Jeremy Howard

Jeremy Howard

🇦🇺 Co-founder: @FastDotAI, Masks4All; Research: @CSIRO, @USFCA, @RealOzSAGE

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10+ Book Recommendations by Jeremy Howard

  • The sole survivor on a desperate, last-chance mission to save both humanity and the earth, Ryland Grace is hurtled into the depths of space when he must conquer an extinction-level threat to our species.

    @chrisalbon @aronchick @omojumiller Yup love this book!

  • Aimed at helping students of Chinese learn and remember Chinese characters, including the pronunciation of characters, fast and effectively, Learning Chinese Characters Volume 1 is a systematic study aid to this difficult language. Designed specifically to ease students into the daunting process of learning Chinese characters, Learning Chinese Characters Volume 1 incorporates the key principle of visual imagery. A book for serious learners of Chinese, it can be used alongside (or after, or even before) a course in the Chinese language. Concise, clear and appealing, this practical guide is well designed and includes an easy-to-use index.

    @NirantK @quantifiedself It's this: Tuttle Learning Chinese Characters: (HSK Levels 1 -3) A Revolutionary New Way to Learn and Remember the 800 Most Basic Chinese Characters https://t.co/RHgmGExTb5

  • Annotation Are you ready to experiment with food the way you tinker with technology? Whether or not you know how to cook, with 'Cooking for Geeks' you'll discover a new method to cooking through experiments that let you see the algorithms behind recipes.

    @migueldeicaza Cooking for geeks is such a cool book! It's been a long time since I looked at it; I really should check it out again.

  • Summary 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 Fran�ois Chollet, this book builds your understanding through intuitive explanations and practical examples. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the Technology Machine learning has made remarkable progress in recent years. We went from near-unusable speech and image recognition, to near-human accuracy. We went from machines that couldn't beat a serious Go player, to defeating a world champion. Behind this progress is deep learning--a combination of engineering advances, best practices, and theory that enables a wealth of previously impossible smart applications. About the Book 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 Fran�ois 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 About the Reader Readers need intermediate Python skills. No previous experience with Keras, TensorFlow, or machine learning is required. About the Author Fran�ois Chollet works on deep learning at Google in Mountain View, CA. He is the creator of the Keras deep-learning library, as well as a contributor to the TensorFlow machine-learning framework. He also does deep-learning research, with a focus on computer vision and the application of machine learning to formal reasoning. His papers have been published at major conferences in the field, including the Conference on Computer Vision and Pattern Recognition (CVPR), the Conference and Workshop on Neural Information Processing Systems (NIPS), the International Conference on Learning Representations (ICLR), and others. Table of Contents PART 1 - FUNDAMENTALS OF DEEP LEARNING What is deep learning? Before we begin: the mathematical building blocks of neural networks Getting started with neural networks Fundamentals of machine learning PART 2 - DEEP LEARNING IN PRACTICE Deep learning for computer vision Deep learning for text and sequences Advanced deep-learning best practices Generative deep learning Conclusions appendix A - Installing Keras and its dependencies on Ubuntu appendix B - Running Jupyter notebooks on an EC2 GPU instance

    ...and @fchollet and @rasbt books are also both really superb.

