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Sequence Models, Week 3

Taking the Coursera Deep Learning Specialization, Sequence Models course. Will post condensed notes every week as part of the review process. All material originates from the free Coursera course, taught by Andrew Ng. See deeplearning.ai for more details. Table of Contents Sequence models & Attention mechanism Various sequence to sequence architectures Basic Models Picking the most likely sentence Beam search Refinements to Beam Search Error analysis in Beam Search Bleu Score Attention Model Intuition Attention Model Speech recognition - Audio data Speech Recognition Trigger Word Detection Conclusion Sequence models & Attention mechanism Sequence models can have an attention mechanism.

Sequence Models, Week 2

Taking the Coursera Deep Learning Specialization, Sequence Models course. Will post condensed notes every week as part of the review process. All material originates from the free Coursera course, taught by Andrew Ng. See deeplearning.ai for more details. Table of Contents Natural Langauge Processing & Word Embeddings Introduction to Word Embeddings Word Representation Using word embeddings Properties of word embeddings Embedding matrix Learning Word Embeddings: Word2vec & GloVe Learning word embeddings Word2Vec Negative Sampling GloVe word vectors Applications using Word Embeddings Sentiment Classification Debiasing word embeddings Natural Langauge Processing & Word Embeddings Learn about how to use deep learning for natraul language processing.

Sequence Models, Week 1

Taking the Coursera Deep Learning Specialization, Sequence Models course. Will post condensed notes every week as part of the review process. All material originates from the free Coursera course, taught by Andrew Ng. See deeplearning.ai for more details. Table of Contents Recurrent Neural Networks Recurrent Neural Networks Why sequence models Notation Recurrent Neural Network Model Backpropagation through time Different types of RNNs Language model and sequence generation Sampling novel sequences Vanishing gradients with RNNs Gated Recurrent Unit (GRU) Long Short Term Memory (LSTM) Bidirectional RNN Deep RNNs Recurrent Neural Networks Learn about recurrent neural networks.

Convolutional Neural Networks, Week 4

Taking the Coursera Deep Learning Specialization, Convolutional Neural Networks course. Will post condensed notes every week as part of the review process. All material originates from the free Coursera course, taught by Andrew Ng. See deeplearning.ai for more details. Table of Contents Special Applications: Face Recognition & Neural Style Transfer Face Recognition What is face recognition? One Shot Learning Siamese Network Triplet Loss Face Verification and Binary Classification Neural Style Transfer What is neural style transfer?

Convolutional Neural Networks, Week 3

Taking the Coursera Deep Learning Specialization, Convolutional Neural Networks course. Will post condensed notes every week as part of the review process. All material originates from the free Coursera course, taught by Andrew Ng. See deeplearning.ai for more details. Table of Contents Object Detection Learning Objectives Detection Algorithms Object Localization Landmark Detection Object Detection Convolutional Implementation of Sliding Windows Bounding Box Predictions Intersection Over Union Non-max Suppression Anchor Boxes YOLO Algorithm (Optional) Region Proposals Object Detection Learning Objectives Understand the challenges of Object Localization, Object Detection, Landmark Finding Understand and implement non-max suppression Understand and implement intersection over union Understand how to label a dataset for an object detection application Remember the vocabulary of object detection (landmark, anchor, bounding box, grid) Detection Algorithms Object Localization Image classification: One object (Is cat or no cat)

Convolutional Neural Networks, Week 2

Taking the Coursera Deep Learning Specialization, Convolutional Neural Networks course. Will post condensed notes every week as part of the review process. All material originates from the free Coursera course, taught by Andrew Ng. See deeplearning.ai for more details. Table of Contents Deep Convolutional Models: Case Studies Learning Objectives Case Studies Why look at case studies Classic Networks Residual Networks (ResNets) Why ResNets Work Networks in Networks and 1x1 Convolutions Inception Network Motivation Inception Network Practical Advices for using ConvNets Using Open-Source Implementation Transfer Learning Data Augmentation State of Computer Vision Deep Convolutional Models: Case Studies Learning Objectives Understand foundational papers of Convolutional Neural Networks (CNN) Analyze dymensionality reduction of a volume in a very deep network Understand and implement a residual network Build a deep neural network using Keras Implement skip-connection in your network Clone a repository from Github and use transfer learning Case Studies Why look at case studies Good way to gain intuition about convolutional neural networks is to read existing architectures that utilize CNNs

Convolutional Neural Networks, Week 1

Taking the Coursera Deep Learning Specialization, Convolutional Neural Networks course. Will post condensed notes every week as part of the review process. All material originates from the free Coursera course, taught by Andrew Ng. See deeplearning.ai for more details. Table of Contents Foundations of Convolutional Neural Networks Convolutional Neural Networks Computer Vision Edge Detection Example More Edge Detection Padding Strided Convolutions Convolutions Over Volume One Layer of a Convolutional Network Simple Convolutional Network Example Pooling Layers CNN Example Why Convolutions?

Structuring Machine Learning Projects, Week 1

Taking the Coursera Deep Learning Specialization, Structuring Machine Learning Projects course. Will post condensed notes every week as part of the review process. All material originates from the free Coursera course, taught by Andrew Ng. See deeplearning.ai for more details. Table of Contents ML Strategy Introduction to ML Strategy Why ML Strategy Orthogonalization Setting Up Your Goal Single Number Evaluation Metric Satisficing and Optimizing Metric Train/Dev/Test Distributions Size of the Dev and Test Sets When to Change Dev/Test Sets and Metrics Comparing to Human-Level Performance Why Human-level Performance?

Improving Deep Neural Networks, Week 3

Taking the Coursera Deep Learning Specialization, Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization course. Will post condensed notes every week as part of the review process. All material originates from the free Coursera course, taught by Andrew Ng. See deeplearning.ai for more details. Assumes you have knowledge of Improving Deep Neural Networks, Week 2. Table of Contents Hyperparameter Tuning, Batch Normalization, and Programming Frameworks Hyperparameter Tuning Tuning Process Using an appropriate scale to pick hyperparameters Hyperparameters tuning in practice: Pandas vs Caviar Batch Normalization Normalizing activations in a network Fitting Batch Normalization into a neural network Why does Batch Normalization Work?

Improving Deep Neural Networks, Week 2

Taking the Coursera Deep Learning Specialization, Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization course. Will post condensed notes every week as part of the review process. All material originates from the free Coursera course, taught by Andrew Ng. See deeplearning.ai for more details. Assumes you have knowledge of Improving Deep Neural Networks, Week 1. Table of Contents Optimization Algorithms Mini-Batch Gradient Descent Understanding Mini-batch Gradient Descent Exponentially Weighted Averages Understanding Exponentially Weighted Averages Bias Correction in Exponentially Weighted Averages Gradient Descent with Momentum RMSprop Adam Optimization Algorithm Learning Rate Decay The Problem of Local Optima Optimization Algorithms Mini-Batch Gradient Descent Rather than training on your entire training set during each step of gradient descent, break out your examples into groups.