Here are 70 books that The Hundred-Page Machine Learning Book fans have personally recommended if you like
The Hundred-Page Machine Learning Book.
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I have been building real-time, production machine learning models for over 20 years. My book, and my book recommendations, are informed by that experience. I have a lot of empathy for people who are new to machine learning because I’ve taught courses on the topic. I founded the Advanced Solutions Lab at Google where we helped data scientists working for Google Cloud customers (who already knew ML) become ML engineers capable of building reliable ML models. The first two are the books I’d recommend today to newcomers and the last three to folks attending the ASL.
There are three types of machine learning books — books written for people who want to become machine learning engineers, books written for people who want to become machine learning researchers, and books written for business executives. Reading a book written for researchers or executives can be a frustrating experience if you are a software engineer, social scientist, or mechanical engineer who wants to learn machine learning and get an ML job in the industry.
If you are a coder who wants to become an ML engineer, you have got to learn machine learning concepts, but you want to learn them in a practical way. You need a book that leads with intuition and shows you implementations with code. It has to do this without getting sidetracked into ML theory, getting mired in statistical concepts, or being so superficial that you don’t understand why the code works.…
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. This practical book shows you how.
By using concrete examples, minimal theory, and two production-ready Python frameworks-Scikit-Learn and TensorFlow-author Aurelien Geron helps you gain an intuitive understanding of the concepts and tools for building intelligent systems. You'll learn a range of techniques, starting with simple linear regression and progressing to deep neural networks. With exercises in each chapter to help…
I’ve been working in machine learning for about a decade. I’ve always been more interested in applied than theoretical problems and while blogs and MOOCs (Massive Online Open Courses) are a great way to learn, for certain deep topics only a book would do. I also teach at University of Oxford, University of Birmingham, and various FTSE100 companies. My machine learning has exposed me to many fascinating problems—from leading my own ML-focused startup through Y Combinator—to working at various companies as a consultant. I think there is currently no great curriculum for the practitioners really wanting to apply deep learning in practical cases, so I have given it my best shot.
This is a fantastic book to get you started. It is written by the author of a leading deep learning framework Keras, which makes even Tensorflow very easy to use. Chollet is a true leader of the deep learning craft and the Manning team always does an excellent job of forcing authors to make the subject matter accessible. Highly recommended!
"The first edition of Deep Learning with Python is one of the best books on the subject. The second edition made it even better." - Todd Cook
The bestseller revised! Deep Learning with Python, Second Edition is a comprehensive introduction to the field of deep learning using Python and the powerful Keras library. Written by Google AI researcher Francois Chollet, the creator of Keras, this revised edition has been updated with new chapters, new tools, and cutting-edge techniques drawn from the latest research. You'll build your understanding through practical examples and intuitive explanations that make the complexities of deep learning…
I have been building real-time, production machine learning models for over 20 years. My book, and my book recommendations, are informed by that experience. I have a lot of empathy for people who are new to machine learning because I’ve taught courses on the topic. I founded the Advanced Solutions Lab at Google where we helped data scientists working for Google Cloud customers (who already knew ML) become ML engineers capable of building reliable ML models. The first two are the books I’d recommend today to newcomers and the last three to folks attending the ASL.
This recommendation is a bit of a cheat — I’m not recommending this exact book, but one of the books in the series that this book is part of.
Once you have the first two books under your belt, you’ll know how to solve ML problems. But you will keep reinventing the wheel. What you need next is a book on common “ML tricks” — best practices and common techniques when doing ML in production.
The problem is that these tricks are specific to the type of data that you will be processing. If you are going to be processing images or time series, read the corresponding books in the same series instead.
Many books and courses tackle natural language processing (NLP) problems with toy use cases and well-defined datasets. But if you want to build, iterate, and scale NLP systems in a business setting and tailor them for particular industry verticals, this is your guide. Software engineers and data scientists will learn how to navigate the maze of options available at each step of the journey.
Through the course of the book, authors Sowmya Vajjala, Bodhisattwa Majumder, Anuj Gupta, and Harshit Surana will guide you through the process of building real-world NLP solutions embedded in larger product setups. You'll learn how to…
I’ve been working in machine learning for about a decade. I’ve always been more interested in applied than theoretical problems and while blogs and MOOCs (Massive Online Open Courses) are a great way to learn, for certain deep topics only a book would do. I also teach at University of Oxford, University of Birmingham, and various FTSE100 companies. My machine learning has exposed me to many fascinating problems—from leading my own ML-focused startup through Y Combinator—to working at various companies as a consultant. I think there is currently no great curriculum for the practitioners really wanting to apply deep learning in practical cases, so I have given it my best shot.
