deep-learning-specialization

• This repo contains all the code of my assignments for Andrew Ng specialization called "Deep Learning Specialization". This specialization is divided into five courses:

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

• Neural Networks and Deep Learning

  • In this course, you will learn the foundations of deep learning. When you finish this class, you will:

    • Understand the major technology trends driving Deep Learning
    • Be able to build, train and apply fully connected deep neural networks
    • Know how to implement efficient (vectorized) neural networks
    • Understand the key parameters in a neural network's architecture

  • This course also teaches you how Deep Learning actually works, rather than presenting only a cursory or surface-level description. So after completing it, you will be able to apply deep learning to a your own applications. If you are looking for a job in AI, after this course you will also be able to answer basic interview questions.

  • Programming Assignments:

    • Python Basics with Numpy [notebook] [py]
    • Logistic Regression with a Neural Network mindset [notebook] [py]
    • Planar data classification with one hidden layer [notebook] [py]
    • Building your Deep Neural Network Step by Step [notebook] [py]
    • Deep Neural Network Application [notebook] [py]

• Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization

  • This course will teach you the "magic" of getting deep learning to work well. Rather than the deep learning process being a black box, you will understand what drives performance, and be able to more systematically get good results. You will also learn TensorFlow.

    • Understand industry best-practices for building deep learning applications.
    • Be able to effectively use the common neural network "tricks", including initialization, L2 and dropout regularization, Batch normalization, gradient checking,
    • Be able to implement and apply a variety of optimization algorithms, such as mini-batch gradient descent, Momentum, RMSprop and Adam, and check for their convergence.
    • Understand new best-practices for the deep learning era of how to set up train/dev/test sets and analyze bias/variance
    • Be able to implement a neural network in TensorFlow.

  • Programming Assignments:

    • Initialization [notebook] [py]
    • Regularization [notebook] [py]
    • Gradient Checking [notebook] [py]
    • Optimization [notebook] [py]
    • Tensorflow [notebook] [py]

• Structuring Machine Learning Projects

  • You will learn how to build a successful machine learning project. If you aspire to be a technical leader in AI, and know how to set direction for your team's work, this course will show you how. Much of this content has never been taught elsewhere, and is drawn from my experience building and shipping many deep learning products. This course also has two "flight simulators" that let you practice decision-making as a machine learning project leader. This provides "industry experience" that you might otherwise get only after years of ML work experience.
    • Understand how to diagnose errors in a machine learning system, and
    • Be able to prioritize the most promising directions for reducing error
    • Understand complex ML settings, such as mismatched training/test sets, and comparing to and/or surpassing human-level performance
    • Know how to apply end-to-end learning, transfer learning, and multi-task learning

• Convolutional Neural Networks

• Sequence Models