https://github.com/aitamilnadu/nn
Neural Network Best Resources
https://github.com/aitamilnadu/nn
cnn gan keras neural-network neural-network-tutorials neural-networks nn pytorch rnn tensorflow
Last synced: 4 months ago
JSON representation
Neural Network Best Resources
- Host: GitHub
- URL: https://github.com/aitamilnadu/nn
- Owner: aitamilnadu
- Created: 2018-09-08T14:02:32.000Z (almost 7 years ago)
- Default Branch: master
- Last Pushed: 2019-10-24T18:16:49.000Z (over 5 years ago)
- Last Synced: 2025-01-18T06:42:13.711Z (6 months ago)
- Topics: cnn, gan, keras, neural-network, neural-network-tutorials, neural-networks, nn, pytorch, rnn, tensorflow
- Language: Jupyter Notebook
- Homepage:
- Size: 16.6 KB
- Stars: 3
- Watchers: 1
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Neural Networks & Deep Learning
## YouTube Tutorials
### How Deep Neural Networks Work by Brandon Rohrer
https://www.youtube.com/watch?v=ILsA4nyG7I0
https://brohrer.github.io/how_neural_networks_work.html
### A friendly introduction to Deep Learning and Neural Networks by Luis Serrano
https://www.youtube.com/watch?v=BR9h47Jtqyw
### Visual Introduction to Neural Network by [3Blue1Brown](https://www.youtube.com/channel/UCYO_jab_esuFRV4b17AJtAw)
https://www.youtube.com/playlist?list=PLZHQObOWTQDNU6R1_67000Dx_ZCJB-3pi
### Deep Learning Demystified
https://www.youtube.com/watch?v=Q9Z20HCPnww
http://brohrer.github.io/deep_learning_demystified.html
## Courses
### How Deep Neural Networks Work by Brandon Rohrer
A conceptual overview of neural networks, the workhorse of artificial intelligence
https://end-to-end-machine-learning.teachable.com
### Stanford Winter Quarter 2016 class: CS231n: Convolutional Neural Networks for Visual Recognition by Andrej Karpathy
https://www.youtube.com/playlist?list=PLkt2uSq6rBVctENoVBg1TpCC7OQi31AlC
Latest Course : http://cs231n.stanford.edu/
# Machine Learning
## A Friendly Introduction to Machine Learning by Luis Serrano
https://www.youtube.com/watch?v=IpGxLWOIZy4
This video explains
* What is Machine Learning? Humans learn from past experiences, computers learn from previous data.
* Linear Regression: Finding the line that works best between a given set of points.
* Gradient Descent : Square of error minimization to get best line fit
* Detecting Spam e-mails with Naive Bayes Algorithm
* Decision Tree
* Logistic Regression
* Neural network as a logistic regression set intersection
* Support Vector Machine with linear optimization
* Kernel trick: planes for curves and vice-versa
* K-Means clustering
* Hierarchical Clustering
* Summary
## Machine Learning Cheatsheet
Brief visual explanations of machine learning concepts with diagrams, code examples and links to resources for learning more.
https://ml-cheatsheet.readthedocs.io/en/latest/