alessandrocorradini / university-of-toronto-neural-networks-for-machine-learning

Programming Assignments for the Neural Networks for Machine Learning Course on Coursera

Neural Networks for Machine Learning - University of Toronto

This repository contains all programming assignments solutions for the Neural Networks for Machine Learning course on Coursera taught by the legendary prof. and godfather of Ai Geoffrey Hinton.

Certificate of Completion

You can see the Certificate of Completion and other certificates in my Certificates Repo that contains all my certificates obtained through my journey as a self-made Data Science and better developer.

This course covers:

• The Perceptron learning procedure
• The backpropagation learning proccedure
• Learning feature vectors for words
• Object recognition with neural nets
• Optimization: How to make the learning go faster
• Recurrent neural networks
• More recurrent neural networks
• Ways to make neural networks generalize better
• Combining multiple neural networks to improve generalization
• Hopfield nets and Boltzmann machines
• Restricted Boltzmann machines (RBMs)
• Stacking RBMs to make Deep Belief Nets
• Deep neural nets with generative pre-training
• Modeling hierarchical structure with neural nets
• Recent applications of deep neural nets

⚠️ Disclaimer ⚠️

Please, don't fork or copy this repository.

The Neural Networks for Machine Learning course offered by University of Toronto, is a intermediate level course. Data Science is one of the hardest subfield of Computer Science and requires a lot of study and hard work.