Here are 76 books that High Performance Django fans have personally recommended if you like
High Performance Django.
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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’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’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.
This is a very well-written book that covers some less covered areas like how to write the most minimal Django application or integrating with Tornado server. The book is short and quite engaging. This is not exactly a book for an impatient beginner as it takes time to build the concepts. Also, the book might be a bit dated since it was last updated in 2014.
How can you take advantage of the Django framework to integrate complex client-side interactions and real-time features into your web applications? Through a series of rapid application development projects, this hands-on book shows experienced Django developers how to include REST APIs, WebSockets, and client-side MVC frameworks such as Backbone.js into new or existing projects. Learn how to make the most of Django's decoupled design by choosing the components you need to build the lightweight applications you want. Once you finish this book, you'll know how to build single-page applications that respond to interactions in real time. If you're familiar with…
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.
I am motivated by working on products that many people use. I've been a part of companies that deliver products impacting millions of people. To achieve it, I am working in the Big Data ecosystem and striving to simplify it by contributing to Dremio's Data LakeHouse solution. I worked on projects using Spark, HDFS, Cassandra, and Kafka technologies. I have been working in the software engineering industry for ten years now, and I've tried to share my experience and lessons learned in the Software Mistakes and Tradeoffs book, hoping that it will allow current and the next generation of engineers to create better software, leading to more happy users.
The Hands-on Machine Learning book presents an end-to-end approach to many problems that can be solved with machine learning.
Every concept and topic is backed up with a running code that you can experiment with and adapt to your real-world problems.
Thanks to this book, you will be able to understand the state of the art of today's machine learning and feel comfortable using the most up-to-date ML methods.
Through a recent series of 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 best-selling book uses concrete examples, minimal theory, and production-ready Python frameworks--scikit-learn, Keras, and TensorFlow--to help you gain an intuitive understanding of the concepts and tools for building intelligent systems.
With this updated third edition, author Aurelien Geron explores a range of techniques, starting with simple linear regression and progressing to deep neural networks. Numerous code examples and exercises throughout…
I started my career in neuroscience. I wanted to understand brains. That is still proving difficult, and somewhere along the way, I realized my real motivation was to build things, and I wound up working in AI. I love the elegance of mathematical models of the world. Even the simplest machine learning model has complex implications, and exploring them is a joy.
Of course, this is not the obvious book to recommend for reinforcement learning, but if you are a beginner, then it’s a quick and easy place to start. It’s compact and gets straight into the main algorithms.
It has a good balance between theory and code and will get you up and running quickly.
The Contemporary Introduction to Deep Reinforcement Learning that Combines Theory and Practice
Deep reinforcement learning (deep RL) combines deep learning and reinforcement learning, in which artificial agents learn to solve sequential decision-making problems. In the past decade deep RL has achieved remarkable results on a range of problems, from single and multiplayer games-such as Go, Atari games, and DotA 2-to robotics.
Foundations of Deep Reinforcement Learning is an introduction to deep RL that uniquely combines both theory and implementation. It starts with intuition, then carefully explains the theory of deep RL algorithms, discusses implementations in its companion software library SLM…
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’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 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…
