/Deep_Learning_specilization_Coursera

Deep_Learning_specialization_Coursera

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Deep Learning Specialization on Coursera

Master Deep Learning, and Break into AI

Instructor: Andrew Ng

Goals

  • Learn the foundations of Deep Learning
  • Understand how to build neural networks
  • Learn how to lead successful machine learning projects
  • Learn about Convolutional networks, RNNs, LSTM, Adam, Dropout, BatchNorm, Xavier/He initialization, and more.
  • Work on case studies from health-care, autonomous driving, sign language reading, music generation, and natural language processing.
  • Practice all these ideas in Python and in TensorFlow.

Courses

Course 1: Neural Networks and Deep Learning

  • Week 1 - Introduction to deep learning

  • Week 2 - Neural Networks Basics

    • Learning Objectives
      • Build a logistic regression model, structured as a shallow neural network
      • Implement the main steps of an ML algorithm, including making predictions, derivative computation, and gradient descent.
      • Implement computationally efficient, highly vectorized, versions of models.
      • Understand how to compute derivatives for logistic regression, using a backpropagation mindset.
      • Become familiar with Python and Numpy
      • Work with iPython Notebooks
      • Be able to implement vectorization across multiple training examples
    • Notes 1 - Standard Notation

  • Week 3 - Shallow Neural Networks

    • Learning Objectives
      • Understand hidden units and hidden layers
      • Be able to apply a variety of activation functions in a neural network.
      • Build your first forward and backward propagation with a hidden layer
      • Apply random initialization to your neural network
      • Become fluent with Deep Learning notations and Neural Network Representations
      • Build and train a neural network with one hidden layer.

  • Week 4 - Deep Neural Networks

    • Learning Objectives
      • Learning Objectives
      • See deep neural networks as successive blocks put one after each other
      • Build and train a deep L-layer Neural Network
      • Analyze matrix and vector dimensions to check neural network implementations.
      • Understand how to use a cache to pass information from forward propagation to back propagation.
      • Understand the role of hyper-parameters in deep learning

Course 2: Improving Deep Neural Networks: Hyper-parameter tuning, Regularization and Optimization

  • Week 1 - Practical aspects of Deep Learning

    • Learning Objectives
      • Recall that different types of initializations lead to different results
      • Recognize the importance of initialization in complex neural networks.
      • Recognize the difference between train/dev/test sets
      • Diagnose the bias and variance issues in your model
      • Learn when and how to use regularization methods such as dropout or L2 regularization.
      • Understand experimental issues in deep learning such as Vanishing or Exploding gradients and learn how to deal with them
      • Use gradient checking to verify the correctness of your back-propagation implementation

  • Week 2 - Optimization algorithms

    • Learning Objectives
      • Remember different optimization methods such as (Stochastic) Gradient Descent, Momentum, RMSProp and Adam
      • Use random mini-batches to accelerate the convergence and improve the optimization
      • Know the benefits of learning rate decay and apply it to your optimization

  • Week 3 - Hyper-parameter tuning, Batch Normalization and Programming Frameworks

    • Learning Objectives
      • Master the process of hyper-parameter tuning
      • Learning Batch Norm
      • Learn about multi-class classifier
      • Train how to use tensorflow

Course 3: Structuring Machine Learning Projects

  • Week 1 - ML Strategy (1)

    • Learning Objectives
      • Understand why Machine Learning strategy is important
      • Apply satisficing and optimizing metrics to set up your goal for ML projects
      • Get to know single number evaluation metrics and how to deal with N metrics
      • Choose a correct train/dev/test split of your dataset
      • Understand how to define human-level performance
      • Use human-level perform to define your key priorities in ML projects
      • Take the correct ML Strategic decision based on observations of performances and dataset
    • Notes - Introduction to ML Strategy

  • Week 2 - ML Strategy (2)

    • Learning Objectives
      • Understand what multi-task learning and transfer learning are
      • Manual help might be needed to assist in figuring out next steps
      • Building up your system quickly then iterate
      • Recognize bias, variance and data-mismatch by looking at the performances of your algorithm on train/dev/test sets
      • Get to know when to use Transfer Learning and Multi-task learning
      • Introduction to End-to-end deep learning
    • Notes - Error Analysis
    • Notes - End-to-end Deep Learning

Course 4: Convolutional Neural Networks

  • Week 1 - Foundations of Convolutional Neural Networks

    • Learning Objectives
      • Understand the convolution operation
      • Understand the pooling operation
      • Remember the vocabulary used in convolutional neural network (padding, stride, filter, ...)
      • Build a convolutional neural network for image multi-class classification
    • Notes - Convolutional Neural Networks

  • Week 2 - Deep convolutional models: case studies

    • Learning Objectives
      • Understand multiple foundational papers of convolutional neural networks
      • Analyze the dimensionality reduction of a volume in a very deep network
      • Understand and Implement a Residual network
      • Build a deep neural network using Keras
      • Implement a skip-connection in your network
      • Clone a repository from github and use transfer learning
    • Notes - Practical advices for using ConvNets

  • Week 3 - Deep convolutional models: case studies

    • Learning Objectives
      • Understand the challenges of Object Localization, Object Detection and Landmark Finding
      • Understand and implement non-max suppression
      • Understand and implement intersection over union
      • Understand how we label a dataset for an object detection application
      • Remember the vocabulary of object detection (landmark, anchor, bounding box, grid, ...)

  • Week 4 - Deep convolutional models: case studies

    • Learning Objectives
      • Discover how CNNs can be applied to multiple fields, including art generation and face recognition.
      • Implement your own algorithm to generate art and recognize faces!

License

License These assignments are under MIT license Copyright (c) 2018 Mohamed el-Maghraby LICENSE.md

Each week contain it's assignment and data