English | 2020 | 769 Pages | PDF, EPUB, CODE | 185 MB
Zero to Deep Learning is carefully designed to teach you step-by-step how to build, train, evaluate, improve and deploy deep learning models. Each chapter covers a topic and we provide full code examples as executable Jupyter notebooks.
Within the first few minutes, we’ll know enough deep learning to start seeing the benefits of using it in our applications.
Learn the best practices, such as: handling overfitting, code organization, and how to serve our model to our apps. We’ll walk through practical, common examples of how to implement complete applications powered by deep learning.
Every single chapter and line of code includes an interactive Jupyter notebook. You’ll get access to a Jupyter notebook for all code samples.
You’ll learn core deep learning concepts – from the multiperceptron through deep neural networks including convolutional and recurrent neural networks.
- Getting Started
- Data Manipulation
- Machine Learning
- Deep Learning
- Deep Learning Internals
- Convolutional Neural Networks
- Time Series and Recurrent Neural Networks
- Natural Language Processing and Text Data
- Training with GPUs
- Performance Improvement
- Pre-trained Models for Images
- Pre-trained Models for Text
- Serving Deep Learning Models
- Conclusion and Next Steps