Deep Learning Specialization on Coursera - Break into AI

Deep Learning is a superpower. With it you can make a computer see, synthesize novel art, translate languages, render a medical diagnosis, or build pieces of a car that can drive itself. If that isn’t a superpower, I don’t know what is. — Andrew Ng, Founder of deeplearning.ai and Coursera Deep Learning Specialization, Course 5

Disclaimer According to the code of honor, while I deeply respect, it is forbidden to opensource the solutions as it may effect the learning of the enrolled students and also the standards of the specialization itself. However this repository is to document my incredible experience, by just listing the Projects, Assignments and Quizzes that I completed as a part of the specialization. This does not contain any of the work itself.

My Deep Learning journey started here!

Certificates availble in github repo

Projects

• Logistic Regression - Image classification
• Deep Neural Network - Image classification - Cat / Not Cat
• Convolutional Neural Network using TensorFlow - Hand Sign Recognition
• Happy House using Keras - Facial Expression 'Classification'
• Residual Networks - Hand Sign Recognition
• Autonomous Driving Application - Car detection using YOLO on Drive.ai dataset
• Face Recognition for the Happy House - FaceNet Architecture - OpenFace Model
• Art Generation with Neural Style Transfer- ImageNet VGG 16 Very Deep ConvNet
• Name Generation - Character level language model - Dinosaurus name
• Shakespearian poem generator using RNN LSTM in Keras
• Music Generation - Jazz Solo with an LSTM Network

Programming Assignments

• Course 1: Neural Networks and Deep Learning

  • PA 1 - Logistic Regression with a Neural Network mindset
  • PA 2 - Planar data classification with one hidden layer
  • PA 3 - Building your Deep Neural Network: Step by Step
  • PA 4 - Deep Neural Network for Image Classification: Application

• Course 2: Improving Deep Neural Networks: Hyper parameter tuning, Regularization and Optimization

  • PA 1 - Initialization
  • PA 2 - Regularization
  • PA 3 - Gradient Checking
  • PA 4 - Optimization Methods
  • PA 5 - TensorFlow Tutorial

• Course 3: Structuring Machine Learning Projects

  • There is no PA for this course. But this course comes with very interesting case study quizzes.

• Course 4: Convolutional Neural Networks

  • PA 1 - Convolutional Model: step by step
  • PA 2 - Convolutional Model: application
  • PA 3 - Keras - Tutorial - Happy House
  • PA 4 - Residual Networks

• Course 5: Sequence Models

  • PA 1 - Building a Recurrent Neural Network - Step by Step
  • PA 2 - Character level language model - Dinosaurus land