Jeremy Howard is the lead author and has always been a world-class educator. This book is based on his fast.ai course, which has managed to splice all rigor, simplicity, and cutting edge techniques into one course. It also uses its custom fast.ai framework built on PyTorch, which is the dominant language for researchers. This book is very practically oriented and gets you off the ground very quickly with your own projects!
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…
For as long as I can remember I’ve been obsessed with figuring out how things work. What started with me pulling apart redundant household tech as a child (thanks to my very supportive parents) has become a lifelong passion in making and restoring one of the most incredible machines invented – the watch. Our millennia-old obsession with making things tells us so much about who we are and the world we like in. I love all of these books as, in varied ways, they inspire curiosity and connect us with our innately human instinct to understand the world around us.
As adults, we get to a point in our lives where we generally know what we’re good at, and when we’re good at something, it becomes challenging to try new things we know we’ll initially, probably, be very bad at it. It pushes us outside our comfort zone.
Ploszajski is a brilliant materials scientist who bravely heads outside her field on a journey to explore the hands-on world of the materials she knows so well in the lab. It’s an incredibly inspiring read for anyone holding back from trying new skills. It’s hard to come away from this book without having set your heart on taking up a new craft!
From atomic structures to theories about magnetic forces, scientific progress has given us a good grasp on the properties of many different materials. However, most scientists cannot measure the temperature of steel just by looking at it, or sculpt stone into all kinds of shapes, or know how it feels to blow up a balloon of glass. Handmade is the story of materials through making and doing. Author and material scientist Anna Ploszajski journeys into the domain of makers and craftspeople to comprehend how the most popular materials really work.
Anna has the fresh perspective of someone at the forefront…
I started my career in tourism but soon discovered my passion for urban heritage. Working as a site manager for a world heritage site, I gathered extensive insights on various levels of heritage management and urban governance from many colleagues around the world. Today there is no single project or meeting that does not address the challenges of climate change. Obtaining my Ph.D. late in life, in Heritage-Based Urban Development, I quicklybecame convinced that the traditional ideas of what cultural heritage is do not reflect the situation today and hinder giving cultural heritage a role in climate change prevention and adaption, beyond the narrative that it has to be preserved.
This book gives a great overview of traditional architecture around the world and how it was designed for specific climates.
With great images and descriptions, this book is able to broaden your horizon and help you to discover fabric, design, and uses that can also serve to develop new ideas and solutions that can potentially be transferred into your own context.
Rather than going very deep into examples, it provides more of an overview that can trigger creativity and imagination in the early phases of projects.
A compact edition of this landmark publication, which celebrates humanity's ability to create buildings that for millennia have responded ingeniously to cultural and environmental conditions.
There has never been a more important time to understand how to make the best use of local natural resources and create buildings that do not rely on stripping our planet or transporting materials across the globe. First published in 2017, this major book gathers together the world's leading experts on vernacular architecture to examine how local buildings have stood the test of time and offer lessons for the future.
I am a faculty in Materials Science and along with my colleague researcher Dr. Arun DI, I have published many research articles in the field of Smart materials, specifically shape-memory materials. We have developed Polyurethane based space-grade shape-memory nanocomposite and have proven electro-active shape memory effect with the highest recovery efficiency reported so far. We are continuing our research in the field of smart and intelligent materials which we believe will benefit the advanced application fields such as space exploration.
The basics of composite materials, reinforcements-Matrix materials, their interfaces, categories of composite materials, micro/macro mechanics, mechanical aspects involved in composite engineering, etc., are covered in detail in this book. The concepts involved in composite systems are well explained for any enthusiast in the field.
Focusing on the relationship between structure and properties, this is a well-balanced treatment of the mechanics and the materials science of composites, while not neglecting the importance of processing. This updated second edition contains new chapters on fatigue and creep of composites, and describes in detail how the various reinforcements, the materials in which they are embedded, and of the interfaces between them, control the properties of the composite materials at both the micro- and macro-levels. Extensive use is made of micrographs and line drawings, and examples of practical applications in various fields are given throughout the book, together with…
I am a faculty in Materials Science and along with my colleague researcher Dr. Arun DI, I have published many research articles in the field of Smart materials, specifically shape-memory materials. We have developed Polyurethane based space-grade shape-memory nanocomposite and have proven electro-active shape memory effect with the highest recovery efficiency reported so far. We are continuing our research in the field of smart and intelligent materials which we believe will benefit the advanced application fields such as space exploration.
