This is the code repository for Python Deep Learning - Third Edition, published by Packt.
Understand how deep neural networks work and apply them to real-world tasks
The field of deep learning has developed rapidly in the past years and today covers broad range of applications. This makes it challenging to navigate and hard to understand without solid foundations. This book will guide you from the basics of neural networks to the state-of-the-art large language models in use today.
This book covers the following exciting features:
- Establish theoretical foundations of deep neural networks
- Understand convolutional networks and apply them in computer vision applications
- Become well versed with natural language processing and recurrent networks
- Explore the attention mechanism and transformers
- Apply transformers and large language models for natural language and computer vision
- Implement coding examples with PyTorch, Keras, and Hugging Face Transformers
- Use MLOps to develop and deploy neural network models
If you feel this book is for you, get your copy today!
All of the code is organized into folders.
The code will look like the following:
def build_fe_model():
""""Create feature extraction model from the pre-trained model ResNet50V2"""
# create the pre-trained part of the network, excluding FC layers
base_model = tf.keras.applications.MobileNetV3Small
Following is what you need for this book: This book is for software developers/engineers, students, data scientists, data analysts, machine learning engineers, statisticians, and anyone interested in deep learning. Prior experience with Python programming is a prerequisite.
With the following software and hardware list you can run all code files present in the book (Chapter 1-10).
| Chapter | Software required | OS required |
|---|---|---|
| 1-10 | PyTorch 2.0.1 | Windows, macOS, or Linux |
| 1-10 | TensorFlow 2.13 | Windows (legacy support), macOS, or Linux |
| 1-10 | Hugging Face Transformers 4.33 | Windows, macOS, or Linux |
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Hands-On Graph Neural Networks Using Python [Packt] [Amazon]
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Enhancing Deep Learning with Bayesian Inference [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 its development. 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 focused on financial machine learning. He has co-founded an algorithmic trading company, where he's the lead engineer.
He holds an MSc degree in Artificial Intelligence from The University of Sofia, St. Kliment Ohridski, and has written two prior books on the same topic.