Deep Learning Demystified | Will Ramey Nvidia GTC

Will Ramey, NVIDIA

AI has evolved and improved methods for data analysis and complex computations, solving problems that seemed well beyond our reach only a few years ago. Learn the fundamentals of accelerated data analytics, high-level use cases, and problem-solving methods — and how deep learning is transforming every industry. We'll cover: demystifying AI, machine learning, and deep learning; understanding the key challenges organizations face in adopting this new approach and how to address them; and learning about the latest tools and technologies, along with training resources, that can help deliver breakthrough results.

Suggested Reading:

1. Deep Learning with Python First Edition Deep Learning with Python introduces the field of deep learning using the Python language and the powerful Keras library. Written by Keras creator and Google AI researcher François Chollet, this book builds your understanding through intuitive explanations and practical examples. You'll explore challenging concepts and practice with applications in computer vision, natural-language processing, and generative models. By the time you finish, you'll have the knowledge and hands-on skills to apply deep learning in your own projects.

What's Inside Deep learning from first principles Setting up your own deep-learning environment Image-classification models Deep learning for text and sequences Neural style transfer, text generation, and image generation

2. Deep Learning (Adaptive Computation and Machine Learning series) An introduction to a broad range of topics in deep learning, covering mathematical and conceptual background, deep learning techniques used in industry, and research perspectives. “Written by three experts in the field, Deep Learning is the only comprehensive book on the subject.” —Elon Musk, cochair of OpenAI; cofounder and CEO of Tesla and SpaceX

Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning.

The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models.

Deep Learning can be used by undergraduate or graduate students planning careers in either industry or research, and by software engineers who want to begin using deep learning in their products or platforms. A website offers supplementary material for both readers and instructors.

3. 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.

Book link: https://www.nvidia.com/en-us/training/books/

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Source: Nvidia On Demand ... https://www.youtube.com/watch?v=onBwI_BAksE