Deep learning course offered by deeplearning.ai at coursera
- Week 1 Quiz - Introduction to deep learning
- Week 2 Quiz - Network Basics.md
- Week 3 Quiz - Shallow Neural Networks
- Week 4 Quiz - Key concepts on Deep Neural Networks
- Week 1 --> No programming assignment
- Week 2 - Logistic Regression with a Neural Network mindset
- Week 3 - Planar data classification with one hidden layer
- Week 4 - Building your Deep Neural Network - Step by Step
- Week 4 - Deep Neural Network for Image Classification: Application
- 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
- Week 1 - Practical aspects of deep learning
- Week 2 - Optimization algorithms
- Week 3 - Hyperparameter tuning, Batch Normalization, Programming Frameworks
- Week 1 Gradient Checking
- Week 1 initialization
- Week 1 Regularization
- Week 2 Optimization Methods
- Week 3 TensorFlow Tutorial
- 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
- Week 1 - Bird recognition in the city of Peacetopia (case study)
- Week 2 - Autonomous driving (case study).md
- 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
- Week 1 - The basics of ConvNets
- Week 2 - Deep convolutional models
- Week 3 - Detection algorithms
- Week 4 - Special applications: Face recognition & Neural style transfer
- Week 1 - Convolutional Neural Networks: Application
- Week 2 - Keras
- Week 2 - ResNets
- Week 3 - Car detection for Autonomous Driving
- Week 4 - Face Recognition
- Week 4 - Neural Style Transfer
- 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
- Week 1 - Recurrent Neural Networks
- Week 2 - Natural Language Processing & Word Embeddings
- Week 3 - Sequence models & Attention mechanism
- Week 1 - Building a Recurrent Neural Network - Step by Step
- Week 1 - Character level language model - Dinosaurus Island
- Week 1 - Improvise a Jazz Solo with an LSTM Network
- Week 2 - Word Vector Representation
- Week 2 - Emojify
- Week 3 - Machine Translation
- Week 3 - Trigger word detection
- 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
