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This repository contains the programming assignments and slides from the deep learning course from coursera offered by deeplearning.ai

Deep learning course offered by deeplearning.ai at coursera

1. Neural Networks and Deep Learning

Quizes

Programming assignment

Lectures + my notes

  • Week 1 --> Introduction, NN, Why Deep learning
  • Week 2 --> Logistic regression, Gradient Descent, Derivatives, Vectorization, Python Broadcasting
  • Week 3 --> NN, Activation function, Backpropagate, Random Initialization
  • Week 4 --> Deep L-layer Neural network, Matrix dimension right, Why Deep representation, Building blocks of NN, Parameters vs Hyperparameters, Relationship with brain

2. Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization

Quizes

Programming assignment

Lectures + My notes

  • Week 1 --> Train/Dev/Test set, Bias/Variance, Regularization, Why regularization, Dropout, Normalizing inputs, vanishing/exploding gradients, Gradient checking
  • Week 2 --> Mini-batch, Exponentially weighted average, GD with momentum, RMSProp, Adam optimizer, Learning rate decay, Local optima problem, Plateaus problem
  • Week 3 --> Tuning process, Picking hyperparameter, Normalizing activations, Softmax regression, Deep learning programming framework

3. Structuring Machine Learning Projects

Quizes

Lectures + my notes

  • Week 1 --> Why ML Strategy?, Orthogonalization, Single number evaluation metric, Satisficing and optimizing metrics, Train/dev/test distribution, Human level performance, Avoidable bias
  • Week 2 --> Error analysis, Incorrectly labeled data, Data augmentation, Transfer learning, Multitask learning, End-to-End Deep learning

4. Convolutional Neural Networks

Quizes

Programming excercise

Lectures + My notes

  • Week 1 --> Computer vision, Edge detection, Padding, Strided convolution, Convolutions over volume, Pooling layers, CNN, Why CNN?
  • Week 2 --> LeNet-5, AlexNet, VGG-16, ResNets, 1x1 convolutions, InceptionNet, Data augmentation
  • Week 3 --> Object localization, Landmark detection, Object detection, Sliding window, Bounding box prediction, Intersection over union(IOU), Non-max suppression, Anchor box, YOLO algorithm
  • Week 4 --> Face recognition, One-shot learning, Siamese network, Neural style transfer

5. Sequence Models

Quizes

Programming assignment

Lectures + My notes

  • Week 1 --> RNN, Notation, Vanishing gradient, GRU, LSTM, Bidirectional RNN, Deep RNN
  • Week 2 --> Word representation, Word embedding, Cosine similarity, Word2Vec, Negetive sampling, GloVe words, Debiasing word
  • Week 3 --> Beam search, Error analysis in Beam search, Bleu score, Attention model, Speech recognition

Specialization certificate:

Certificate