This is the code repository for Advanced Deep Learning with Python, published by Packt.
Design and implement advanced next-generation AI solutions using TensorFlow and PyTorch
This book is an expert-level guide to master the neural network variants using the Python ecosystem. You will gain the skills to build smarter, faster, and efficient deep learning systems with practical examples. By the end of this book, you will be up to date with the latest advances and current researches in the deep learning domain.
This book covers the following exciting features:
- Cover advanced and state-of-the-art neural network architectures
- Understand the theory and math behind neural networks
- Train DNNs and apply them to modern deep learning problems
- Use CNNs for object detection and image segmentation
- Implement generative adversarial networks (GANs) and variational autoencoders to generate new images
- Solve natural language processing (NLP) tasks, such as machine translation, using sequence-to-sequence models
- Understand DL techniques, such as meta-learning and graph neural networks
If you feel this book is for you, get your copy today!
All of the code files is organized into folders.
Following is what you need for this book: To get the most out of this book, you should be familiar with Python and have some knowledge of machine learning. The book includes short introductions to the major types of NNs, but it will help if you are already familiar with the basics of NNs.
With the following software and hardware list you can run all code files present in the book (Chapter 2-11).
| Chapter | Software required | OS required |
|---|---|---|
| 2-11 | Python 3.7 (TensorFlow 2.0.0, PyTorch 1.3.1) | Windows/macOS/Linux |
We also provide a PDF file that has color images of the screenshots/diagrams used in this book. Click here to download it.
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Hands-On Deep Learning Architectures with Python [Packt] [Amazon]
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Hands-On Deep Learning Algorithms with Python [Packt] [Amazon]
Ivan Vasilev started working on the first open source Java deep learning library with GPU support in 2013. The library was acquired by a German company, where he continued to develop it. He has also worked as a machine learning engineer and researcher in the area of medical image classification and segmentation with deep neural networks. Since 2017, he has been focusing on financial machine learning. He is working on a Python-based platform that provides the infrastructure to rapidly experiment with different machine learning algorithms for algorithmic trading. Ivan holds an MSc degree in artificial intelligence from the University of Sofia, St. Kliment Ohridski.
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