Articles liés à Learn Keras for Deep Neural Networks: A Fast-Track...
Learn Keras for Deep Neural Networks: A Fast-Track Approach to Modern Deep Learning with Python - Couverture souple
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 Goal: 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 Keras: What's special about Keras?
Chapter 2: Keras in Action
Chapter Goal: 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 sophisticationChapter 3: Deep Neural networks for Supervised Learning
Chapter Goal: Embrace the core fundamentals of deep learning and its development
Sub - Topics:
1. Introduction to supervised learning
2. Classification use-case - implementing DNN
3. Regression use-case - implementing DNNChapter 4: Measuring Performance for DNN
Chapter Goal: Aid the reader in understanding the craft of validating deep neural networks
Sub - Topics:
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 DeploymentChapter Goal: Understand how to tune the model hyperparameters to achieve improved performance
Sub - Topics:
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 - Topics:
1. What's next for deep learning expertise?
2. Further reading
3. GPU for deep learning
4. Active research areas and breakthroughs in deep learning5. Conclusion
Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.
