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.
publication date
2019-02-28
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
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
approach
Readers will be taken through various neural network architectures in a specific real-world use case. We will explain a key component of the architecture (e.g. convolution, max pooling), and their reasoning. They will learn how to perform data pre-processing and how to implement the algorithms in Python. The reader will be taken through sequential projects that build on additional complexity.
audience
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.
short description
This book contains practical implementations of several deep learning projects in multiple domains, including in regression-based tasks such as taxi fare prediction in New York City, image classification of cats and dogs using a convolutional neural network, implementing a facial recognition security system using Siamese Neural Networks, and more.
subtitle
The ultimate guide to using Python to explore the true power of neural networks through six projects
keywords
Neural Network, Deep Learning, Machine Learning, Artificial Intelligence, Python, Keras, TensorFlow
Product ISBN
9781789138900
