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

Book Description

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.

What you will learn

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

Who this book is for

This book is a perfect match for data scientists, machine learning engineers, and deep learning enthusiasts who wish to create practical neural network projects in Python. Readers should already have some basic knowledge of machine learning and neural networks.

Table of Contents

Machine Learning and Neural Networks 101
Predicting Diabetes with Multilayer Perceptrons
Predicting Taxi Fares with Deep Feedforward Networks
Cats Versus Dogs - Image Classification Using CNNs
Removing Noise from Images Using Autoencoders
Sentiment Analysis of Movie Reviews Using LSTM
Implementing a Facial Recognition System with Neural Networks
What's Next?

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