Description
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, 3rd Edition, is a comprehensive book designed to equip programmers with the knowledge and tools to implement machine learning and deep learning systems. Through a series of recent breakthroughs, deep learning has significantly advanced the field, making it accessible to those with limited prior knowledge of the technology. This book bridges that gap by focusing on practical application and intuitive understanding.
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The core of the book revolves around using production-ready Python frameworks: Scikit-Learn for traditional machine learning tasks and Keras and TensorFlow for deep learning. It guides readers through building intelligent systems by starting with fundamental concepts like linear regression and progressively moving towards complex deep neural networks. The approach emphasizes concrete examples and minimal theory, ensuring that readers can quickly gain practical skills.
The updated third edition explores a wide array of techniques. Readers will learn to track an example ML project from start to finish using Scikit-learn, covering various models such as support vector machines, decision trees, random forests, and ensemble methods. Unsupervised learning techniques, including dimensionality reduction, clustering, and anomaly detection, are also thoroughly explored. For deep learning, the book delves into neural network architectures like convolutional nets, recurrent nets, generative adversarial networks, autoencoders, diffusion models, and transformers.
Furthermore, the book provides extensive guidance on using TensorFlow and Keras to construct and train neural networks for diverse applications, including computer vision, natural language processing, generative models, and deep reinforcement learning. Numerous code examples and exercises are integrated throughout the text to reinforce learning and encourage hands-on experimentation. The only prerequisite is programming experience, making it an ideal resource for developers looking to enter the field of AI and machine learning.
- CategoryOther AI Tools
Book Details
Practical guide to machine learning and deep learning
Utilizes Scikit-Learn for traditional ML tasks
Employs Keras and TensorFlow for deep learning
Covers techniques from linear regression to deep neural networks
Focuses on concrete examples and minimal theory
Includes numerous code examples and exercises
Explores various ML models (SVM, decision trees, random forests, ensembles)
Covers unsupervised learning (dimensionality reduction, clustering, anomaly detection)
Details neural network architectures (CNNs, RNNs, GANs, autoencoders, diffusion models, transformers)
Applications in computer vision, NLP, generative models, and deep reinforcement learning
Updated third edition with current practices
Who This Book Is For
- Build Intelligent Systems
- Computer Vision Tasks
- Natural Language Processing
- Generative Models
- Deep Reinforcement Learning
- Data Analysis and Prediction
- Anomaly Detection