  • Python Machine Learning

    Sebastian Raschka

    Applied machine learning with a solid foundation in theory. Revised and expanded for TensorFlow 2, GANs, and reinforcement learning. Key Features Third edition of the bestselling, widely acclaimed Python machine learning book Clear and intuitive explanations take you deep into the theory and practice of Python machine learning Fully updated and expanded to cover TensorFlow 2, Generative Adversarial Network models, reinforcement learning, and best practices Book Description Python Machine Learning, Third Edition is a comprehensive guide to machine learning and deep learning with Python. It acts as both a step-by-step tutorial, and a reference you'll keep coming back to as you build your machine learning systems. Packed with clear explanations, visualizations, and working examples, the book covers all the essential machine learning techniques in depth. While some books teach you only to follow instructions, with this machine learning book, Raschka and Mirjalili teach the principles behind machine learning, allowing you to build models and applications for yourself. Updated for TensorFlow 2.0, this new third edition introduces readers to its new Keras API features, as well as the latest additions to scikit-learn. It's also expanded to cover cutting-edge reinforcement learning techniques based on deep learning, as well as an introduction to GANs. Finally, this book also explores a subfield of natural language processing (NLP) called sentiment analysis, helping you learn how to use machine learning algorithms to classify documents. This book is your companion to machine learning with Python, whether you're a Python developer new to machine learning or want to deepen your knowledge of the latest developments. What you will learn Master the frameworks, models, and techniques that enable machines to 'learn' from data Use scikit-learn for machine learning and TensorFlow for deep learning Apply machine learning to image classification, sentiment analysis, intelligent web applications, and more Build and train neural networks, GANs, and other models Discover best practices for evaluating and tuning models Predict continuous target outcomes using regression analysis Dig deeper into textual and social media data using sentiment analysis Who This Book Is For If you know some Python and you want to use machine learning and deep learning, pick up this book. Whether you want to start from scratch or extend your machine learning knowledge, this is an essential resource. Written for developers and data scientists who want to create practical machine learning and deep learning code, this book is ideal for anyone who wants to teach computers how to learn from data.

    ...and @fchollet and @rasbt books are also both really superb.

  • Through a series of recent breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. The updated edition of this best-selling book uses concrete examples, minimal theory, and two production-ready Python frameworks-Scikit-Learn and TensorFlow 2-to help you gain an intuitive understanding of the concepts and tools for building intelligent systems. Practitioners will learn a range of techniques that they can quickly put to use on the job. Part 1 employs Scikit-Learn to introduce fundamental machine learning tasks, such as simple linear regression. Part 2, which has been significantly updated, employs Keras and TensorFlow 2 to guide the reader through more advanced machine learning methods using deep neural networks. With exercises in each chapter to help you apply what you've learned, all you need is programming experience to get started. NEW FOR THE SECOND EDITION:Updated all code to TensorFlow 2 ; Introduced the high-level Keras API ; New and expanded coverage including TensorFlow's Data API, Eager Execution, Estimators API, deploying on Google Cloud ML, handling time series, embeddings and more With Early Release ebooks, you get books in their earliest form-the author's raw and unedited content as he or she writes-so you can take advantage of these technologies long before the official release of these titles. You'll also receive updates when significant changes are made, new chapters are available, and the final ebook bundle is released.

    So @aureliengeron's book is 1st in neural nets sales, & ours (with @GuggerSylvain) is 2nd. But also ours is 4th & his is 5th. So it's a tie?!? (In all seriousness, Aurélien's book is amazing and also he helped us a *lot* with our book!) https://t.co/9H2dKYsDOZ

  • Deep learning has the reputation as an exclusive domain for math PhDs. Not so. With this book, programmers comfortable with Python will learn how to get started with deep learning right away. Using PyTorch and the fastai deep learning library, you'll learn how to train a model to accomplish a wide range of tasks--including computer vision, natural language processing, tabular data, and generative networks. At the same time, you'll dig progressively into deep learning theory so that by the end of the book you'll have a complete understanding of the math behind the library's functions.

    We're really proud of what people are saying about the new book. https://t.co/guKT7y9VfM https://t.co/Rqge3lUsrp

  • Deep learning has the reputation as an exclusive domain for math PhDs. Not so. With this book, programmers comfortable with Python will learn how to get started with deep learning right away. Using PyTorch and the fastai deep learning library, you'll learn how to train a model to accomplish a wide range of tasks--including computer vision, natural language processing, tabular data, and generative networks. At the same time, you'll dig progressively into deep learning theory so that by the end of the book you'll have a complete understanding of the math behind the library's functions.

    fastai v2 is not API-compatible with fastai v1 (it’s a from-scratch rewrite). It’s much easier to use, more powerful, and better documented than v1, and there’s even a book (624 pages!) about it https://t.co/guKT7y9VfM

  • Deep learning has the reputation as an exclusive domain for math PhDs. Not so. With this book, programmers comfortable with Python will learn how to get started with deep learning right away. Using PyTorch and the fastai deep learning library, you'll learn how to train a model to accomplish a wide range of tasks--including computer vision, natural language processing, tabular data, and generative networks. At the same time, you'll dig progressively into deep learning theory so that by the end of the book you'll have a complete understanding of the math behind the library's functions.

