Machine Learning For Programmers
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Programming Machine Learning
Author: Paolo Perrotta
language: en
Publisher: The Pragmatic Programmers LLC
Release Date: 2020-03-31
You've decided to tackle machine learning - because you're job hunting, embarking on a new project, or just think self-driving cars are cool. But where to start? It's easy to be intimidated, even as a software developer. The good news is that it doesn't have to be that hard. Master machine learning by writing code one line at a time, from simple learning programs all the way to a true deep learning system. Tackle the hard topics by breaking them down so they're easier to understand, and build your confidence by getting your hands dirty. Peel away the obscurities of machine learning, starting from scratch and going all the way to deep learning. Machine learning can be intimidating, with its reliance on math and algorithms that most programmers don't encounter in their regular work. Take a hands-on approach, writing the Python code yourself, without any libraries to obscure what's really going on. Iterate on your design, and add layers of complexity as you go. Build an image recognition application from scratch with supervised learning. Predict the future with linear regression. Dive into gradient descent, a fundamental algorithm that drives most of machine learning. Create perceptrons to classify data. Build neural networks to tackle more complex and sophisticated data sets. Train and refine those networks with backpropagation and batching. Layer the neural networks, eliminate overfitting, and add convolution to transform your neural network into a true deep learning system. Start from the beginning and code your way to machine learning mastery. What You Need: The examples in this book are written in Python, but don't worry if you don't know this language: you'll pick up all the Python you need very quickly. Apart from that, you'll only need your computer, and your code-adept brain.
Machine Learning for Programmers
Author: Booker Blunt
language: en
Publisher: Independently Published
Release Date: 2025-06-28
Build Smarter Software with Machine Learning-Without Starting from Zero If you're a programmer who wants to break into artificial intelligence, Machine Learning for Programmers is your fast track. This book skips the math-heavy theory and goes straight into building intelligent, real-world applications using Python. Designed for developers, not data scientists, this guide focuses on practical implementation: loading data, training models, testing predictions, and deploying ML systems using tools you already know-like Python, scikit-learn, and TensorFlow. What You'll Learn: Core ML concepts made simple for coders How to build classification, regression, and clustering models Real-world projects: spam filters, recommendation engines, and more Step-by-step tutorials using Python and popular ML libraries Data preparation and feature engineering explained clearly Hyperparameter tuning, model evaluation, and cross-validation The basics of neural networks and deep learning How to deploy models into live applications and services Best practices for clean, testable, production-ready ML code You don't need a PhD to get started-just Python skills and a problem to solve. Code smarter. Think in models. Build the future.
Deep Learning for Coders with fastai and PyTorch
Author: Jeremy Howard
language: en
Publisher: "O'Reilly Media, Inc."
Release Date: 2020-06-29
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 gain a complete understanding of the algorithms behind the scenes. Train models in computer vision, natural language processing, tabular data, and collaborative filtering Learn the latest deep learning techniques that matter most in practice Improve accuracy, speed, and reliability by understanding how deep learning models work Discover how to turn your models into web applications Implement deep learning algorithms from scratch Consider the ethical implications of your work Gain insight from the foreword by PyTorch cofounder, Soumith Chintala