Zachary Lipton: Like any good product, you have to pay attention to what people are doing. Dive into Deep Learning now provides a way to address those different implementations. So, if you don’t know that framework, you can’t work with the model. That’s because a researcher may propose a new model or algorithm and provide implementation in only one framework. So, we decided to add them to the book.Īston Zhang: Another factor is that for machine learning practitioners, it’s not enough to know just one framework. But then we started getting a lot of requests for PyTorch and TensorFlow implementations. Originally, we used MXNet because it’s a major interface and easy to learn. A big asset of the book is the fact we provide all the coding information. Mu Li: The book is designed to teach people different algorithms used in machine learning. What’s the significance of adding PyTorch and TensorFlow implementations to Dive into Deep Learning? The book also is incorporated into Amazon Machine Learning University courseware.Īmazon Science spoke to the authors previously about their book, and we recently reconnected with them to learn about the significance of the new frameworks they’ve added to their book. That gives the book-originally written for MXNet-even broader appeal within the open-source machine-learning community of students, developers, and scientists. Recently the authors added two programming frameworks to their book: PyTorch and TensorFlow. Special thanks to and for contributing code and our Google friends and More at. Dive into Deep Learning now supports The first 7 chapters are released today with more on their way.
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