Ai Foundations Of Deep Learning
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Deep Learning for Coders with fastai and PyTorch
Deep learning is often viewed as the exclusive domain of math PhDs and big tech companies. But as this hands-on guide demonstrates, programmers comfortable with Python can achieve impressive results in deep learning with little math background, small amounts of data, and minimal code. How? With fastai, the first library to provide a consistent interface to the most frequently used deep learning applications. Authors Jeremy Howard and Sylvain Gugger, the creators of fastai, show you how to train a model on a wide range of tasks using fastai and PyTorch. You’ll also dive progressively further into deep learning theory to gain a complete understanding of the algorithms behind the scenes. Train models in computer vision, natural language processing, tabular data, and collaborative filtering Learn the latest deep learning techniques that matter most in practice Improve accuracy, speed, and reliability by understanding how deep learning models work Discover how to turn your models into web applications Implement deep learning algorithms from scratch Consider the ethical implications of your work Gain insight from the foreword by PyTorch cofounder, Soumith Chintala
Deep Learning
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.
AI Foundations of Deep Learning
Discover the Future with "AI Foundations Of Deep Learning" Embark on a fascinating journey into the heart of Artificial Intelligence with this captivating book. "Artificial Intelligence: Deep Learning Made Easy" is more than just a guide; it's your window into the complex yet thrilling world of AI and deep learning. Key Features: Deep Learning Demystified: Unravel the mysteries of neural networks and their striking resemblance to human brain neurons. Real-World Applications: Explore how deep learning is revolutionizing fields like healthcare, autonomous vehicles, and natural language processing through engaging case studies. Insightful Narratives: Meet the thought leaders and pioneers whose contributions have shaped the landscape of AI technology. Ethical and Societal Impacts: Delve into the ethical considerations and societal impacts of deep learning, fostering a comprehensive understanding of AI's role in our world. Accessible to All: Whether you're a student, professional, or simply an AI enthusiast, this book breaks down complex concepts into an easy-to-understand format. Inspiring and Thought-Provoking: Concludes with a reflection on deep learning's key aspects, stirring your imagination and inviting you to join the ongoing AI evolution. Product Description: "Artificial Intelligence: Deep Learning Made Easy" takes you on an enlightening exploration of the silent revolution reshaping our existence. Each chapter peels back a layer of AI's most enigmatic tool, revealing how deep learning transforms data into sophisticated learning machines. Witness firsthand the transformative power of AI in various industries. Understand how it aids in medical diagnoses, powers self-driving cars, and enables computers to communicate fluently. This book not only informs but also inspires, showcasing the collaborative spirit at the intersection of technology and human ingenuity. As a tribute to the relentless curiosity driving AI from theory to reality, this book is an invitation to participate in the dialogue shaping our future's limitless possibilities. It's an essential read for anyone interested in the impact and future of AI and deep learning. Add this book to your collection and step into the world where technology meets human ingenuity!