4.50 (30 reviews)
☑ Hopfield neural networks theory
☑ Hopfield neural network implementation in Python
☑ Neural neural networks theory
☑ Neural networks implementation
☑ Loss functions
☑ Gradient descent and back-propagation algorithms
This course is about artificial neural networks. Artificial intelligence and machine learning are getting more and more popular nowadays. In the beginning, other techniques such as Support Vector Machines outperformed neural networks, but in the 21st century neural networks again gain popularity. In spite of the slow training procedure, neural networks can be very powerful. Applications ranges from regression problems to optical character recognition and face detection.
what are Hopfield neural networks
modeling the human brain
the big picture behind Hopfield neural networks
Hopfield neural networks implementation
auto-associative memory with Hopfield neural networks
what are feed-forward neural networks
modeling the human brain
the big picture behind neural networks
feed-forward neural networks implementation
gradient descent with back-propagation
In the first part of the course you will learn about the theoretical background of neural networks, later you will learn how to implement them in Python from scratch.
If you are keen on learning machine learning methods, let's get started!
Artificial Intelligence Basics
Why to learn artificial intelligence and machine learning?
Types of artificial intelligence learning methods
Neural Networks With Backpropagation Theory
Artificial neural networks - inspiration
Artificial neural networks - layers
Artificial neural networks - the model
Why to use activation functions?
Neural networks - the big picture
Using bias nodes in the neural network
How to measure the error of the network?
Optimization with gradient descent
Gradient descent with backpropagation
Single Perceptron Model
Perceptron model training
Perceptron model implementation I
Perceptron model implementation II
Trying to solve XOR problem
Conclusion: linearity and hidden layers
Backpropagation implementation I
Backpropagation implementation II
Backpropagation implementation III
Backpropagation implementation IV
Backpropagation implementation V
Testing the Neural Network
Testing the network
Next steps in machine learning
Course Materials (DOWNLOADS)
The lecturer is great but I'm the problem. I have no background in this and it's hard to assimilate the info. That being said, let's continue.