Deep Learning

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Machine learning: When to use each method and technique

What exactly can you do with machine learning? We explain the various methods and techniques available to you

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What is CUDA? Parallel programming for GPUs

You can accelerate deep learning and other compute-intensive apps by taking advantage of CUDA and the parallel processing power of GPUs

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Powering AI: The explosion of new AI hardware accelerators

Specialized hardware platforms are the future of artificial intelligence, machine learning, and deep learning at every tier and for every task in the cloud-to-edge world in which we will live

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PyTorch review: A deep learning framework built for speed

PyTorch 1.0 shines for rapid prototyping with dynamic neural networks, auto-differentiation, deep Python integration, and strong support for GPUs

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Review: Amazon SageMaker scales deep learning

AWS machine learning service offers easy scalability for training and inference, includes a good set of algorithms, and supports any others you supply

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PyTorch tutorial: Get started with deep learning in Python

Learn how to create a simple neural network, and a more accurate convolutional neural network, with the PyTorch deep learning library

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For developers, the focus is deep learning, multiplatform, and coding skills

Angular, TensorFlow, React, and Electron all have seen large increases in developer activity on GitHub

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What is deep reinforcement learning: The next step in AI and deep learning

Reinforcement learning is well-suited for autonomous decision-making where supervised learning or unsupervised learning techniques alone can’t do the job

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TensorFlow review: The best deep learning library gets better

At version r1.5, Google's open source machine learning and neural network library is more capable, more mature, and easier to learn and use

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DIY GPU server: Build your own PC for deep learning

Building your own GPU server isn't hard, and it can easily beat the cost of training deep learning models in the cloud

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What AI can really do for your business (and what it can’t)

Artificial intelligence, machine learning, and deep learning are no silver bullets. A CIO explains what every business should know before investing in AI

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Now’s the time to do deep learning in the cloud

Machine learning is still all the rage, but the subset called deep learning may hold more value for enterprises

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Microsoft’s Project Brainwave accelerates deep learning in Azure

Using FPGAs deployed throughout the Azure cloud, Microsoft’s new system for accelerating deep learning poses a challenge to Google’s TPUs

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IBM speeds deep learning by using multiple servers

IBM's Distributed Deep Learning spreads model training across any number of hardware nodes—as long as they’re IBM nodes

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How deep learning will transform automation

Managing computer systems, finding bugs, and plugging security holes are all problems that AI will solve

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6 predictions for the future of deep learning

The potential of deep learning seems boundless, but developers are still figuring out how to put it to work. These near-term trends will help move it along

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