Practical Deep Learning
If you've been curious about machine learning but didn't know where to start, this is the book you've been waiting for. Focusing on the subfield of machine learning known as deep learning, it explains core concepts and gives you the foundation you need to start building your own models. Rather than simply outlining recipes for using existing toolkits, Practical Deep Learning teaches you the why of deep learning and will inspire you to explore further.
All you need is basic familiarity with computer programming and high school math - the book will cover the rest. After an introduction to Python you'll move through key topics like how to build a good training dataset, work with the scikit-learn and Keras libraries, and evaluate your models' performance.
You'll also learn:
- How to use classic machine learning models like k-Nearest Neighbors, Random Forests, and Support Vector Machines;
- How neural networks work and how they're trained;
- How to use convolutional neural n ...
Dive Into Algorithms
Dive Into Algorithms is a wide-ranging, Pythonic tour of many of the world's most interesting algorithms. With little more than a bit of computer programming experience and basic high-school math, you'll explore standard computer science algorithms for searching, sorting, and optimization; human-based algorithms that help us determine how to catch a baseball or eat the right amount at a buffet; and advanced algorithms like ones used in machine learning and artificial intelligence. You'll even explore how ancient Egyptians and Russian peasants used algorithms to multiply numbers, how the ancient Greeks used them to find greatest common divisors, and how Japanese scholars in the age of samurai designed algorithms capable of generating magic squares.
You'll explore algorithms that are useful in pure mathematics and learn how mathematical ideas can improve algorithms. You'll learn about an algorithm for generating continued fractions, one for quick calculations of square roots, and anot ...
Computer Vision Using Deep Learning
Organizations spend huge resources in developing software that can perform the way a human does. Image classification, object detection and tracking, pose estimation, facial recognition, and sentiment estimation all play a major role in solving computer vision problems.
This book will bring into focus these and other deep learning architectures and techniques to help you create solutions using Keras and the TensorFlow library. You'll also review mutliple neural network architectures, including LeNet, AlexNet, VGG, Inception, R-CNN, Fast R-CNN, Faster R-CNN, Mask R-CNN, YOLO, and SqueezeNet and see how they work alongside Python code via best practices, tips, tricks, shortcuts, and pitfalls. All code snippets will be broken down and discussed thoroughly so you can implement the same principles in your respective environments.
Computer Vision Using Deep Learning offers a comprehensive yet succinct guide that stitches DL and CV together to automate operations, reduce human i ...
Beyond the Basic Stuff with Python
You've completed a basic Python programming tutorial or finished Al Sweigart's best selling Automate the Boring Stuff with Python. What's the next step toward becoming a capable, confident software developer?
Welcome to Beyond the Basic Stuff with Python. More than a mere collection of advanced syntax and masterful tips for writing clean code, you'll learn how to advance your Python programming skills by using the command line and other professional tools like code formatters, type checkers, linters, and version control. Sweigart takes you through best practices for setting up your development environment, naming variables, and improving readability, then tackles documentation, organization and performance measurement, as well as object-oriented design and the Big-O algorithm analysis commonly used in coding interviews. The skills you learn will boost your ability to program - not just in Python but in any language.
- Coding style, and how to u ...
Machine Learning for Algorithmic Trading, 2nd Edition
The explosive growth of digital data has boosted the demand for expertise in trading strategies that use machine learning (ML). This thoroughly revised and expanded 2nd edition enables you to build and evaluate sophisticated supervised, unsupervised, and reinforcement learning models.
This edition introduces end-to-end machine learning for the trading workflow, from the idea and feature engineering to model optimization, strategy design, and backtesting. It illustrates this workflow using examples that range from linear models and tree-based ensembles to deep-learning techniques from the cutting edge of the research frontier.
This revised version shows how to work with market, fundamental, and alternative data, such as tick data, minute and daily bars, SEC filings, earnings call transcripts, financial news, or satellite images to generate tradeable signals. It illustrates how to engineer financial features or alpha factors that enable a machine learning model to predict returns f ...
Python Machine Learning By Example, 3rd Edition
Python Machine Learning By Example, 3rd Edition serves as a comprehensive gateway into the world of machine learning (ML).
With six new chapters, on topics including movie recommendation engine development with Naïve Bayes, recognizing faces with support vector machine, predicting stock prices with artificial neural networks, categorizing images of clothing with convolutional neural networks, predicting with sequences using recurring neural networks, and leveraging reinforcement learning for making decisions, the book has been considerably updated for the latest enterprise requirements.
