fundamentals_of_machine_learning.tar.gz

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The machine learning field is rapidly advancing today due to the availability of large datasets and the ability to process big data efficiently. Moreover, several new techniques have produced groundbreaking results for standard machine learning problems.

This course provides a detailed description of different machine learning algorithms and techniques, including regression, deep learning, reinforcement learning, Bayes nets, support vector machines (SVMs), and decision trees. The course also offers sufficient mathematical details for a deeper understanding of how different techniques work. An overview of the Python programming language and the fundamental theoretical aspects of ML, including probability theory and optimization, is also included. The course contains several practical coding exercises as well.

By the end of the course, you will have a deep understanding of different machine-learning methods and the ability to choose the right method for different applications.

Mastering Machine Learning Theory and Practice

## Programming derivatives ##
Calculating the gradients for a multilayer perceptron of simple sigmoid units has already been shown to be a bit cumbersome, so we might be worried about how more elaborate networks could be implemented.


# Programming derivatives ##
Calculating the gradients for a multilayer perceptron of simple sigmoid units has already been shown to be a bit cumbersome, so we might be worried about how more elaborate networks could be implemented.


Learn about automatic differentiation and how it can be programmed.

Getting started

Introduction

Scientific Programming with Python

Machine Learning with Sklearn

Neural Networks and Keras

Regression and Optimization

Basic Probability Theory

Probabilistic Regression and Bayes Nets

Generative Models

Cyclic Models and Recurrent Neural Networks

Reinforcement Learning

Artificial intelligence, the Brain, and Our Society

Conclusion

Automatic Differentiation

Programming derivatives