Deep-Learning-Specialization
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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
- 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
Programming assignment
- 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
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
- Week 1 - Practical aspects of deep learning
- Week 2 - Optimization algorithms
- Week 3 - Hyperparameter tuning, Batch Normalization, Programming Frameworks
Programming assignment
- Week 1 Gradient Checking
- Week 1 initialization
- Week 1 Regularization
- Week 2 Optimization Methods
- Week 3 TensorFlow Tutorial
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
- Week 1 - Bird recognition in the city of Peacetopia (case study)
- Week 2 - Autonomous driving (case study).md
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
- Week 1 - The basics of ConvNets
- Week 2 - Deep convolutional models
- Week 3 - Detection algorithms
- Week 4 - Special applications: Face recognition & Neural style transfer
Programming excercise
- 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
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
- Week 1 - Recurrent Neural Networks
- Week 2 - Natural Language Processing & Word Embeddings
- Week 3 - Sequence models & Attention mechanism
Programming assignment
- 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
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:
