coding-blocks-archives/Perceptron_Summer_2017

Name: Perceptron_Summer_2017

Owner: Coding Blocks Archives

Description: null

Created: 2017-06-12 07:45:49.0

Updated: 2018-04-11 08:24:49.0

Pushed: 2017-08-10 10:53:53.0

Homepage: null

Size: 30695

Language: Jupyter Notebook

GitHub Committers

UserMost Recent Commit# Commits

Other Committers

UserEmailMost Recent Commit# Commits

README

Perceptron Summer 2017 (Machine Learning Course)

Coding Blocks, Pitampura.

Contents
  1. Class_01: Introduction to Python and Machine Learning
  2. Class_02: K-Nearest Neighbours
  3. Class_03: Face Recognition with KNN
  4. Class_04: K-Means clustering and Most Dominant Color extraction
  5. Class_05: Decision Trees and Random Forests
  6. Class_06: Principal Component Analysis
  7. Class_07: Linear Regression
  8. Class_08: NeuralNets w/ Keras
  9. Class_09: Neural Nets (numpy), MNIST classification, AutoEncoder (stacked, simple)
  10. Class_11: ConvNets and Conv Auto Encoders
  11. Class_13: Transfer Learning, Differential Evolution, Genetic Algorithm
  12. Class_14: Markov chains, intro to RNN
  13. Class_15: RNN for Addition, LSTM for image generation
  14. Class_16: Deep Dream and Neural Art
  15. Class_17: Naive Bayes and SVM
  16. Class_18: Word2Vec and Scraping
  17. Class_19: Attention mechanism
  18. Class_20: Simple RL and Q-leanring
  19. Class_21: Deep Q-learning and Sentiment Analysis
  20. Class_22: Generative Adversarial Networks
This work is supported by the National Institutes of Health's National Center for Advancing Translational Sciences, Grant Number U24TR002306. This work is solely the responsibility of the creators and does not necessarily represent the official views of the National Institutes of Health.