Neural Network Projects with Python: The ultimate guide to using Python to explore the true power of neural networks through six projects - Softcover

Loy, James

Softcover

ISBN 10: 1789138906 ISBN 13: 9781789138900

Verlag: Packt Publishing, 2019

Über diesen Titel

Reseña del editor

Build your Machine Learning portfolio by creating 6 cutting-edge Artificial Intelligence projects using neural networks in Python

Key Features

• 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

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?

Biografía del autor

James Loy has more than five years, expert experience in data science in the finance and healthcare industries. He has worked with the largest bank in Singapore to drive innovation and improve customer loyalty through predictive analytics. He has also experience in the healthcare sector, where he applied data analytics to improve decision-making in hospitals. He has a master's degree in computer science from Georgia Tech, with a specialization in machine learning. His research interest includes deep learning and applied machine learning, as well as developing computer-vision-based AI agents for automation in industry. He writes on Towards Data Science, a popular machine learning website with more than 3 million views per month.

Details zur Ausgabe

• Verlag: Packt Publishing
• Erscheinungsdatum: 2019
• ISBN 10: 1789138906
• ISBN 13: 9781789138900
• Einband: Tapa blanda
• Sprache: Englisch
• Anzahl der Seiten: 308