Self-organizing maps (SOMs) have been proposed by Willshaw and Von Der Malsburg (Willshaw D. J., Von Der Malsburg C., How patterned neural connections can be set up by self-organization, Proceedings of the Royal Society of London, B/194, N. 1117) to model different neurobiological phenomena observed in animals. In particular, they discovered that some areas of the brain develop structures with different areas, each of them with a high sensitivity for a specific input pattern. The process behind such a behavior is quite different from what we have discussed up until now, because it's based on competition among neural units based on a principle called winner-takes-all. During the training period, all the units are excited with the same signal, but only one will produce the highest response. This unit is automatically candidate to become the receptive basin for that specific pattern. The particular model we are going to present has been introduced by Kohonen (in the paper...
Python: Advanced Guide to Artificial Intelligence
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Python: Advanced Guide to Artificial Intelligence
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Overview of this book
This Learning Path is your complete guide to quickly getting to grips with popular machine learning algorithms. You'll be introduced to the most widely used algorithms in supervised, unsupervised, and semi-supervised machine learning, and learn how to use them in the best possible manner. Ranging from Bayesian models to the MCMC algorithm to Hidden Markov models, this Learning Path will teach you how to extract features from your dataset and perform dimensionality reduction by making use of Python-based libraries.
You'll bring the use of TensorFlow and Keras to build deep learning models, using concepts such as transfer learning, generative adversarial networks, and deep reinforcement learning. Next, you'll learn the advanced features of TensorFlow1.x, such as distributed TensorFlow with TF clusters, deploy production models with TensorFlow Serving. You'll implement different techniques related to object classification, object detection, image segmentation, and more.
By the end of this Learning Path, you'll have obtained in-depth knowledge of TensorFlow, making you the go-to person for solving artificial intelligence problems
This Learning Path includes content from the following Packt products:
• Mastering Machine Learning Algorithms by Giuseppe Bonaccorso
• Mastering TensorFlow 1.x by Armando Fandango
• Deep Learning for Computer Vision by Rajalingappaa Shanmugamani
Table of Contents (31 chapters)
Title Page
About Packt
Contributors
Preface
Free Chapter
Machine Learning Model Fundamentals
Introduction to Semi-Supervised Learning
Graph-Based Semi-Supervised Learning
Bayesian Networks and Hidden Markov Models
EM Algorithm and Applications
Hebbian Learning and Self-Organizing Maps
Clustering Algorithms
Advanced Neural Models
Classical Machine Learning with TensorFlow
Neural Networks and MLP with TensorFlow and Keras
RNN with TensorFlow and Keras
CNN with TensorFlow and Keras
Autoencoder with TensorFlow and Keras
TensorFlow Models in Production with TF Serving
Deep Reinforcement Learning
Generative Adversarial Networks
Distributed Models with TensorFlow Clusters
Debugging TensorFlow Models
Tensor Processing Units
Getting Started
Image Classification
Image Retrieval
Object Detection
Semantic Segmentation
Similarity Learning
Other Books You May Enjoy
Index
Customer Reviews