At the same time, this book provides actionable insights on the key fundamentals of ML with Python programming. Hayden applies his expertise to demonstrate implementations of algorithms in Python both from scratch and with libraries.
Each chapter walks through an industry-adopted application. With the help of realistic examples, you will gain an understanding of the mechan ...
Advanced Analytics in Power BI with R and Python
This easy-to-follow guide provides R and Python recipes to help you learn and apply the top languages in the field of data analytics to your work in Microsoft Power BI. Data analytics expert and author Ryan Wade shows you how to use R and Python to perform tasks that are extremely hard, if not impossible, to do using native Power BI tools. For example, you will learn to score Power BI data using custom data science models and powerful models from Microsoft Cognitive Services.
The R and Python languages are powerful complements to Power BI. They enable advanced data transformation techniques that are difficult to perform in Power BI in its default configuration but become easier by leveraging the capabilities of R and Python. If you are a business analyst, data analyst, or a data scientist who wants to push Power BI and transform it from being just a business intelligence tool into an advanced data analytics tool, then this is the book to help you do that. ...
Building Versatile Mobile Apps with Python and REST
Develop versatile iOS apps using Python with RESTful web services. This book will show you how to blend Django, a high-level Python Web framework, with Django REST, the powerful, feature-filled extension, to build iOS mobile apps.
Using easy-to-follow examples, you'll begin by building a simple app using the RESTful Web API and iOS. You'll begin by using traditional Django to create models and connect your App to the database. You'll then see how to serialize your data and create the RESTful API.
The second part of the book introduces Xcode, a programming environment to develop iOS apps. Using Swift, the programming language for iOS, you'll design the actual app. Once you have your back-end in Django and a front-end in Swift, you'll connect them using our RESTful API. You'll be able to log in, browse places of interest, and rate them and leave comments.
Guided step-by-step instructions, Building Versatile Mobile Apps with Python and REST will demonstrate how ...
Artificial Intelligence in Finance
The widespread adoption of AI and machine learning is revolutionizing many industries today. Once these technologies are combined with the programmatic availability of historical and real-time financial data, the financial industry will also change fundamentally. With this practical book, you'll learn how to use AI and machine learning to discover statistical inefficiencies in financial markets and exploit them through algorithmic trading.
Author Yves Hilpisch shows practitioners, students, and academics in both finance and data science practical ways to apply machine learning and deep learning algorithms to finance. Thanks to lots of self-contained Python examples, you'll be able to replicate all results and figures presented in the book.
In five parts, this guide helps you: Learn central notions and algorithms from AI, including recent breakthroughs on the way to artificial general intelligence (AGI) and superintelligence (SI); Understand why data-driven finance, AI, and ...
With its emphasis on project-based practice, Real World Python will take you from playing with syntax to writing complete programs in no time. You'll conduct experiments, explore statistical concepts, and solve novel problems that have frustrated geniuses throughout history, like detecting distant exoplanets, as you continue to build your Python skills.
Chapters begin with a clearly defined project goal and a discussion of ways to attack the problem, followed by a mission designed to make you think like a programmer. You'll direct a Coast Guard search-and-rescue effort, plot and execute a NASA flight to the moon, protect access to a secure lab using facial recognition, and more. Along the way you'll learn how to: Use libraries like matplotlib, NumPy, Bokeh, pandas, Requests, Beautiful Soup, and turtle; Work with Natural Language Processing and computer vision modules like NLTK and OpenCV; Write a program to detect and track objects moving across a starfield; Scrape spe ...
The only way to master a skill is to practice. In Python Workout, author Reuven M. Lerner guides you through 50 carefully selected exercises that invite you to flex your programming muscles. As you take on each new challenge, you'll build programming skill and confidence. The thorough explanations help you lock in what you've learned and apply it to your own projects. Along the way, Python Workout provides over four hours of video instruction walking you through the solutions to each exercise and dozens of additional exercises for you to try on your own.
To become a champion Python programmer you need to work out, building mental muscle with your hands on the keyboard. Each carefully selected exercise in this unique book adds to your Python prowess - one important skill at a time.
Python Workout presents 50 exercises that focus on key Python 3 features. In it, expert Python coach Reuven Lerner guides you through a series of small p ...