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Learning Deep Learning

Get started with deep learning with this new book from NVIDIA’s Magnus Ekman.

Learning Deep Learning is a complete guide to deep learning. Illuminating both the core concepts and the hands-on programming techniques needed to succeed, this book is ideal for developers, data scientists, analysts, and others—-including those with no prior machine learning or statistics experience. The book provides concise, well-annotated code examples using TensorFlow with Keras. And with corresponding PyTorch examples provided online, the book covers the two dominating Python libraries used for deep learning in industry and academia.

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Book: Learning Deep Learning
Learn More About Author Magnus Eklam
Video: introduction to Deep Learning Presented at the GTC 2021 Conference
Explore Code Examples from the Book

Recommended Reading List

Book: Deep Learning (Adaptive Computation and Machine Learning)

Deep Learning (Adaptive Computation and Machine Learning)

by Ian Goodfellow, Yoshua Bengio, & Aaron Courville

Book: Machine Learning Yearning
Book: Deep Learning with Python

Deep Learning with Python

by François Chollet

Book: Ray Tracing Gems II

Ray Tracing Gems II

by Adam Marrs, Peter Shirley, & Ingo Wald

Book: Programming Massively Parallel Processors: A Hands-on Approach
Book: Parallel Programming: Concepts and Practice

Parallel Programming: Concepts and Practice

by Bertil Schmidt Jorge Gonzalez-Dominguez, Christian Hundt, & Moritz Schlarb

Book: Professional CUDA C Programming

Professional CUDA C Programming

by John Cheng, Max Grossman & Ty McKercher

Book: Robotics, Vision and Control

NVIDIA Research

Groundbreaking technology begins right here with the world’s leading researchers. Explore various research activities in AI,  deep learning, robotics, high-performance computing, computer graphics, and more.

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Deep Learning programs for instructors

Educator Programs and
Teaching Kits

NVIDIA DLI offers downloadable course materials for university educators and free self-paced, online training to students through DLI Teaching Kits. Educators can also get certified to teach DLI workshops on campus through the University Ambassador Program.

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Technical Training

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Deep Learning Training

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A Deep Learning Training Session by Magnus Ekman

NVIDIA GTC 21 Deep Learning Training Session


Magnus conducts a short training session on deep learning. This presentation is a great preview to topics that Magnus covers in his book, Learning Deep Learning (LDL). The talk begins with a high-level description of the topics covered in LDL. It then moves on to a more detailed description of some of the content in the initial chapters, including programming examples. It concludes with an overview of a more complex application, an image captioning network that generates textual descriptions of images. This image captioning network is the focus of one of the final programming examples in LDL and gives a flavor of the type of skills the reader will master after reading the book.

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Code Samples from Author Magnus Ekman

Sample Codes from GitHub


Explore more code examples from the author. The Learning Deep Learning GitHub repository contains Python files for all code examples included in the book. The repository also contains well-documented Jupyter notebooks that let you step through each example interactively. Many of the code examples are based on the TensorFlow DL framework, which is the framework that is taught in the printed book. For readers who are interested in learning the PyTorch DL framework instead of (or in addition to) TensorFlow, the repository also contains PyTorch versions of these examples. The Jupyter notebooks contain detailed descriptions of the PyTorch constructs, at a similar detail level as TensorFlow is described in the book.

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Magnus Ekman

Magnus Ekman

Director of Architecture, NVIDIA


Magnus Ekman has a PhD in computer engineering, is a director of architecture at NVIDIA, and is the inventor of multiple patents. He has previously worked with processor design and R&D at Sun Microsystems and Samsung Research America. In his current role at NVIDIA, he leads an engineering team working on CPU performance and power efficiency for systems-on-chips targeting the autonomous vehicle market.

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