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Distributed Services with Go, in beta; Programming Machine Learning in print

March 25, 2020


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Looking to level up your knowledge of Go into the next level of distributed systems? We've got you covered with Distributed Services with Go: Your Guide to Reliable, Scalable, and Maintainable Systems, now available in beta from pragprog.com/book/tjgo.

And if you're looking beyond the buzzwords, take a hands-on approach with Python and learn all about Programming Machine Learning: From Coding to Deep Learning, now content complete and available from pragprog.com/book/pplearn.

Come and get it!

Distributed Services with Go: Your Guide to Reliable, Scalable, and Maintainable Systems

Take your Go skills to the next level by learning how to design, develop, and deploy a distributed service. Start from the bare essentials of storage handling, then work your way through networking a client and server, and finally to distributing server instances, deployment, and testing. All this will make coding in your day job or side projects easier, faster, and more fun.

Lay out your applications and libraries to be modular and easy to maintain. Build networked, secure clients and servers with gRPC. Monitor your applications with metrics, logs, and traces to make them debuggable and reliable. Test and benchmark your applications to ensure they're correct and fast. Build your own distributed services with service discovery and consensus. Write CLIs to configure your applications. Deploy applications to the cloud with Kubernetes and manage them with your own Kubernetes Operator.

Dive into writing Go and join the hundreds of thousands who are using it to build software for the real world.

Now available in beta from pragprog.com/book/tjgo.

Programming Machine Learning: From Coding to Deep Learning

Peel away the obscurities of machine learning, starting from scratch and going all the way to deep learning. Machine learning can be intimidating, with its reliance on math and algorithms that most programmers don't encounter in their regular work. Take a hands-on approach, writing the Python code yourself without any libraries to obscure what's really going on. Iterate on your design, and add layers of complexity as you go.

Build an image recognition application from scratch with supervised learning. Predict the future with linear regression. Dive into gradient descent, a fundamental algorithm that drives most of machine learning. Create perceptrons to classify data. Build neural networks to tackle more complex and sophisticated data sets. Train and refine those networks with backpropagation and batching. Layer the neural networks, eliminate overfitting, and add convolution to transform your neural network into a true deep learning system.

Start from the beginning and code your way to machine learning mastery.

Now in print: The ebook is content complete and available from pragprog.com/book/pplearn. For the paperback edition, please support your local independent bookstore.

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