Deep Learning Specialization with deeplearning.ai

Introduction

My name is Jerry Lin, and I am a graduate of General Assembly’s Data Science Immersive and a former chemistry professor with an interest in machine learning (ML) and natural language processing (NLP). To further hone my skills, I have enrolled in the Deep Learning Specialization offered through deeplearning.ai on Coursera.

These posts will serve as my notes to the course and may also contain my personal insights, examples, or outside research on the topic. I am not affiliated with deeplearning.ai, General Assembly, or any of their contributors. If the owners of the course content wish to have this taken down, please contact me.

The Deep Learning Specialization

The specialization is separated into five courses:

1. Neural Networks and Deep Learning
2. Improving Deep Neural Networks
3. Structuring Machine Learning Projects
4. Convolutional Neural Networks
5. Sequence Models

As I progress through the course, I will be writing and posting my notes on the material. They will follow a similar structure to the course, but that may change as I learn.

Part 1: Data Science Basics

1. Vectorization and Broadcasting: Numpy Tips and Tricks
2. Logistic Regression
3. Gradient Descent

Part 2: Neural Networks from Scratch

1. Gradient Descent on Multiple Examples
2. Vectorizing Logistic Regression
3. Creating a Neural Network from Scratch

More content to follow.