Read it now on the O’Reilly learning platform with a 10-day free trial. O’Reilly members get unlimited access to books, live events, courses curated by job role, and more from O’Reilly and nearly 200 top publishers. Learn how easy it is to apply sophisticated statistical and machine learning methods to real-world problems when you build on top of the Google Cloud Platform (GCP). This hands-on guid
Read it now on the O’Reilly learning platform with a 10-day free trial. O’Reilly members get unlimited access to books, live events, courses curated by job role, and more from O’Reilly and nearly 200 top publishers. How do you detangle a monolithic system and migrate it to a microservice architecture? How do you do it while maintaining business-as-usual? As a companion to Sam Newman’s extremely po
Read it now on the O’Reilly learning platform with a 10-day free trial. O’Reilly members get unlimited access to books, live events, courses curated by job role, and more from O’Reilly and nearly 200 top publishers. With so many interacting components, the number of things that can go wrong in a distributed system is enormous. You’ll never be able to prevent all possible failure modes, but you can
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The two positions are not interchangeable—and misperceptions of their roles can hurt teams and compromise productivity. It’s important to understand the differences between a data engineer and a data scientist. Misunderstanding or not knowing these differences are making teams fail or underperform with big data. A key misunderstanding is the strengths and weaknesses of each position. I think some
How CapsNets can overcome some shortcomings of CNNs, including requiring less training data, preserving image details, and handling ambiguity. Capsule networks (CapsNets) are a hot new neural net architecture that may well have a profound impact on deep learning, in particular for computer vision. Wait, isn’t computer vision pretty much solved already? Haven’t we all seen fabulous examples of conv
Join the O'Reilly online learning platform. Get a free trial today and find answers on the fly, or master something new and useful. Learn more Know what anomaly detection is and the common techniques for solving it Be able to set up your MXNet environment See the difference between different types of networks, along with their strengths and weaknesses Load and preprocess the data for such a task B
Read it now on the O’Reilly learning platform with a 10-day free trial. O’Reilly members get unlimited access to books, live events, courses curated by job role, and more from O’Reilly and nearly 200 top publishers. Can machine learning techniques solve our computer security problems and finally put an end to the cat-and-mouse game between attackers and defenders? Or is this hope merely hype? Now
How new developments in algorithms, machine learning, analytics, infrastructure, data ethics, and culture will shape the data world. Here’s what we expect to see—or see more of—in the data world. 1. New tools will make graphs and time series easier, leading to new use cases Graphs and time series have been a crucial part of the explosion in big data. We will see the emergence of a new generation o
Read it now on the O’Reilly learning platform with a 10-day free trial. O’Reilly members get unlimited access to books, live events, courses curated by job role, and more from O’Reilly and nearly 200 top publishers. If you need to build a scalable, fault tolerant system with requirements for high availability, discover why the Erlang/OTP platform stands out for the breadth, depth, and consistency
Data Show, I spoke with Soumith Chintala, AI research engineer at Facebook. Among his many research projects, Chintala was part of the team behind DCGAN (Deep Convolutional Generative Adversarial Networks), a widely cited paper that introduced a set of neural network architectures for unsupervised learning. Our conversation centered around PyTorch, the successor to the popular Torch scientific com
The quest to evolve neural networks through evolutionary algorithms. Neuroevolution is making a comeback. Prominent artificial intelligence labs and researchers are experimenting with it, a string of new successes have bolstered enthusiasm, and new opportunities for impact in deep learning are emerging. Maybe you haven’t heard of neuroevolution in the midst of all the excitement over deep learning
Read it now on the O’Reilly learning platform with a 10-day free trial. O’Reilly members get unlimited access to books, live events, courses curated by job role, and more from O’Reilly and nearly 200 top publishers. Feature engineering is a crucial step in the machine-learning pipeline, yet this topic is rarely examined on its own. With this practical book, you’ll learn techniques for extracting a
Practical Generative Adversarial Networks for Beginners You can download and modify the code from this generative adversarial networks tutorial on GitHub here. According to Yann LeCun, “adversarial training is the coolest thing since sliced bread.” Sliced bread certainly never created this much excitement within the deep learning community. Generative adversarial networks—or GANs, for short—have d
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