PyTorch for Deep Learning: Building and training neural networks for predictive analytics
You've mastered Scikit-Learn. You've run your regressions. But your model's accuracy has plateaued.
PyTorch for Deep Learning is the key to breaking through that ceiling. While other frameworks hide the complexity behind rigid APIs, PyTorch puts the power of the "Dynamic Computation Graph" in your hands. It allows you to build, debug, and iterate on neural networks with the same flexibility and ease as writing standard Python code.
This book is tailored for the Data Scientist who wants to apply Deep Learning to Predictive Analytics. We move beyond the standard "cats vs. dogs" tutorials to focus on what matters to you: forecasting sales, predicting customer churn, and analyzing complex tabular data.
The Pythonic Way to Build IntelligenceThis is a code-first guide. You will learn to construct neural networks from the ground up, understanding every layer, every neuron, and every tensor operation.
Tensors & Autograd: Master the building blocks of PyTorch. Learn how to manipulate data on the GPU and how automatic differentiation makes training massive models possible.
The Training Loop: Stop calling .fit(). Learn to write your own training loops to have complete control over optimization, learning rates, and gradient descent.
Tabular Deep Learning: Discover architectures specifically designed for structured business data (Excel/SQL outputs) that outperform gradient boosting methods.
Time Series Forecasting: Build Recurrent Neural Networks (RNNs) and LSTMs to predict future trends based on historical sequences.
Model Deployment: Learn to export your PyTorch models to ONNX or serve them via a REST API, bridging the gap between research and production.
Whether you are a researcher needing flexibility or an engineer building a recommendation engine, this book gives you the tools to solve your hardest problems.
Don't just use a library. Master the framework. Scroll up and grab your copy to start building the future of analytics.
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