This is the code repository for Hands-On Deep Learning Architectures with Python, published by Packt.
Create deep neural networks to solve computational problems using TensorFlow and Keras
Deep learning architectures are composed of multilevel nonlinear operations that represent high-level abstractions; this allows you to learn useful feature representations from the data. This book will help you learn and implement deep learning architectures to resolve various deep learning research problems.
This book covers the following exciting features:
- Implement CNNs, RNNs, and other commonly used architectures with Python
- Explore architectures such as VGGNet, AlexNet, and GoogLeNet
- Build deep learning architectures for AI applications such as face and image recognition, fraud detection, and many more
- Understand the architectures and applications of Boltzmann machines and autoencoders with concrete examples
- Master artificial intelligence and neural network concepts and apply them to your architecture
If you feel this book is for you, get your copy today!
All of the code is organized into folders. For example, Chapter02.
The code will look like the following:
import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
Following is what you need for this book: If you're a data scientist, machine learning developer/engineer, or deep learning practitioner, or are curious about AI and want to upgrade your knowledge of various deep learning architectures, this book will appeal to you. You are expected to have some knowledge of statistics and machine learning algorithms to get the best out of this book
With the following software and hardware list you can run all code files present in the book.
| Chapter | Software required | OS required |
|---|---|---|
| 1 | TensorFlow, Keras | Windows, Mac OS X, and Linux (Any) |
| 2 - 8 | Python 3.x | Windows, Mac OS X, and Linux (Any) |
We also provide a PDF file that has color images of the screenshots/diagrams used in this book. Click here to download it.
Yuxi (Hayden) Liu is an author of a series of machine learning books and an education enthusiast. His first book, the first edition of Python Machine Learning By Example, was a #1 bestseller on Amazon India in 2017 and 2018 and his other book R Deep Learning Projects, both published by Packt Publishing. He is an experienced data scientist who is focused on developing machine learning and deep learning models and systems. He has worked in a variety of data-driven domains and has applied his machine learning expertise to computational advertising, recommendations, and network anomaly detection. He published five first-authored IEEE transaction and conference papers during his master's research at the University of Toronto.
Saransh Mehta has cross-domain experience of working with texts, images, and audio using deep learning. He has been building artificial, intelligence-based solutions, including a generative chatbot, an attendee-matching recommendation system, and audio keyword recognition systems for multiple start-ups. He is very familiar with the Python language, and has extensive knowledge of deep learning libraries such as TensorFlow and Keras. He has been in the top 10% of entrants to deep learning challenges hosted by Microsoft and Kaggle.
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