Machine Learning™ - Neural Networks from Scratch [Python]
Video: .mp4 (1280x720, 30 fps(r)) | Audio: aac, 44100 Hz, 2ch | Size: 1.06 GB
Genre: eLearning Video | Duration: 39 lectures (3 hour, 30 mins) | Language: English
Learn Hopfield networks and neural networks (and back-propagation) theory and implementation in Python
What you'll learn
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
Requirements
Very basic Python
Description
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 Hopfield neural networks
modeling the human brain
the big picture behind Hopfield neural networks
Section 2:
Hopfield neural networks implementation
auto-associative memory with Hopfield neural networks
Section 3:
what are feed-forward neural networks
modeling the human brain
the big picture behind neural networks
Section 4:
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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