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Hopfield networks, neural networks, gradient descent and backpropagation algorithms explained step by step
What you'll learn:- Basics of neural networks
- Hopfield networks
- Concrete implementation of neural networks
- Backpropagation
- Optical character recognition
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 21th 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.
Section 1:
what are neural networks
modeling the human brain
the big picture
Section 2:
Hopfield neural networks
how to construct an autoassociative memory with neural networks
Section 3:
what is back-propagation
feedforward neural networks
optimizing the cost function
error calculation
backpropagation and gradient descent
Section 4:
the single perceptron model
solving linear classification problems
logical operators (AND and XOR operation)
Section 5:
applications of neural networks
clustering
classification (Iris-dataset)
optical character recognition (OCR)
smile-detector application from scratch
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.
If you are keen on learning methods, let's get started!
Overview
