Object recognition is the ability to find a specific object in a sequence of images or videos. This task is performed automatically by human beings, even in particularly complex scenarios. For machines, it represents the challenges of the future. In convolutional neural networks, the fields of different neurons are partially overlapped so that they cover the entire field of view altogether. The response of a single neuron to stimuli taking place in its receptive field can be mathematically approximated by a convolution operation. A CNN is a particular type of ANN, which used above all other models for the analysis of images and the application of graphic filters. CNNs eliminate the need for the manual extraction of features, as these are learned directly from CNN. They produce state-of-the-art recognition results...
Keras 2.x Projects
By :
Keras 2.x Projects
By:
Overview of this book
Keras 2.x Projects explains how to leverage the power of Keras to build and train state-of-the-art deep learning models through a series of practical projects that look at a range of real-world application areas.
To begin with, you will quickly set up a deep learning environment by installing the Keras library. Through each of the projects, you will explore and learn the advanced concepts of deep learning and will learn how to compute and run your deep learning models using the advanced offerings of Keras. You will train fully-connected multilayer networks, convolutional neural networks, recurrent neural networks, autoencoders and generative adversarial networks using real-world training datasets. The projects you will undertake are all based on real-world scenarios of all complexity levels, covering topics such as language recognition, stock volatility, energy consumption prediction, faster object classification for self-driving vehicles, and more.
By the end of this book, you will be well versed with deep learning and its implementation with Keras. You will have all the knowledge you need to train your own deep learning models to solve different kinds of problems.
Related Content you might be interested in
Current Title:
Keras 2.x Projects
No titles found
Table of Contents (13 chapters)
Preface
Free Chapter
Getting Started with Keras
Modeling Real Estate Using Regression Analysis
Heart Disease Classification with Neural Networks
Concrete Quality Prediction Using Deep Neural Networks
Fashion Article Recognition Using Convolutional Neural Networks
Movie Reviews Sentiment Analysis Using Recurrent Neural Networks
Stock Volatility Forecasting Using Long Short-Term Memory
Reconstruction of Handwritten Digit Images Using Autoencoders
Robot Control System Using Deep Reinforcement Learning
Reuters Newswire Topics Classifier in Keras
What is Next?
Other Books You May Enjoy
Customer Reviews