README.md

Deep Learning Specialization on Coursera (offered by deeplearning.ai)

Programming assignments and quizzes answers from all courses in the Coursera Deep Learning Specialization offered by

Credits

This repo contains my work for this specialization. The code base, quiz questions and diagrams are taken from the Deep Learning Specialization, unless specified otherwise.

Courses

The Deep Learning Specialization on Coursera contains five courses:

• Course 1: Neural Networks and Deep Learning
• Course 2: Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization
• Course 3: Structuring Machine Learning Projects
• Course 4: Convolutional Neural Networks
• Course 5: Sequence Models

Specialization Info

• The Deep Learning Specialization is a foundational program that will help you understand the capabilities, challenges, and consequences of deep learning and prepare you to participate in the development of leading-edge AI technology.In this Specialization, you will build and train neural network architectures such as Convolutional Neural Networks, Recurrent Neural Networks, LSTMs, Transformers, and learn how to make them better with strategies such as Dropout, BatchNorm, Xavier/He initialization, and more. Get ready to master theoretical concepts and their industry applications using Python and TensorFlow and tackle real-world cases such as speech recognition, music synthesis, chatbots, machine translation, natural language processing, and more.AI is transforming many industries. The Deep Learning Specialization provides a pathway for you to take the definitive step in the world of AI by helping you gain the knowledge and skills to level up your career. Along the way, you will also get career advice from deep learning experts from industry and academia.

Applied Learning Project

By the end you’ll be able to:

• Build and train deep neural networks, implement vectorized neural networks, identify architecture parameters, and apply DL to your applications

• Use best practices to train and develop test sets and analyze bias/variance for building DL applications, use standard NN techniques, apply optimization algorithms, and implement a neural network in TensorFlow

• Use strategies for reducing errors in ML systems, understand complex ML settings, and apply end-to-end, transfer, and multi-task learning

• Build a Convolutional Neural Network, apply it to visual detection and recognition tasks, use neural style transfer to generate art, and apply these algorithms to image, video, and other 2D/3D data

• Build and train Recurrent Neural Networks and its variants (GRUs, LSTMs), apply RNNs to character-level language modeling, work with NLP and Word Embeddings, and use HuggingFace tokenizers and transformers to perform Named Entity Recognition and Question Answering

What you will learn

• Build and train deep neural networks, identify key architecture parameters, implement vectorized neural networks and deep learning to applications

• Train test sets, analyze variance for DL applications, use standard techniques and optimization algorithms, and build neural networks in TensorFlow

• Build a CNN and apply it to detection and recognition tasks, use neural style transfer to generate art, and apply algorithms to image and video data

• Build and train RNNs, work with NLP and Word Embeddings, and use HuggingFace tokenizers and transformer models to perform NER and Question Answering

Usage

I share the assignment notebooks with my prefilled and from the contributors code structred as in the course Course/Week The assignment notebooks are subject to changes through time.

Connect with your mentors and fellow learners on Slack!

Once you enrolled to the course, you are invited to join a slack workspace for this specialization: Please join the Slack workspace by going to the following link deeplearningai-nlp.slack.com This Slack workspace includes all courses of this specialization.

Programming Assignments

Course 1: Neural Networks and Deep Learning

• Week 1

  • No Lab
  • Week 1 Quiz Introduction to Deep Learning

• Week 2 Labs & Quiz:

  • Python Basics with Numpy (optional assignment)
  • Logistic Regression with a Neural Network mindset
  • Week 2 Quiz Neural Network Basics

• Week 3 Lab & Quiz:

  • Planar data classification with one hidden layer
  • Week 3 Quiz Shallow Neural Networks

• Week 4 Labs & Quiz:

  • Building your Deep Neural Network: Step by Step
  • Deep Neural Network for Image Classification: Application
  • Week 4 Quiz Key Concepts on Deep Neural Networks

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

• Week 1 Labs & Quiz:
  • Initialization
  • Regularization
  • Gradient Checking
  • Week 1 Quiz Practical aspects of Deep Learning
• Week 2 Labs & Quiz:
  • Optimization Methods
  • Week 2 Quiz Optimization Algorithms
• Week 3 Labs & Quiz:
  • Introduction to TensorFlow
  • Week 3 Quiz Hyperparameter tuning, Batch Normalization, Programming Frameworks

Course 3: Structuring Machine Learning Projects

• Week 1 Labs & Quiz:
  • No Lab
  • Week 1 Quiz Bird Recognition in the City of Peacetopia (Case Study)
• Week 2 Labs & Quiz:
  • No Lab
  • Week 2 Quiz Autonomous Driving (Case Study)

Course 4: Convolutional Neural Networks

• Week 1 Labs & Quiz:
  • Convolutional Neural Networks: Step by Step
  • Convolutional Neural Networks: Application
  • Week 1 Quiz The Basics of ConvNets
• Week 2 Labs & Quiz:
  • Residual Networks
  • Transfer Learning with MobileNetV2
  • Week 2 Quiz Deep Convolutional Models
• Week 3 Labs & Quiz:
  • Autonomous Driving - Car Detection
  • Image Segmentation with U-Net
  • Week 3 Quiz Detection Algorithms
• Week 4 Labs & Quiz:
  • Face Recognition
  • Deep Learning & Art: Neural Style Transfer
  • Week 4 Quiz Special Applications Face Recognition and Neural Style Transfer

Course 5: Sequence Models

• Week 1 Labs & Quiz:

  • Building your Recurrent Neural Network - Step by Step
  • Character level language model - Dinosaurus Island
  • Improvise a Jazz Solo with an LSTM Network
  • Week 1 Quiz Recurrent Neural Networks

• Week 2 Labs & Quiz:

  • Operations on Word Vectors
  • Emojify!
  • Week 2 Quiz Natural Language Processing and Word Embeddings

• Week 3 Labs & Quiz:

  • Neural Machine Translation
  • Trigger Word Detection
  • Week 3 Quiz Sequence Models and Attention Mechanism

• Week 4 Labs & Quiz:

  • Transformer Network
  • Week 4 Quiz Trasformers

  Certificate

1. Neural Networks and Deep Learning
2. Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization
3. Structuring Machine Learning Projects
4. Convolutional Neural Networks
5. Sequence Models
6. Deep Learning Specialization(Final Certificate)

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References

1. Neural Networks and Deep Learning
2. Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization
3. Structuring Machine Learning Projects
4. Convolutional Neural Networks
5. Sequence Models

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📝 License

The gem is available as open source under the terms of the MIT license.

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Disclaimer

I recognize the hard time people spend on building intuition, understanding new concepts and debugging assignments. The solutions uploaded here are only for reference. They are meant to unblock you if you get stuck somewhere. Please do not copy any part of the code as-is (the programming assignments are fairly easy if you read the instructions carefully). Similarly, try out the quizzes yourself before you refer to the quiz solutions.