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Machine Learning with Pytorch and Scikit-Learn
Book • 2022
This book is a thorough introduction to machine learning, starting with traditional machine learning using Scikit-Learn and progressing to deep learning with PyTorch.
It covers a wide range of topics, including data preprocessing, model evaluation, hyperparameter tuning, neural networks, transformers, graph neural networks, and reinforcement learning.
The book provides clear explanations, visualizations, and practical examples to help readers build and train various machine learning models.
It also includes the latest trends in deep learning, such as large-scale transformers for natural language processing and generative adversarial networks for generating new data.
It covers a wide range of topics, including data preprocessing, model evaluation, hyperparameter tuning, neural networks, transformers, graph neural networks, and reinforcement learning.
The book provides clear explanations, visualizations, and practical examples to help readers build and train various machine learning models.
It also includes the latest trends in deep learning, such as large-scale transformers for natural language processing and generative adversarial networks for generating new data.
