Deep Learning Notes
Notes taken during the Deep Learning Specialization
by Andrew Ng & Eddy Shyu
Stanford University & DeepLearning.AI
What I Built
| # | Course | Focus |
|---|---|---|
| 1 | Neural Networks and Deep Learning | Logistic regression as a neural network, shallow & deep networks, forward/backpropagation |
| 2 | Improving Deep Neural Networks | Hyperparameter tuning, regularization (Dropout, BatchNorm), optimization (Momentum, Adam), Xavier/He initialization |
| 3 | Structuring Machine Learning Projects | Train/dev/test splits, error analysis, transfer learning, end-to-end deep learning |
| 4 | Convolutional Neural Networks | CNNs, edge detection, classic & modern architectures (LeNet, AlexNet, VGG, ResNet, Inception, MobileNet, EfficientNet), object detection (YOLO), face recognition (FaceNet, Siamese), neural style transfer, U-Net |
| 5 | Sequence Models | RNNs, GRUs, LSTMs, word embeddings (Word2Vec, GloVe), attention models, Transformers, speech recognition, music synthesis, machine translation, chatbots |
— emreaslan —