Programming assignments and quizzes from all courses in the Coursera Deep Learning specialization offered by deeplearning.ai.

Instructor: Andrew Ng

The code base, quiz questions and diagrams are taken from the Deep Learning Specialization on Coursera, unless specified otherwise.

• Introduction to Deep Learning
  • Analyze the major trends driving the rise of deep learning, and give examples of where and how it is applied today.
• Neural Networks Basics
  • Set up a machine learning problem with a neural network mindset and use vectorization to speed up your models.
• Shallow Neural Networks
  • Build a neural network with one hidden layer, using forward propagation and backpropagation.
• Deep Neural Networks
  • Analyze the key computations underlying deep learning, then use them to build and train deep neural networks for computer vision tasks.

• Deep Learning
• Artificial Neural Network
• Backpropagation
• Python Programming
• Neural Network Architecture

• Practical Aspects of Deep Learning
  • Discover and experiment with a variety of different initialization methods, apply L2 regularization and dropout to avoid model overfitting, then apply gradient checking to identify errors in a fraud detection model.
• Optimization Algorithms
  • Develop your deep learning toolbox by adding more advanced optimizations, random minibatching, and learning rate decay scheduling to speed up your models.
• Hyperparameter Tuning, Batch Normalization and Programming Frameworks
  • Explore TensorFlow, a deep learning framework that allows you to build neural networks quickly and easily, then train a neural network on a TensorFlow dataset.

• Tensorflow
• Deep Learning
• Mathematical Optimization
• Hyperparameter tuning

• How to build a successful machine learning project and get to practice decision-making as a machine learning project leader.
• Diagnose errors in a machine learning system; prioritize strategies for reducing errors; understand complex ML settings, such as mismatched training/test sets, and comparing to and/or surpassing human-level performance; and apply end-to-end learning, transfer learning, and multi-task learning.

• Deep Learning
• Inductive Transfer
• Machine Learning
• Multi-Task Learning
• Decision-Making

• Understand how computer vision has evolved and become familiar with its exciting applications such as autonomous driving, face recognition, reading radiology images, and more.
• Build a convolutional neural network, including recent variations such as residual networks; apply convolutional networks to visual detection and recognition tasks; and use neural style transfer to generate art and apply these algorithms to a variety of image, video, and other 2D or 3D data.

• Deep Learning
• Facial Recognition System
• Convolutional Neural Network
• Tensorflow
• Object Detection and Segmentation

• Become familiar with sequence models and their exciting applications such as speech recognition, music synthesis, chatbots, machine translation, natural language processing (NLP), and more.
• Build and train Recurrent Neural Networks (RNNs) and commonly-used variants such as GRUs and LSTMs; apply RNNs to Character-level Language Modeling; gain experience with natural language processing and Word Embeddings; and use HuggingFace tokenizers and transformer models to solve different NLP tasks such as NER and Question Answering.

• Natural Language Processing
• Long Short Term Memory (LSTM)
• Gated Recurrent Unit (GRU)
• Recurrent Neural Network
• Attention Models