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)

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

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?

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?

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?

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.

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 Neural Networks and Deep Learning.
Table of Contents Practical Aspects of Deep Learning Setting Up Your Machine Learning Application Train/Dev/Test Sets Bias/Variance Basic Recipe for Machine Learning Regularizing your Neural Network Regularization Why regularization reduces overfitting?

Taking the Coursera Deep Learning Specialization, Neural Networks and Deep Learning 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 Week 3.
Table of Contents Deep Neural Networks Deep Neural Network Deep L-layer neural network Forward Propagation in a Deep Network Getting your matrix dimensions right Why deep representations?

Taking the Coursera Deep Learning Specialization, Neural Networks and Deep Learning 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 Week 2.
Table of Contents Shallow Neural Networks Shallow Neural Network Neural Networks Overview Neural Network Representation Computing a Neural Network’s Output Vectorizing Across Multiple Examples Explanation for Vectorized Implementation Activation Functions Why do you need non-linear activation functions?

Taking the Coursera Deep Learning Specialization, Neural Networks and Deep Learning 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 Week 1.
Table of Contents Neural Networks Basics Logistic Regression as a Neural Network Binary Classification Logistic Regression Logistic Regression Cost Function Gradient Descent Derivatives More Derivatives Examples Computation Graph Derivatives with a Computation Graph Logistic Regression Gradient Descent Gradient Descent on m Examples Python and Vectorization Vectorization More Vectorization Examples Vectorizing Logistic Regression Vectorizing Logistic Regression’s Gradient Output Broadcasting in Python Note on Python/NumPy Vectors Neural Networks Basics Logistic Regression as a Neural Network Binary Classification Binary classification is basically answering a yes or no question.