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…
Since I was a kid, I’ve been passionate about technology and had a clear vocation to work with computers. I’ve been a developer for more than 20 years now, spending half of them mainly in the Python environment, and I’ve always been interested in improving my skills. While it’s true that software development is a field that changes constantly and technology evolves at great speed, there are some elements that remain relatively unchanged and can be used to compound knowledge and ability. In particular, the elements that are closer to the human element, teamwork, coordination, etc. are quite stable over time.
While this is a Python-specific book, it’s a fantastic description of all the possibilities for testing with a powerful module like Pytest offers. Testing is one of the basic experiences for a programmer, as it should be included as a core part of the development process. Understanding all the different options available like mark groups of tests, parametric tests, building your own extensions, or test coverage, to name only a few details, expands the understanding of how to design better tests and run them more efficiently.
Do less work when testing your Python code, but be just as expressive, just as elegant, and just as readable. The pytest testing framework helps you write tests quickly and keep them readable and maintainable - with no boilerplate code. Using a robust yet simple fixture model, it's just as easy to write small tests with pytest as it is to scale up to complex functional testing for applications, packages, and libraries. This book shows you how. For Python-based projects, pytest is the undeniable choice to test your code if you're looking for a full-featured, API-independent, flexible, and extensible testing…
I’ve been teaching and writing Python code (and managing others while they write Python code) for over 20 years. After all that time Python is still my tool of choice, and many times Python is the key part of how I explore and think about problems. My experience as a teacher also has prompted me to dig in and look for the simplest way of understanding and explaining the elegant way that Python features fit together.
Dane covers the more advanced topics a programmer needs to be successful as a professional. In particular, he has good discussions of the basics of software design – things like separation of concerns, encapsulation, testing, and performance, as well as some of the issues involved with creating and maintaining large-scale systems. This is the book that I wish I’d had early in my coding career.
Summary Professional developers know the many benefits of writing application code that’s clean, well-organized, and easy to maintain. By learning and following established patterns and best practices, you can take your code and your career to a new level. With Practices of the Python Pro, you’ll learn to design professional-level, clean, easily maintainable software at scale using the incredibly popular programming language, Python. You’ll find easy-to-grok examples that use pseudocode and Python to introduce software development best practices, along with dozens of instantly useful techniques that will help you code like a pro.
Being able to understand and change reality through our knowledge and skill is literal magic. We’re building systems with so many exciting and unexpected properties that can be exploited and repurposed for both good and evil. I want to keep some of that magic and help people engineer – build great systems that make people’s lives better. I’ve been securing (and breaking) systems, from operating rooms to spaceships, from banks to self-driving cars for over 25 years. The biggest lesson I’ve learned is that if security is not infused from the start, we’re forced to rely on what ought to be our last lines of defense. This list helps you infuse security into your systems.
Loren’s been contributing to security for over 40 years, and this book captures his hard-won wisdom in a way that’s both humble and accessible. It scales from principles and design approaches to in-depth explanations of exactly how things go wrong and how to avoid those problems. (Also, I was honored to write the foreword.)
Designing Secure Software consolidates Loren Kohnfelder's more than twenty years of experience into a concise, elegant guide to improving the security of technology products. Written for a wide range of software professionals, it emphasizes building security into software design early and involving the entire team in the process. The book begins with a discussion of core concepts. The second part, perhaps this book's most important contribution, covers the process of designing and reviewing a software design with security considerations in mind. The final section details the most common coding flaws that create vulnerabilities, making copious use of code snippets written…
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…
I’ve been dabbling in Python for the last 22 years. I am a regular speaker at Pycon India ever since its inception. Most of my talks are related to Django. I host arunrocks.com where I write tutorials, and articles and publish screencasts on several Django and Python topics. My initial screencast titled "Building a blog in 30 mins with Django" is one of the most popular screencasts for beginners in Django. I’m a developer member of the Django Software Foundation.
Building scalable and performant web applications is both an art and a science. This book focused on such techniques and hence goes beyond what most books on Django try to cover. Anyone running a Django site under heavy load will definitely learn a few tips from this book. However, it is light on explanations and expects you to figure out many things from reading the examples.
Since I was a kid, I’ve been passionate about technology and had a clear vocation to work with computers. I’ve been a developer for more than 20 years now, spending half of them mainly in the Python environment, and I’ve always been interested in improving my skills. While it’s true that software development is a field that changes constantly and technology evolves at great speed, there are some elements that remain relatively unchanged and can be used to compound knowledge and ability. In particular, the elements that are closer to the human element, teamwork, coordination, etc. are quite stable over time.
