Welcome to the repository for my submissions, notes, and related code for the specialization. The specialization consists of five courses. Completion certificate can be viewed here.
Course 1: Neural Networks and Deep Learning
Certificate: here.
Programming assignments
- Week 1 (No programming assignments)
- Week 2 - PA 1 - Python Basics with Numpy
- Week 2 - PA 2 - Logistic Regression with a Neural Network mindset
- Week 3 - PA 3 - Planar data classification with one hidden layer
- Week 4 - PA 4 - Building your Deep Neural Network: Step by Step
- Week 4 - PA 5 - Deep Neural Network for Image Classification: Application
Topics covered
- Week 1: Introduction, NN, Why Deep learning
- Week 2: Logistic regression, Gradient Descent, Derivatives, Vectorization, Python Broadcasting
- Week 3: NN, Activation function, Backpropagation, Random Initialization
- Week 4: Deep L-layer Neural network, Matrix dimension verfication, Why Deep representation, Building blocks of NN, Parameters vs Hyperparameters, Relationship with brain
Course 2: Improving Deep Neural Networks
Certificate: here.
Programming assignments
- Week 1 - PA 1 - Initialization
- Week 1 - PA 2 - Regularization
- Week 1 - PA 3 - Gradient Checking
- Week 2 - PA 4 - Optimization Methods
- Week 3 - PA 5 - TensorFlow Tutorial
Topics covered
- 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, Hyperparameter selection, Batch Normalization, Softmax regression, Deep learning programming framework
Course 3: Structuring Machine Learning Projects
Certificate: here.
Programming assignments: No programming assignment.
Topics covered
- 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
Course 4: Convolutional Neural Networks
Certificate: here.
Programming assignments
- Week 1 - PA 1 - Convolutional Model: step by step
- Week 1 - PA 2 - Convolutional Neural Networks: Application
- Week 2 - PA 3 - Residual Networks
- Week 2 - PA 4 - Transfer Learning with MobileNet
- Week 3 - PA 5 - Car detection with YOLO for Autonomous Driving
- Week 3 - PA 6 - Image Segmentation Unet
- Week 4 - PA 7 - Face Recognition
- Week 4 - PA 8 - Art Generation with Neural Style Transfer
Topics covered
- 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
Course 5: Sequence Models
Certificate: here.
Programming assignments
- Week 1 - PA 1 - Building a Recurrent Neural Network - Step by Step
- Week 1 - PA 2 - Dinosaur Land -- Character-level Language Modeling
- Week 1 - PA 3 - Jazz improvisation with LSTM
- Week 2 - PA 4 - Word Vector Representation and Debiasing
- Week 2 - PA 5 - Emojify!
- Week 3 - PA 6 - Neural Machine Translation with Attention
- Week 3 - PA 7 - Trigger Word Detection
- Week 4 - PA 8 - Transformer Implementation
- Week 4 - PA 9 - Transformer Application: Named Entity Recognition
- Week 4 - PA 10 - Transformer Application: Question Answering
- Week 4 - PA 11 - Transformer Application: Transformer Preprocessing
Topics covered
- Week 1: RNN, Notation, BPTT, RNN-variants, Vanishing gradient, GRU, LSTM, Bidirectional RNN, Deep RNN
- Week 2: Word representation, Word embedding, Cosine similarity, Word2Vec, Negetive sampling, GloVe word vectors, Debiasing word
- Week 3: Beam search, Error analysis in Beam search, Bleu score, Attention model, Speech recognition
- Week 4: Transformer Intution, Self Attention, Multi-head Attention, Transformers