"Neural Networks for All" Lectures from Sung Kim

Lectures

From the site: http://hunkim.github.io/ml/

Deep learning basics

Code: https://github.com/hunkim/DeepLearningZeroToAll

List of Videos

• About
• Basic derivative
• Lecture 1, Machine learning basics
  • Lab 1, TensorFlow basics
• Lecture 2, Linear Regression
  • Lab 2, Linear Regression
• Lecture 3, How to minimize cost
  • Lab 3, Minimizing cost
• Lecture 4, Multivariable linear regression
  • Lab 4-1, Multivariable linear regression
  • Lab 4-2, Loading Data from File
• Lecture 5-1, Logistic(regression) classification
  • Lecture 5-2, Logistic(regression) classification: cost function & gradient decent
  • Lab 5, Logistics(regression) classifier
• Lecture 6-1, Softmax classification: Multinomial classification
  • Lecture 6-2, (continue)
  • Lab 6-1, Softmax classifier
  • Lab 6-2, Fancy Softmax Classifier cross_entropy, one_hot, reshape
• Lecture 7-1, Application & Tips: Learning rate, data preprocessing, overfitting
  • Lecture 7-2, Application & Tips: Learning and test data sets
  • Lab 7-1, Learning rate, Evaluation
  • Lab 7-2, MNIST data
• Lecture 8-1, Deep Neural Nets for Everyone
  • Lecture 8-2, Deep Neural Nets for Everyone
  • Lab 8, Tensor Manipulation
• Lecture 9-1, Neural Nets(NN) for XOR
  • Lecture 9-2, Backpropagation
  • Lab 9-1, NN for XOR
  • Lab 9-2, Tensorboard for XOR NN
• Lecture 10-1, ReLU: Better non-linearity
  • Lecture 10-2, Initialize weights in a smart way
  • Lecture 10-3, NN dropout and model ensemble
  • Lecture 10-4, NN LEGO Play
  • Lab 10, NN, ReLU, Xavier, Dropout and Adam
• Lecture 11-1, CNN introduction
  • Lecture 11-2, CNN introduction: Max pooling and others
  • Lecture 11-3, CNN case study
  • Lab 11-1, CNN basics
  • Lab 11-2, CNN MNIST: 99%!
  • Lab 11-3, Class, Layers, Ensemble
• Lecture 12, RNN
  • Lab 12-1, RNN basics
  • Lab 12-2, Hi Hello RNN
  • Lab 12-3, RNN with long sequences
  • Lab 12-4, RNN with long sequences: Stacked RNN + Softmax layer
  • Lab 12-5, Dynamic RNN
  • Lab 12-6, RNN with time series data(stock)
• Bonus
  • Lab 13, TensorFlow GPU @AWS
  • Lab 14, TensorFlow GPU @AWS Spot Instances without losing data
  • Google Cloud ML Examples