This book covers almost all the aspects of smart materials, not limiting to fundamentals, fabrications, applications, etc. It gives prime importance to the science of intelligence with proper illustrations, explanations, and the associated problems with solutions. Undergraduates, post-graduate students as well as researchers will find it very useful to understand the basics of intelligent material physiology and chemistry behind them.
Smart materials are of significant interest and this is the first textbook to provide a comprehensive graduate level view of topics that relate to this field. Fundamentals of Smart Materials consists of a workbook and solutions manual covering the basics of different functional material systems aimed at advanced undergraduate and postgraduate students.
Topics include piezoelectric materials, magnetostrictive materials, shape memory alloys, mechanochromic materials, thermochromic materials, chemomechanical polymers and self-healing materials. Each chapter provides an introduction to the material, its applications and uses with example problems, fabrication and manufacturing techniques, conclusions, homework problems and a bibliography.
I love knowing about things. Science is both a knowledge base and a way to discover new knowledge. I’ve been looking through microscopes and telescopes (that my dad built) from my earliest toddling. Though I have never been to university I have picked the brains of my scientific siblings (one of whom is a biology professor) and I read widely. Gathering crumbs from many sources gives a wider knowledge base than one university child afford. Scientists begin with speculation. I love inventing systems and worlds where we break one or a few of our known laws of nature or physics. Marrying science with fantasy births marvelous offspring!
The power of unrequited love that seeks no alternative. A knight loves his queen. He gives his life to protect her and is banished from her presence, yet he cannot resist the magic that arranges the world to draw them together.
Evil tries to convince us that it gives the greatest benefit. Sometimes that is temporarily true. I love stories where characters resist and suffer so that they receive the higher benefit of good. I am a musician and the magic of our knight rings true in my soul.
Highly philosophical, spiritual, fantastical, and deeply scientific, this book satisfies on every level.
“Thermodynamics, signal theory, Bayesian inference, inversion, quantum tunneling, and materials science are all elements of the internally consistent system of dynamics.” Lee Hunt
"This spectacular standalone fantasy bursts with epic battles and avid romance." - Booklife Reviews Editor's Pick
“A beautifully crafted setting with complex character dynamics and layers of political intrigue… A showstopper.Hunt’s ambitious standalone latest has everything—a well-imagined fantasy world, great characters, incredible tension, and fierce love. The real genius here is the mixture of extraordinarily deep worldbuilding with relevant and complex themes, which include identity, intolerance, love, passion, friendship, integrity, honor and more.” - Prairies Book Review
“An intriguing storyline, scenarios grounded in the real world, and a breathless pace make Hunt’s latest standalone fantasy a must-read.” - BookView ReviewsRecommended…
I was trained as a mathematician but have always been motivated by problem-solving challenges. Statistics and analytics combine mathematical models with statistical thinking. My career has always focused on this combination and, as a statistician, you can apply it in a wide range of domains. The advent of big data and machine learning algorithms has opened up new opportunities for applied statisticians. This perspective complements computer science views on how to address data science. The Real Work of Data Science, covers 18 areas (18 chapters) that need to be pushed forward in order to turning data into information, better decisions, and stronger organizations
The text covers classic statistical inference, early computer-age methods, and twenty-century topics. This puts a unique perspective on current analytic technologies labeled machine learning, artificial intelligence, and statical learning. The examples used provide a powerful description of the methods covered and the compare and contrast sections highlight the evolution of analytics. This book by Efron and Hastie is a natural follow-up source for readers interested in more details.
The twenty-first century has seen a breathtaking expansion of statistical methodology, both in scope and influence. 'Data science' and 'machine learning' have become familiar terms in the news, as statistical methods are brought to bear upon the enormous data sets of modern science and commerce. How did we get here? And where are we going? How does it all fit together? Now in paperback and fortified with exercises, this book delivers a concentrated course in modern statistical thinking. Beginning with classical inferential theories - Bayesian, frequentist, Fisherian - individual chapters take up a series of influential topics: survival analysis, logistic…
