README.md

Deep Learning Specialization on Coursera

Master Deep Learning, and Break into AI

Instructor: Andrew Ng, DeepLearning.ai

Introduction

This repo contains all of the updated latest version of Programming Assignments, Quizes for this specialization. All the code base, quiz questions, screenshot, and images, are taken from, unless specified, Deep Learning Specialization on Coursera

What I want to say

This is quite a fantastic course and it has everything equipped in it for you to start your deep learning knowledge as a beginner and pave the path for advanced level. This Repo is only supposed to help you in this journey ONLY FOR EDUCATIONAL PURPOSES ONLY!

Below are Programming Assignments and Quiz for every course in the specialisation

You can watch course videos by Deeplearning.ai Youtube Channel for free here

If you are struck or wanted some guidance I am more than happy to help Contact Me

Here are some Notes and a brief PPT which will make you understand concepts better and go back to revise them any time you want.

Programming Assignments

• Course 1: Neural Networks and Deep Learning

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

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

  • Week 1 - PA 1 - Initialization
  • Week 1 - PA 2 - Regularization
  • Week 1 - PA 3 - Gradient Checking
  • Week 2 - PA 1 - Optimization Methods
  • Week 3 - PA 2 - 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

  • Week 1 - PA 1 - Convolutional Model: step by step
  • Week 1 - PA 2 - Convolutional Model: application
  • Week 2 - PA 1 - Residual Networks
  • Week 3 - PA 1 - Car detection for Autonomous Driving
  • Week 4 - PA 1 - Face Recognition
  • Week 4 - PA 2 - Neural Style Transfer

• Course 5: Sequence Models

  • Week 1 - PA 1 - Building a Recurrent Neural Network - Step by Step
  • Week 1 - PA 2 - Character level language model - Dinosaurus land
  • Week 1 - PA 3 - Jazz improvisation with LSTM
  • Week 2 - PA 1 - Emojifi
  • Week 2 - PA 2 - Word Vector Representation
  • Week 3 - PA 1 - Machine Translation
  • Week 3 - PA 2 - Trigger Word Detection

Quiz Solutions

There are concerns that some people may use the content here to quickly ace the course. I wanted to tell you that you can attempt it as many times you like and you will never grab the concepts if you just try to have good score which doesn't even reflect anywhere (Remember knowledge is far important than some certificate to achieve you dreams) instead use this key with explanation as a last resort if you dont understand why you are wrong.

• Course 1: Neural Networks and Deep Learning

  • Week 1 Quiz - Introduction to deep learning
  • Week 2 Quiz - Neural Network Basics
  • Week 3 Quiz - Shallow Neural Networks
  • Week 4 Quiz - Key concepts on Deep Neural Networks

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

  • Week 1 Quiz - Practical aspects of deep learning
  • Week 2 Quiz - Optimization algorithms
  • Week 3 Quiz - Hyperparameter tuning, Batch Normalization, Programming Frameworks

• Course 3: Structuring Machine Learning Projects

  • Week 1 Quiz - Bird recognition in the city of Peacetopia (case study)
  • Week 2 Quiz - Autonomous driving (case study)

• Course 4: Convolutional Neural Networks

  • Week 1 Quiz - Basics of ConvNets
  • Week 2 Quiz - Deep Convoltuion Models
  • Week 3 Quiz - Detection Algorithms
  • Week 4 Quiz - Face Recognition & Neural Style Transfer

• Course 5: Sequence Models

  • Week 1 Quiz - Recurrent Neural Networks
  • Week 2 Quiz - Natural Language Processing & Word Embeddings
  • Week 3 Quiz - Sequence Models & Attention Mechanisms

Thank You !