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Neural Networks for Machine Learning

Improving Deep Neural Networks_Hyperparameter Tuning_Regularization and Optimization

structuring machine learning projects

convolutional neural networks

sequence models

Deep Learning

  • [x] Neural Networks and Deep Learning
  • [x] Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization
  • [x] Structuring Machine Learning Projects
  • [x] Convolutional Neural Networks
  • [x] Sequence Models

Deep Learning -

Coursera Deep Learning Course by projects

## Course 1. Neural Networks and Deep Learning 1. Week1 - Introduction to deep learning 2. Week2 - Neural Networks Basics 3. Week3 - Shallow neural networks 4. Week4 - Deep Neural Networks

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

  1. Week1 - Practical aspects of Deep Learning - Setting up your Machine Learning Application - Regularizing your neural network - Setting up your optimization problem
  2. Week2 - Optimization algorithms
  3. Week3 - Hyperparameter tuning, Batch Normalization and Programming Frameworks

Course 3. Structuring Machine Learning Projects

  1. Week1 - Introduction to ML Strategy - Setting up your goal - Comparing to human-level performance
  2. Week2 - ML Strategy (2) - Error Analysis - Mismatched training and dev/test set - Learning from multiple tasks - End-to-end deep learning

## Course 4. Convolutional Neural Networks 1. Week1 - Foundations of Convolutional Neural Networks 2. Week2 - Deep convolutional models: case studies 3. Week3 - Object detection - Papers for read: You Only Look Once: Unified, Real-Time Object Detection, YOLO 4. Week4 - Special applications: Face recognition & Neural style transfer - Papers for read: DeepFace, FaceNet

## Course 5. Sequence Models 1. Week1 - Recurrent Neural Networks 2. Week2 - Natural Language Processing & Word Embeddings 3. Week3 - Sequence models & Attention mechanism

source from *Andrew Ng*'s Deep learning course on Coursera

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