Overview

Are you ready to build powerful artificial intelligence models that solve real-world problems? This practical guide provides the essential knowledge and hands-on skills for deep learning with PyTorch. PyTorch for Data Science offers a clear, practical pathway to mastering deep learning. This book starts with the core components of PyTorch, guiding you through tensor operations, automatic differentiation, and efficient data handling. You'll then progress to constructing and training various neural network architectures, including feedforward networks and Convolutional Neural Networks (CNNs) for image tasks, and Recurrent Neural Networks (RNNs) for sequential data like text. Beyond foundational concepts, this guide explores advanced topics such as Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs) for creating new data, and Graph Neural Networks (GNNs) for complex relational data. Crucially, it emphasizes responsible AI development, covering vital aspects like bias, explainability, privacy, and the importance of human oversight. The book culminates in a comprehensive project: building and deploying a multi-label image classifier, bringing all learned concepts into a practical application. What's inside this Book? Build diverse neural networks: Create models for image, text, and graph data. Master transfer learning: Effectively use pre-trained models for faster, better results.1 Understand generative AI: Learn to build models that create new content. Implement ethical AI practices: Address bias, ensure transparency, and protect data privacy. Deploy AI models: Turn your trained models into accessible web APIs for real-world use. Get Your Copy Today!Start building and deploying intelligent systems with confidence. Purchase PyTorch for Data Science and transform your AI development skills.

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