A very personal recommendation, as it is about Vim, a very particular text editor that can be difficult to learn at first, but this is the best technical book that I’ve ever read. I use Vim as my main editor and this book makes an astonishing job in clearly explaining why it works the way it works. This book gets you into the proper mindset to use Vim, making it click internally and from there on, to feel way more natural and powerful. Even if you don’t want to use Vim as your main editor, it’s ubiquitous and it’s available by default on a huge amount of computers, making being comfortable with its usage a really powerful tool in a lot of situations.
Vim is a fast and efficient text editor that will make you a faster and more efficient developer. It's available on almost every OS--if you master the techniques in this book, you'll never need another text editor. Practical Vim shows you 120 vim recipes so you can quickly learn the editor's core functionality and tackle your trickiest editing and writing tasks. Vim, like its classic ancestor vi, is a serious tool for programmers, web developers, and sysadmins. No other text editor comes close to Vim for speed and efficiency; it runs on almost every system imaginable and supports most coding…
Some programmers learn through online articles, videos, and blog posts. Not me. I need a throughline—a consistent, expert distillation of the material to take me from where I am to where I want to be. I am not good at patching together information from disparate sources. I need a great book. I have a PhD in computer science education, and I want to know what helps people learn. More importantly, I want to know how we can use such discoveries to write more effective books. The books I appreciate most are those that demonstrate not only mastery of the subject matter but also mastery of teaching.
I used this book for several years starting in 2013 when the first edition came out. It absolutely holds up today. Learning the Python language (the syntax) is one thing. Learning how to design programs using this syntax is another. We need both but, unfortunately, many books forgo the latter for the former. Not this book! I like the Problem Solving and Worked Example sections: they help learners apply a disciplined, step-by-step strategy to programming projects. There are multiple, varied contexts here as well, which helps capture a broader base of learners. Bonus feature: the Computing & Society boxes.
Python for Everyone, 3rd Edition is an introduction to programming designed to serve a wide range of student interests and abilities, focused on the essentials, and on effective learning. It is suitable for a first course in programming for computer scientists, engineers, and students in other disciplines. This text requires no prior programming experience and only a modest amount of high school algebra. Objects are used where appropriate in early chapters and students start designing and implementing their own classes in Chapter 9. New to this edition are examples and exercises that focus on various aspects of data science.
As a child of the microcomputer revolution in the late 1970s, I’ve always been fascinated by the concept of a general-purpose machine that I could control. The deep learning revolution of 2010 or so, followed most recently by the advent of large language models like ChatGPT, has completely altered the landscape. It is now difficult to interpret the behavior of these systems in a way that doesn’t argue for intelligence of some kind. I’m passionate about AI because, decades after the initial heady claims made in the 1950s, AI has reached a point where the lofty promise is genuinely beginning to be kept. And we’re just getting started.
Goodfellow’s Deep Learning is a must in the field because it was the first. Prince’s new book is an essential follow-up to be up-to-date with the latest model types, including diffusion models (think Stable Diffusion or DALL-E), transformers (the heart of large language models), graph networks (reasoning over relationships), and reinforcement learning.
The math level is similar to what you’ll find in Goodfellow’s book.
An authoritative, accessible, and up-to-date treatment of deep learning that strikes a pragmatic middle ground between theory and practice.
Deep learning is a fast-moving field with sweeping relevance in today’s increasingly digital world. Understanding Deep Learning provides an authoritative, accessible, and up-to-date treatment of the subject, covering all the key topics along with recent advances and cutting-edge concepts. Many deep learning texts are crowded with technical details that obscure fundamentals, but Simon Prince ruthlessly curates only the most important ideas to provide a high density of critical information in an intuitive and digestible form. From machine learning basics to advanced…
I’ve been dabbling in Python for the last 22 years. I am a regular speaker at Pycon India ever since its inception. Most of my talks are related to Django. I host arunrocks.com where I write tutorials, and articles and publish screencasts on several Django and Python topics. My initial screencast titled "Building a blog in 30 mins with Django" is one of the most popular screencasts for beginners in Django. I’m a developer member of the Django Software Foundation.
Two Scoops is the bible of Django development. It has the most detailed coverage of the Django web framework including best practices and tips. The book has a delightful ice cream-based theme including delightful illustrations. There are several editions of this book so make sure you have the latest one. Overall a valuable reference. However, this book might be daunting for an absolute beginner.
Two Scoops of Django 1.11 Will Help You Build Django Projects.
In this book we introduce you to the various tips, tricks, patterns, code snippets, and techniques that we've picked up over the years. We have put thousands of hours into the fourth edition of the book, writing and revising its material to include significant improvements and new material based on feedback from previous editions.
Table of Contents
Chapter 1: Coding Style Chapter 2: The Optimal Django Environment Setup Chapter 3: How To Lay Out Django Projects Chapter 4: Fundamentals of Django App Design Chapter 5: Settings and Requirements Files…
