Deep learning is a branch of machine learning algorithms based on learning multiple levels of abstraction. Neural networks, which are at the core of deep learning, are being used in predictive analytics, computer vision, natural language processing, time series forecasting, and to perform a myriad of other complex tasks.

This book is conceived for developers, data analysts, machine learning practitioners and deep learning enthusiasts who want to build powerful, robust, and accurate predictive models with the power of TensorFlow, combined with other open source Python libraries.

Throughout the book, you’ll learn how to develop deep learning applications for machine learning systems using Feedforward Neural Networks, Convolutional Neural Networks, Recurrent Neural Networks, Autoencoders, and Factorization Machines. Discover how to attain deep learning programming on GPU in a distributed way.

You'll come away with an in-depth knowledge of machine learning techniques and the skills to apply them to real-world projects.

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Spis treści

Deep Learning with TensorFlow. Explore neural networks and build intelligent systems with Python - Second Edition eBook -- spis treści

• 1. Getting Started with Deep Learning
• 2. A First Look at TensorFlow
• 3. Feed-Forward Neural Networks with TensorFlow
• 4. Convolutional Neural Networks
• 5. Optimizing TensorFlow Autoencoders
• 6. Recurrent Neural Networks
• 7. Heterogeneous and Distributed Computing
• 8. Advanced TensorFlow Programming
• 9. Recommendation Systems using Factorization Machines
• 10. Reinforcement Learning