Machine Learning II - Neural Networks from Scratch [Python]

Learn neural networks (and back-propagation) theory and implementation in Python

Expected learning & outcomes

  • Neural neural networks theory
  • Neural networks implementation
  • Loss functions
  • Gradient descent and back-propagation algorithms

Course details

Topic: Neural Networks

Duration: 2hr 6min

Level: Any Level

Created by: Holczer Balazs

Language: English

Provider: Udemy

About this course

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.

Section 1:

  • what are feed-forward neural networks

  • modeling the human brain

  • the big picture behind  neural networks

Section 2:

  • 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!

Who this course is for:

  • Beginner Python developers curious about data science
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