# Learn, Innovate, Lead Continue your dive into deep learning. Explore learning resources on AI, accelerated computing, and accelerated data science. ### 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. [Buy Now](https://www.informit.com/store/learning-deep-learning-theory-and-practice-of-neural-9780137470358?utm_source=referral&utm_medium=nvidia&utm_campaign=nvidia-dl) #### [Meet the Author: Magnus Ekman](#) [Director of Architecture, NVIDIA](#) #### [Watch Magnus’ Introduction to Deep Learning at NVIDIA GTC21](#) #### [Explore Code Examples](#) ## Recommended Reading List #### [Deep Learning (Adaptive Computation and Machine Learning)](https://www.amazon.com/Deep-Learning-Adaptive-Computation-Machine/dp/0262035618/ref=sr_1_3?dchild=1&keywords=deep+learning+goodfellow&qid=1623318135&s=books&sr=1-3) by Ian Goodfellow, Yoshua Bengio, & Aaron Courville #### [Machine Learning Yearning](https://www.goodreads.com/en/book/show/30741739-machine-learning-yearning) by Andrew Ng #### [Deep Learning with Python](https://www.amazon.com/Deep-Learning-Python-Francois-Chollet/dp/1617294438/ref=sr_1_1?dchild=1&keywords=deep+learning+with+Python&qid=1625863321&sr=8-1) by François Chollet #### [Ray Tracing Gems II](https://www.apress.com/us/book/9781484271841) by Adam Marrs, Peter Shirley, & Ingo Wald #### [Programming Massively Parallel Processors: A Hands-on Approach](https://www.amazon.com/Programming-Massively-Parallel-Processors-Hands/dp/0128119861/ref=pd_sbs_1/138-0540704-6053415?pd_rd_w=cPav3&pf_rd_p=f8e24c42-8be0-4374-84aa-bb08fd897453&pf_rd_r=9K62V8JKTTB6PQVEWP97&pd_rd_r=8f3645ac-acac-4760-a527-127a395d1a80&pd_rd_wg=2oVc5&pd_rd_i=0128119861&psc=1) by David B. Kirk and Wen-mei W. Hwu #### [Parallel Programming: Concepts and Practice](https://www.amazon.com/Parallel-Programming-Concepts-Bertil-Schmidt-ebook/dp/B07799JW8W) by Bertil Schmidt Jorge Gonzalez-Dominguez, Christian Hundt, & Moritz Schlarb #### [Professional CUDA C Programming](https://www.amazon.com/Professional-CUDA-Programming-John-Cheng/dp/1118739329) by John Cheng, Max Grossman & Ty McKercher #### [Robotics, Vision and Control](https://www.springer.com/de/book/9783319544120) by Peter Corke ## 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. [Learn More](https://www.nvidia.com/en-us/research.md) ## 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. [Learn More](https://www.nvidia.com/en-us/training/educator-programs.md) ## Technical Training ### [Self-Paced, Online Courses](https://www.nvidia.com/en-us/training/online.md) ### [Live, Virtual, Instructor-Led Workshops](https://www.nvidia.com/en-us/training/instructor-led-workshops.md) ## Questions? ### [Inquire about NVIDIA Deep Learning Institute services.](#) ### [For technical questions, check out the NVIDIA Developer Forums.](https://forums.developer.nvidia.com/) ## Contact Us for Questions on Deep Learning Training Welcome back. Not you? Log Out Welcome back. Not you? Clear form ### 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. [****Watch Now****](https://www.nvidia.com/en-us/on-demand/session/gtcspring21-t3200/) ### 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. [****Learn More****](https://github.com/NVDLI/LDL) ### 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.