Deep Learning

These are the programming assignments from Coursera's Deep Learning Specialization taught by Andrew Ng provided by Deeplearning.ai

In five courses, you will learn the foundations of Deep Learning, understand how to build neural networks, and learn how to lead successful machine learning projects. You will learn about Convolutional networks, RNNs, LSTM, Adam, Dropout, BatchNorm, Xavier/He initialization, and more. You will work on case studies from healthcare, autonomous driving, sign language reading, music generation, and natural language processing. You will master not only the theory, but also see how it is applied in industry. You will practice all these ideas in Python and in TensorFlow, which the specialization will teach.

Programming Assignments

Neural Networks and Deep Learning

• Logistic Regression with a Neural Network mindset
• Planar data classification with a hidden layer
• Building your deep neural network: Step by Step
• Deep Neural Network Application

Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization

• Initialization
• Regularization
• Gradient Checking
• Optimization
• Tensorflow

Structuring Machine Learning Projects

Convolutional Neural Networks

• Convolutional Model: step by step
• Convolutional model: application
• Residual Networks
• Car detection with YOLOv2
• Art generation with Neural Style Transfer
• Face Recognition for the Happy House

Sequence Models

• Building a recurrent neural network - step by step
• Dinosaur Island - Character-Level Language Modeling
• Jazz improvisation with LSTM
• Operations on word vectors - Debiasing
• Emojify
• Neural Machine Translation with Attention
• Trigger word detection