Bennyhwanggggg / Coursera-Deep-Learning-Specialisation

Deep Learning Specialisation by deeplearning.ai on Coursera

Deep Learning Specialisation

This repository contains my work for the Deep Learning specialization courses by DeepLearning.ai on Coursera.

This specialisation contains five courses which can be taken on Coursera. The five courses titles are:

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.

Course 1: Neural Networks and Deep Learning

Summary

Practice Questions

• Introduction to Deep Learning Quiz

• Neural Network Basics

• Shallow Neural Networks

• Key concepts on Deep Neural Networks

Programming Assignments

• Logistic Regression with a Neural Network mindset

• Planar data classification with one hidden layer

• Building your Deep Neural Network: Step by Step

• Deep Neural Network for Image Classification: Application

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

Summary

Practice Questions

• Practical aspects of deep learning

• Optmisation Algorithms

• Hyperparameter tuning, Batch Normalization, Programming Frameworks

Programming Assignments

• Initialization

• Regularization

• Gradient Checking

• Optimization Methods

• TensorFlow Tutorial

Course 3: Structuring Machine Learning Projects

Summary

Practice Questions

• Autonomous Driving Case Study

• Bird Recogintion Case Study

Course 4: Convolutional Neural Networks

Summary

Practice Questions

• The basics of ConvNets

• Deep convolutional models

• Detection Algorithm

• Special applications- Face recognition & Neural style transfer

Programming Assignments

• Convolutional Model: step by step

• Convolutional Model: Application

• Keras Tutorial

• Residual Networks

• Face Recognition

• Car Detection

• Art Generation With Neural Style Transfer

Course 5: Sequence Models

Summary

Practice Questions

• Recurrent Neural Networks

• Natural Language Processing & Word Embeddings

• Sequence models & Attention mechanism

Programming Assignments

• Build RNN: step by step

• Dinosaur Island - Character lvel language model

• Emojify

• Jass Improvisation with LSTM

• Neural machine transation with attention

• Trigger Word Detection

• Operations on word vectors

Acknowledgements

• Thanks Andrew Ng for teaching this course.
• Thanks mbadry1 for his notes for me to add more of mine onto it.