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\nChapter Goal: Introduce the reader to the deep learning and keras framework

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1. Exploring the popular Deep Learning frameworks

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2. Overview of Keras, Pytorch, mxnet, Tensorflow,

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3. A closer look at Keras: What\'s special about Keras?

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Chapter 2: Keras in Action\nChapter Goal: Help the reader to engage with hands-on exercises with Keras and implement the first basic deep neural network

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1. A closer look at the deep learning building blocks

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2. Exploring the keras building blocks for deep learning

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3. Implementing a basic deep neural network with dummy data

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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 \n Chapter 3: Deep Neural networks for Supervised Learning\nChapter Goal: Embrace the core fundamentals of deep learning and its development

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1. Introduction to supervised learning

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2. Classification use-case - implementing DNN

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3. Regression use-case - implementing DNN

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Chapter 4: Measuring Performance for DNN\nChapter Goal: Aid the reader in understanding the craft of validating deep neural networks

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1. Metrics for success - regression

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2. Analyzing the regression neural network performance

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3. Metrics for success - classification

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4. Analyzing the regression neural network performance

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SECTION 3 - Tuning and deploying robust DL models

Chapter 5: Hyperparameter Tuning & Model Deployment\nChapter Goal: Understand how to tune the model hyperparameters to achieve improved performance

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1. Hyperparameter tuning for deep learning models

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2. Model deployment and transfer learning

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Chapter 6: The Path Forward

Chapter goal - Educate the reader about additional reading for advanced topics within deep learning.

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1. What\'s next for deep learning expertise?

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2. Further reading

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3. GPU for deep learning

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4. Active research areas and breakthroughs in deep learning

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5. Conclusion

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