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.

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.
Table of Contents Introduction to Deep Learning What is a Neural Network Supervised Learning with Neural Networks Why is Deep Learning Taking Off? About this Course Optional: Heroes of Deep Learning (Geoffrey Hinton) Introduction to Deep Learning There are five courses in the Coursera Deep Learning Specialization.

Taking the Coursera Machine 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.
Assumes you have knowledge of Week 10.
Table of Contents Application Example: Photo OCR Photo OCR Problem Description and Pipeline Sliding Windows Getting Lots of Data and Artificial Data Ceiling Analysis: What Part of the Pipeline to Work on Next Lecture notes: Lecture18 Application Example: Photo OCR Photo OCR Problem Description and Pipeline Photo OCR (Object Character Recognition) is the task of trying to recognize objects, characters (words and digits) given an image.

Taking the Coursera Machine 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.
Assumes you have knowledge of Week 9.
Table of Contents Large Scale Machine Learning Gradient Descent with Large Datasets Learning With Large Datasets Stochastic Gradient Descent Mini-Batch Gradient Descent Stochastic Gradient Descent Convergence Advanced Topics Online Learning Map Reduce and Data Parallelism Lecture notes: Lecture17 Large Scale Machine Learning Gradient Descent with Large Datasets Learning With Large Datasets One of the best ways to get a high performance machine learning system is to supply a lot of data into a low bias (overfitting) learning algorithm.

Taking the Coursera Machine 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.
Assumes you have knowledge of Week 8.
Table of Contents Anomoly Detection Density Estimation Problem Motivation Gaussian Distribution Algorithm Building an Anomaly Detection System Developing and Evaluating an Anomaly Detection System Anomaly Detection vs. Supervised Learning Choosing What Features to Use Multivariate Gaussian Distribution Algorithm Reccomender Systems Predicting Movie Ratings Problem Forumulation Content Based Recommendations Collaborative Filtering Collaborative Filtering Algorithm Low Rank Matrix Factorization Vectorization: Low Rank Matrix Factorization Implementational Detail: Mean Normalization Lecture notes: Lecture15 Lecture16 Anomoly Detection Density Estimation Problem Motivation Imagine being a manufacturor of aircraft engines.

Taking the Coursera Machine 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.
Assumes you have knowledge of Week 7.
Table of Contents Unsupervised Learning Clustering Introduction K-Means Algorithm Optimization Objective Random Initialization Choosing the Number of Clusters Dimensionality Reduction Motivation Data Compression Visualization Principal Component Analysis Principal Component Analysis Problem Formulation Principal Component Analysis Algorithm Applying PCA Reconstruction from Compressed Representation Choosing the Number of Principal Components Advice for Applying PCA Lecture notes: Lecture13 Lecture14 Unsupervised Learning Clustering Introduction Unsupervised learning is the class of problem solving where when given a set of data with no labels, find structure in the dataset.

There are only four states of being, or identity. Awareness, I (self), Dream (other), Universe (all).
Awareness This is the state you are born into, the state of being when you are first conscious of external stimuli. As an entity with awareness, the only requirement is one can acknowledge receiving some form of flow, or energy.
Examples: A baby crying. An insect navigating around.
I (Self-Awareness) This is the state in which you begin to recognize one self.

Taking the Coursera Machine 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.
Assumes you have knowledge of Week 6.
Table of Contents Support Vector Machines Large Margin Classification Optimization Objective Large Margin Intuition Kernels Source Vector Machines (in Practice) Lecture notes: Lecture12 Support Vector Machines Large Margin Classification Optimization Objective We are simplifying the logistic regression cost function by converting the sigmoid function into two straight lines, as shown here:

Taking the Coursera Machine 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.
Assumes you have knowledge of Week 5.
Table of Contents Advice for Applying Machine Learning Evaluating a Learning Algorithm Evaluating a Hypothesis Model Selection and Train/Validation/Test Sets Diagnosing Bias versus Variance Regularization and Bias/Variance Learning Curves Deciding What to Do Next Machine Learning System Design Building a Spam Classifier Prioritizing What to Work On Error Analysis Machine Learning Practical Tips How to Handle Skewed Data When to Utilize Large Data Sets Lecture notes: Lecture10 Lecture11 Advice for Applying Machine Learning Evaluating a Learning Algorithm Evaluating a Hypothesis Once we have done some trouble shooting for errors in our predictions by:

Taking the Coursera Machine 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.
Assumes you have knowledge of Week 4.
Table of Contents Neural Networks: Learning Cost Function and Backpropagation Cost Function Backpropagation Algorithm Backpropagation Intuition Backpropagation in Practice Implementation Note: Unrolling Parameters Gradient Checking Random Initialization Putting it Together Application of Neural Networks Autonomous Driving Lecture notes: Lecture9 Neural Networks: Learning Cost Function and Backpropagation Cost Function Let’s define a few variables that we will need to use.