Neural Network Projects with Python: The ultimate guide to using Python to explore the true power of neural networks through six projects
By 作者: James Loy
ISBN-10 书号: 1789138906
ISBN-13 书号: 9781789138900
Release Finelybook 出版日期: 2019-02-28
pages 页数: (308 )
The Book Description
Learn various neural network architectures and its advancements in AI
Master deep learning in Python by building and training neural network
Master neural networks for regression and classification
Discover convolutional neural networks for image recognition
Learn sentiment analysis on textual data using Long Short-Term Memory
Build and train a highly accurate facial recognition security system
Neural networks are at the core of recent AI advances, providing some of the best resolutions to many real-world problems, including image recognition, medical diagnosis, text analysis, and more. This book goes through some basic neural network and deep learning concepts, as well as some popular libraries in Python for implementing them.
It contains practical demonstrations of neural networks in domains such as fare prediction, image classification, sentiment analysis, and more. In each case, the book provides a problem statement, the specific neural network architecture required to tackle that problem, the reasoning behind the algorithm used, and the associated Python code to implement the solution from scratch. In the process, you will gain hands-on experience with using popular Python libraries such as Keras to build and train your own neural networks from scratch.
By the end of this book, you will have mastered the different neural network architectures and created cutting-edge AI projects in Python that will immediately strengthen your machine learning portfolio.
Discover neural network architectures (like CNN and LSTM) that are driving recent advancements in AI
Build expert neural networks in Python using popular libraries such as Keras
Includes projects such as object detection, face identification, sentiment analysis, and more
1 Machine Learning and Neural Networks 101
2 Predicting Diabetes with Multilayer Perceptrons
3 Predicting Taxi Fares with Deep Feedforward Networks
4 Cats Versus Dogs - Image Classification Using CNNs
5 Removing Noise from Images Using Autoencoders
6 Sentiment Analysis of Movie Reviews Using LSTM
7 Implementing a Facial Recognition System with Neural Networks
8 What's Next?