an AI to draw entirely original images based on nothing more than text. You type it, a computer draws it, and we're one step closer to a world where using software like Photoshop and Illustrator is a hands-off experience. Researchers created a text-to-image bot that spits out pretty amazing images when fed a series of descriptive words like "this bird is red with white and has a very short beak." This was accomplished through the creation of neural network called an Attentional Generative Adversarial Network (AttnGAN) that creates the image pixel-by-pixel. Like any other artist or designer, it does both broad strokes and fine details in layers.


Machine Learning with Small Data

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Machine learning and big data are broadly believed to be synonymous. The story goes that large amounts of training data are needed for algorithms to discern signal from noise. As a result, machine learning techniques have been most used by web companies with troves of user data. For Google, Facebook, Microsoft, Amazon, Apple (or the "Fearsome Five" as Farhad Manjoo of the New York Times has dubbed them), obtaining large amounts of user data is no issue. Data usage policies have become increasingly broad, allowing these companies to make use of everything from our keystrokes to our personal locations as we use company products.


Transfer Learning: A Beginner's Guide

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This blog post will introduce the concept of'transfer learning' and how it is used in machine learning applications. Transfer learning is not a machine learning model or technique; it is rather a'design methodology' within machine learning. Another type of'design methodology' is, for example, active learning. A next blog post will explain how you can use active learning in conjunction with transfer learning to optimally leverage existing (and new) data. In a broad sense, machine learning applications that leverage external information to improve the performance or generalisation capabilities use transfer learning.


Daneel : Bringing data intelligence into the crypto jungle

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The quick and massive cryptocurrency trading's boom has led to a boom of information. Maybe you have noticed that this information is often oriented and unreliable? Daneel provides unchallenged news curation and market emotions analyses through an A.I. assistant, which understands natural language. It is powered by the reference of the market in the fields of natural language and emotion analysis: IBM Watson.


Net Tech Lead Machine Learning

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Other projects include Artificial Intelligence and Machine Learning. Reporting to the Head of Technology, as a Tech Lead you will be responsible for initiating and driving quality and efficiency improvements in their existing agile process.


Wise up, deep learning may never create a general purpose AI

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During the taping of an MSNBC town hall on jobs with Google CEO Sundar Pichai and YouTube CEO Susan Wojcicki in San Francisco–it'll air next Friday-hosts Kara Swisher and Ari Melber introduced a segment on artificial intelligence with a clip of HAL, 2001: A Space Odyssey's scary-smart computer, and a topical question: In 2018, should people … Continue reading "Google CEO: AI is a bigger deal than fire or electricity" ...