README.md

Course Structure

Each course in the specialization comprises of Lecture videos, quizzes, programming assignments and interviews with various Deep learning Researchers.

1. Neural Networks and Deep Learning

This course comprises of 4 weeks.

Week 1:

• Lecture Topics

  • Introduction to Deep Learning

• Heroes of Deep Learning

  • Geoffrey Hinton Interview

Week 2:

• Lecture Topics

  • Logistic Regression as a Neural Network
  • Python and Vectorization

• Programming Assignment

  • Python Basics with Numpy. My Notebook implementation can be found here.
  • Logistic Regression with a Neural Network mindset. My Notebook implementation can be found here.

• Heroes of Deep Learning

  • Peter Abbeel Interview

Week 3:

• Lecture Topics

  • Shallow Neural Network

• Programming Assignment

  • Planar Data Classification with a hidden layer. My Notebook implementation can be found here.

• Heroes of Deep Learning

  • Ian Goodfellow Interview

Week 4:

• Lecture Topics

  • Deep Neural Network

• Programming Assignment

  • Building your Deep Neural Network: Step by Step. My Notebook implementation can be found here.
  • Deep Neural Network Application. My Notebook implementation can be found here.

2. Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization

This course comprises of 3 weeks.

Week 1:

• Lecture Topics

  • Setting up your Machine Learning Application
  • Regularizing your Neural Network
  • Setting up your Optimization problem

• Programming Assignment

  • Initialization. My Notebook implementation can be found here.
  • Regularization. My Notebook implementation can be found here.
  • Gradient Checking. My Notebook implementation can be found here.

• Heroes of Deep Learning

  • Yoshua Bengio Interview

Week 2:

• Lecture Topics

  • Optimization Algorithms

• Programming Assignment

  • Optimization. My Notebook implementation can be found here.

• Heroes of Deep Learning

  • Yuanqing Lin Interview

Week 3:

• Lecture Topics

  • Hyperparameter tuning.
  • Batch Normalization
  • Multi-Class Classification
  • Introduction to programming frameworks

• Programming Assignment

  • Tensorflow. My Notebook implementation can be found here.

3. Structuring Machine Learning Projects

This course comprises of 2 weeks.

Week 1:

• Lecture Topics

  • Introduction to ML strategy
  • Setting up your Goal
  • Comparing to Human-level performance
  • Machine Learning Flight Simulator

• Heroes of Deep Learning

  • Andrej Karpathy Interview

Week 2:

• Lecture Topics

  • Error Analysis
  • Mismatched training and dev/test set
  • Learning from multiple tasks
  • End-to-end deep learning
  • Machine Learning flight simulator

• Heroes of Deep Learning

  • Ruslan Salakhutdinov Interview

• 4. Convolutional Neural Networks

• 5. Sequence Models