/Deep-Learning-Specialization-Coursera

Deep Learning Specialization courses by Andrew Ng, deeplearning.ai

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Coursera Deep Learning Specialization

This is the repository for my implementations on the Deep Learning Specialization from Coursera.

Taught by Andrew Ng

Foundations of Deep Learning:

  • 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

Codes:

  • 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.

Codes:

  • 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
  • Understand how to build a convolutional neural network, including recent variations such as residual networks.
  • Know how to apply convolutional networks to visual detection and recognition tasks.
  • Know to use neural style transfer to generate art.
  • Be able to apply these algorithms to a variety of image, video, and other 2D or 3D data.

Codes:

  • Understand how to build and train Recurrent Neural Networks (RNNs), and commonly-used variants such as GRUs and LSTMs.
  • Be able to apply sequence models to natural language problems, including text synthesis.
  • Be able to apply sequence models to audio applications, including speech recognition and music synthesis.

Codes: