Machine learning with PyTorch and Scikit-Learn : develop machine learning and deep learning models with Python

Indhold: Giving computers the ability to learn from data ; Training simple machine learning algorithms for classification ; A tour of machine learning classifiers using scikit-learn ; Building good training datasets – data preprocessing ; Compressing data via dimensionality reduction ; Learning best practices for model evaluation and hyperparameter tuning ; Combining different models for ensemble learning ; Applying machine learning to sentiment analysis ; Predicting continuous target variables with regression analysis ; Working with unlabeled data – clustering analysis ; Implementing a multilayer artificial neural network from scratch ; Parallelizing neural network training with pytorch ; Going deeper – the mechanics of pytorch ; Classifying images with deep convolutional neural networks ; Modeling sequential data using recurrent neural networks ; Transformers – improving natural language processing with attention mechanisms ; Generative adversarial networks for synthesizing new data ; Graph neural networks for capturing dependencies in graph structured data ; Reinforcement learning for decision making in complex environments