/Deep-Learning-Specialization-Coursera

Stanford University's Deep Learning specialization on Coursera taught by Prof. Andrew Ng

Primary LanguageJupyter Notebook

Deep Learning Specialization on Coursera

Master Deep Learning, and Break into AI

This is an excellent course, probably the best MOOC on Deep learning on the Internet today (based on my experience with multiple courses on this topic). The course requires basic understanding of Machine Learning concepts. Prof. Andrew Ng hand holds you as a beginner but drops you in the water through the journey until you are well versed with the nity grities of theoretical and practical aspects of Deep Learning.

These are my submissions required for completion of the course. If you are taking this course, I suggest you access this repo only to look for alternative implementations.

Instructor: Andrew Ng, DeepLearning.ai

BE INFORMED: Some of these iPython Notebooks doesn't open on Github interface due to it's size. So, you may be better off downloading and opening on your local machine. But feel free to check if it does open for you on github itself.

Course 1. Neural Networks and Deep Learning

  1. Week1 - Introduction to deep learning
  2. Week2 - Neural Networks Basics
  3. Week3 - Shallow neural networks - One hidden layer
  4. Week4 - 1. Building your Deep NN, 2. Application of your Deep NN on Image classification

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

  1. Week5 - 1, Initialization, 2. Regularization, 3. Gradient chacking - Setting up your Machine Learning Application - Regularizing your neural network - Setting up your optimization problem
  2. Week6 - Optimization methods
  3. Week7 - Tensorflow

Course 3. Structuring Machine Learning Projects

No Project submissions in this part. Only Quizzes.

  1. Week 8 - Bird Recognition in the city of Peacetopia - Case study - Setting up your goal - Comparing to human-level performance
  2. Week 9 - Autonomous Driving - Case study - Error Analysis - Mismatched training and dev/test set - Learning from multiple tasks - End-to-end deep learning

Course 4. Convolutional Neural Networks

  1. Week 10 - Convolution Model - Application
  2. Week 11 - 1. Keras Tutorial, 2.ResNets
  3. Week 12 - Car Detection for Autonomous Driving Unified, Real-Time Object Detection](https://arxiv.org/pdf/1506.02640.pdf), YOLO
  4. Week4 - 1. Face recognition, 2. Neural style transfer Papers for read: DeepFace, FaceNet

Course 5. Sequence Models

In progress