UnitsΒΆ
- Introduction to Deep Learning
- Probability and Statistics
- Gradients, Chain Rule, Automatic Differentiation
- Linear Regression, Basic Optimization
- Likelihood, Loss Functions, Logisitic Regression, Information Theory
- Multilayer Perceptron
- Model Selection, Weight Decay, Dropout
- Numerical Stability, Hardware
- Machine Learning Problems and Statistical Environment
- Layers, Parameters, GPUs
- Convolutional Networks
- Basic Convolutional Networks
- Residual Networks and Advanced Architectures
- Computation Performance, Multi-GPU and Multi-Machine Training
- Image Augmentation, Fine Turning, Neural Style
- Object Detection
- Sequence Models and Language
- Recurrent Neural Networks
- Advanced Sequence Models
- Word2vec, FastText, GloVe, Sentiment Analysis
- Encoder-Decoder, Seq2seq, Machine Translation
- Attention, Transformer, BERT
- Convex Optimization, Convergence Rate
- Momentum, AdaGrad, RMSProp, Adam