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LEARN KERAS FOR DEEP NEURAL NETWORKS: A FAST-TRACK APPROACH TO MODERN DEEP LEARNING WITH PYTHON [Paperback] Moolayil - Softcover
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Synopsis
SECTION 1: Prepares the reader with all the necessary gears to get started on the fast track ride in deep learning. Chapter 1: Deep Learning & Keras Chapter Introduce the reader to the deep learning and keras framework Sub -Topics 1. Exploring the popular Deep Learning frameworks 2. Overview of Keras, Pytorch, mxnet, Tensorflow, 3. A closer look at What's special about Keras? Chapter 2: Keras in Action Chapter Help the reader to engage with hands-on exercises with Keras and implement the first basic deep neural network Sub - Topics 1. A closer look at the deep learning building blocks 2. Exploring the keras building blocks for deep learning 3. Implementing a basic deep neural network with dummy data SECTION 2 - Help the reader embrace the core fundamentals in simple lucid language while abstracting the math and the complexities of model training and validation with the least amount of code without compromising on flexibility, scale and the required sophistication
Chapter 3: Deep Neural networks for Supervised Learning Chapter Embrace the core fundamentals of deep learning and its development Sub - 1. Introduction to supervised learning 2. Classification use-case - implementing DNN 3. Regression use-case - implementing DNN Chapter 4: Measuring Performance for DNN Chapter Aid the reader in understanding the craft of validating deep neural networks Sub - 1. Metrics for success - regression 2. Analyzing the regression neural network performance 3. Metrics for success - classification 4. Analyzing the regression neural network performance SECTION 3 - Tuning and deploying robust DL models Chapter 5: Hyperparameter Tuning & Model Deployment Chapter Understand how to tune the model hyperparameters to achieve improved performance Sub - 1. Hyperparameter tuning for deep learning models 2. Model deployment and transfer learning Chapter 6: The Path Forward Chapter goal - Educate the reader about additional reading for advanced topics within deep learning. Sub - 1. What's next for deep learning expertise? 2. Further reading 3. GPU for deep learning 4. Active research areas and breakthroughs in deep learning 5. Conclusion
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