The multi-layer perceptron is a simple ANN. Its name, however, is a misnomer. A multi-layer perceptron is not a single perceptron with multiple layers, but rather multiple layers of artificial neurons that resemble perceptrons. Multi-layer perceptrons have three or more layers of artificial neurons that form a directed, acyclic graph. Generally, each layer is fully connected to the subsequent layer; the output, or activation, of each artificial neuron in a layer is an input to every artificial neuron in the next layer. Features are input through the Input layer. The simple neurons in the input layer are connected to at least one Hidden layer. Hidden layers represents latent variables; these cannot be observed in the training data. The hidden neurons in these layers are often called hidden units. Finally, the last hidden layer is connected...
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Mastering Machine Learning with scikit-learn
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Mastering Machine Learning with scikit-learn
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Overview of this book
Machine learning is the buzzword bringing computer science and statistics together to build smart and efficient models. Using powerful algorithms and techniques offered by machine learning you can automate any analytical model.
This book examines a variety of machine learning models including popular machine learning algorithms such as k-nearest neighbors, logistic regression, naive Bayes, k-means, decision trees, and artificial neural networks. It discusses data preprocessing, hyperparameter optimization, and ensemble methods. You will build systems that classify documents, recognize images, detect ads, and more. You will learn to use scikit-learn’s API to extract features from categorical variables, text and images; evaluate model performance, and develop an intuition for how to improve your model’s performance.
By the end of this book, you will master all required concepts of scikit-learn to build efficient models at work to carry out advanced tasks with the practical approach.
Table of Contents (15 chapters)
Preface
Free Chapter
The Fundamentals of Machine Learning
Simple Linear Regression
Classification and Regression with k-Nearest Neighbors
Feature Extraction
From Simple Linear Regression to Multiple Linear Regression
From Linear Regression to Logistic Regression
Naive Bayes
Nonlinear Classification and Regression with Decision Trees
From Decision Trees to Random Forests and Other Ensemble Methods
The Perceptron
From the Perceptron to Support Vector Machines
From the Perceptron to Artificial Neural Networks
K-means
Dimensionality Reduction with Principal Component Analysis
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