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DL Specialization

https://www.coursera.org/specializations/deep-learning

1. Neural Networks and 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

This course also teaches you how Deep Learning actually works, rather than presenting only a cursory or surface-level description.

Week 1 - Introduction to deep learning

Week 2 - Neural networks basics

  • Build a logistic regression model structured as a shallow neural network
  • