Deep Learning Specialization courses by Andrew Ng, deeplearning.ai
This is the repository for my implementations on the Deep Learning Specialization from Coursera.
Taught by Andrew Ng
Foundations of Deep Learning: * Understand the major technology trends driving Deep Learning * Be able to build, train and apply fully connected deep neural networks * Know how to implement efficient (vectorized) neural networks * Understand the key parameters in a neural network's architecture
Codes: * Week2: Neural Network Basics * Week3: Shallow Neural Network Implementation * Week4: Deep Neural Network Implementation * Mathematical demonstration: Backpropagation * Mathematical demonstration: Cross-entropy & Softmax gradients
Codes: * Week1: Initialization, Regularization & Gradient Check * Week2: Optimization Algorithms * Week3: Hyperparameter tuning, Batch Normalization & Tensorflow Implementation * Mathematical demostration: Batch Normalization Gradient * Paper: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
Codes: * Week1: Convolutional Neural Network Implementation in Numpy * Week2: * Keras CNN Implementation * ResNet Keras Implementation * Paper: Deep Residual Learning for Image Recognition * Week3: * YOLO Implementation(You Only Look Once) * Paper: You Only Look Once: Unified, Real-Time Object Detection * Paper: YOLO9000: Better, Faster, Stronger * Week4: * Neural Style Transfer * Paper: A Neural Algorithm of Artistic Style * Face Recognition * Paper: FaceNet: A Unified Embedding for Face Recognition and Clustering * Paper: Going deeper with convolutions (Inception Networks)
Codes: * Week1: * RNN & LSTM Implementation in Numpy (Including backpropagation) * Mathematical demonstration: RNN gradient through time * Mathematical demonstration: LSTM gradient through time * Mathematical demonstration: GRU gradient through time * Paper: Vanishing/Exploding gradient & Clipping * Character-Level Language Modeling * Sequence Sampling Generation LSTM * Week2: * Natural Language Processing & Word Embeddings * Operations on word vectors - Debiasing * Week3: * Neural Machine Translation with Attention * Trigger word detection