Deep Learning with Python 1st Edition by Nikhil Ketkar 1484227654 9781484227657

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ISBN 10: 1484227654

ISBN 13: 9781484227657

Author: Nikhil Ketkar

Discover the practical aspects of implementing deep-learning solutions using the rich Python ecosystem. This book bridges the gap between the academic state-of-the-art and the industry state-of-the-practice by introducing you to deep learning frameworks such as Keras, Theano, and Caffe. The practicalities of these frameworks is often acquired by practitioners by reading source code, manuals, and posting questions on community forums, which tends to be a slow and a painful process. Deep Learning with Python allows you to ramp up to such practical know-how in a short period of time and focus more on the domain, models, and algorithms. This book briefly covers the mathematical prerequisites and fundamentals of deep learning, making this book a good starting point for software developers who want to get started in deep learning. A brief survey of deep learning architectures is also included. Deep Learning with Python alsointroduces you to key concepts of automatic differentiation and GPU computation which, while not central to deep learning, are critical when it comes to conducting large scale experiments.  What You Will Learn  Leverage deep learning frameworks in Python namely, Keras, Theano, and Caffe  Gain the fundamentals of deep learning with mathematical prerequisites  Discover the practical considerations of large scale experiments  Take deep learning models to production Who This Book Is For Software developers who want to try out deep learning as a practical solution to a particular problem. Software developers in a data science team who want to take deep learning models developed by data scientists to production.

Deep Learning with Python 1st Table of contents:

1. Introduction to Deep Learning

2. Machine Learning Fundamentals

3. Feed Forward Neural Networks

4. Introduction to Theano

5. Convolutional Neural Networks

6. Recurrent Neural Networks

7. Introduction to Keras

8. Stochastic Gradient Descent

9. Automatic Differentiation

10. Introduction to GPUs

11. Introduction to Tensorflow

12. Introduction to PyTorch

13. Regularization Techniques

14. Training Deep Learning Models

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Nikhil Ketkar,Learning,Python,Deep