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…
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…
You know what ages like milk? Programming books. I always cringe when someone glances at my programming bookshelf. Some of those books are so dated, they make me appear out of touch by association. Sometimes, I feel compelled to justify myself. “Yes, that's the first edition of Thinking in Java…I keep it for nostalgic reasons, you know!” Yesterday’s software book is today’s fish and chip wrapper. However, there are exceptions. A few classics stay relevant for years, or even decades. This is a shortlist of software books that might be older than you, but are still very much worth reading.
In my consulting gigs, I come across plenty of clueless remarks. Here's a classic one: “We're falling behind schedule, so let's hire more coders.” Or a more recent gem: “We'll be ten times more productive if we generate code with AI.”
When I encounter such nonsense, I don't facepalm or cringe. Instead, I put on my poker face and drop a quote from The Mythical Man-Month.
In an industry where last year’s book is already outdated, Fred Brooks' collection of essays has been a guiding light for nearly half a century. His aphorisms have become legendary. “The bearing of a child takes nine months, no matter how many women are assigned.” “Adding manpower to a late software project makes it later.” “There is no silver bullet.” The list goes on and on.
John Carmack, one of the greatest programmers of our times, used to revisit this book every year or…
Few books on software project management have been as influential and timeless as The Mythical Man-Month. With a blend of software engineering facts and thought-provoking opinions, Fred Brooks offers insight for anyone managing complex projects. These essays draw from his experience as project manager for the IBM System/360 computer family and then for OS/360, its massive software system. Now, 20 years after the initial publication of his book, Brooks has revisited his original ideas and added new thoughts and advice, both for readers already familiar with his work and for readers discovering it for the first time.
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.
A beginner-friendly book with very clear writing. Vincent has several books on Django aimed at different levels of expertise. This one has a clear and instructional approach to building simple web applications. It is a little light on concepts and explanation of the requirements, probably intentionally, for which you can rely on other books.
Django for Beginners is a project-based introduction to Django, the popular Python-based web framework. Suitable for total beginners who have never built a website before as well as professional programmers looking for a fast-paced guide to modern web development and Django fundamentals.
In the book you’ll learn how to:
Build 5 websites from scratch, including a Blog and Newspaper website
Deploy online using security best practices
Customize the look and feel of your sites
Write tests and run them for all your code
Integrate user authentication, email, and custom user models
Add permissions and authorizations…
I have been a machine learning engineer applying my ML expertise in computational advertising, and search domain. I am an author of 8 machine learning books. My first book was ranked the #1 bestseller in its category on Amazon in 2017 and 2018 and was translated into many languages. I am also a ML education enthusiast and used to teach ML courses in Toronto, Canada.
This book is more advanced than the first book I recommended. It presents ML theoretical and practical aspects step-by-step from the bottom up. Each chapter elaborates at length on a core building block in the ML life cycle. For example, feature engineering, supervised learning, and model evaluation have their own separate chapters, with intuitive discussions of how they work. Most of the concept is taught through the simple yet powerful Python Module Scikit-Learn so it won’t overburden you with heavy programming. This book will be perfect for practitioners with some understanding of statistics and linear algebra.
Machine learning has become an integral part of many commercial applications and research projects, but this field is not exclusive to large companies with extensive research teams. If you use Python, even as a beginner, this book will teach you practical ways to build your own machine learning solutions. With all the data available today, machine learning applications are limited only by your imagination. You'll learn the steps necessary to create a successful machine-learning application with Python and the scikit-learn library. Authors Andreas Muller and Sarah Guido focus on the practical aspects of using machine learning algorithms, rather than the…
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.
I like this book not just because it’s a complete guide to the many ins and outs of data cleaning with Python, but also because David lays out the types of problems and the issues behind them. There are always trade-offs in data cleaning and this book lays out those trade-offs better than any other I’ve seen. This is one of the few books that as I go through it, I struggle to think of anything that could have been said better.
Think about your data intelligently and ask the right questions
Key Features
Master data cleaning techniques necessary to perform real-world data science and machine learning tasks
Spot common problems with dirty data and develop flexible solutions from first principles
Test and refine your newly acquired skills through detailed exercises at the end of each chapterBook Description
Data cleaning is the all-important first step to successful data science, data analysis, and machine learning. If you work with any kind of data, this book is your go-to resource, arming you with the insights and heuristics experienced data scientists had to learn the…
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…
My life has been about programming for as long as I can remember. Learning to code was a way to connect with my dad and express my creativity at a young age. Since I grew up with code, it became the way I understood the world; often I could look at a process or program and immediately see its source code in my mind. I developed a very strong sense of aesthetics searching for “perfect code,” which for me was code that was not only error-free but resistant to errors. My studies, research, and career is about moving myself and all programmers closer to that goal: Software that never fails.
Continuing down the engineering part of this mini-curriculum, we have a collection of interesting ideas, each written by a different author, all of them inspiring.
Some of the chapters in this book I have reread more times than I can count, because the ideas are so original and intriguing that my fingers start to tingle.
How do the experts solve difficult problems in software development? In this unique and insightful book, leading computer scientists offer case studies that reveal how they found unusual, carefully designed solutions to high-profile projects. You will be able to look over the shoulder of major coding and design experts to see problems through their eyes. This is not simply another design patterns book, or another software engineering treatise on the right and wrong way to do things. The authors think aloud as they work through their project's architecture, the tradeoffs made in its construction, and when it was important to…
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.
Another book with a detailed coverage of the Django web framework. This is a revised book written originally by Adrian Holovaty and Jacob Kaplan-Moss—the creators of Django themselves. Hence the initial chapters are an excellent in-depth description of how Django works. The remaining parts of the books go into intermediate and advanced topics.
Mastering Django is the latest version of Mastering Django: Core—the original, best-selling programmer’s reference for Django.
Mastering Django is not just a revision of the original book—it has been completely rewritten from the ground up to meet the needs of modern Django programmers.
The main goal of this book is to make you a Django expert. By reading this book, you’ll learn the skills needed to develop powerful websites quickly, with code that is clean and easy to maintain.
This book is also a programmer’s manual that provides complete coverage of modern Django version 3 and above.