    We've been blown away by the amazing comments from reviewers for *Deep Learning for Coders*. Google's Peter Norvig, an amazing researcher and writer, said it's "one of the best sources for a programmer to become proficient in deep learning." https://t.co/Uy3cZLkuwa https://t.co/2jp2JCTAm3

  • Learn the skills necessary to design, build, and deploy applications powered by machine learning. Through the course of this hands-on book, you'll build an example ML-driven application from initial idea to deployed product. Data scientists, software engineers, and product managers with little or no ML experience will learn the tools, best practices, and challenges involved in building a real-world ML application step-by-step. Author Emmanuel Ameisen, who worked as a data scientist at Zipcar and led Insight Data Science's AI program, demonstrates key ML concepts with code snippets, illustrations, and screenshots from the book's example application. The first part of this guide shows you how to plan and measure success for an ML application. Part II shows you how to build a working ML model, and Part III explains how to improve the model until it fulfills your original vision. Part IV covers deployment and monitoring strategies. This book will help you: Determine your product goal and set up a machine learning problem Build your first end-to-end pipeline quickly and acquire an initial dataset Train and evaluate your ML model and address performance bottlenecks Deploy and monitor models in a production environment

    ML models need to be integrated into data products and larger systems to be useful. This is a crucial and hard skill to master. I recommend this book by @mlpowered. It covers the entire end-to-end process of building and managing data products. https://t.co/pmSaLTk6NG

  • Network Effect

    Martha Wells

    @JanelleCShane Love those books so much!

  • Regression Modeling Strategies

    Frank E. Harrell Jr.

    The book will serve as a reference for data analysts and statistical methodologists.

    The other person I learned the most from was @f2harrell, specifically his book Regression Modeling Strategies, which has a great chapter on survival analysis (and covers many other under-appreciated gems). https://t.co/q0C2grd4rw

  • Think Stats

    Allen B. Downey

    Teaches the entire exploratory data analysis process using a single case study.

    I will say that my personal knowledge of the field is a little dated, since I haven't had much need for it in recent years. I did read some of @AllenDowney's "Think Stats" a couple of years ago and liked it a lot. It's available for free! https://t.co/S1uWcb7zPb

  • Deep learning has the reputation as an exclusive domain for math PhDs. Not so. With this book, programmers comfortable with Python will learn how to get started with deep learning right away. Using PyTorch and the fastai deep learning library, you'll learn how to train a model to accomplish a wide range of tasks--including computer vision, natural language processing, tabular data, and generative networks. At the same time, you'll dig progressively into deep learning theory so that by the end of the book you'll have a complete understanding of the math behind the library's functions.

    In our forthcoming book & course, you'll learn how to build a real deep learning web app from scratch, including downloading images using Bing's API. You'll also learn what can go wrong! (h/t @rajiinio) https://t.co/guKT7y9VfM https://t.co/p8Ur0mChXU

  • Deep learning has the reputation as an exclusive domain for math PhDs. Not so. With this book, programmers comfortable with Python will learn how to get started with deep learning right away. Using PyTorch and the fastai deep learning library, you'll learn how to train a model to accomplish a wide range of tasks--including computer vision, natural language processing, tabular data, and generative networks. At the same time, you'll dig progressively into deep learning theory so that by the end of the book you'll have a complete understanding of the math behind the library's functions.

    @marco_lainez_ @HeyerSascha It does! https://t.co/guKT7y9VfM

  • Regression Modeling Strategies

    Frank E. Harrell Jr.

    The book will serve as a reference for data analysts and statistical methodologists.

    @TheSandyCoder Yes it's really well covered in @f2harrell's book: https://t.co/q0C2grd4rw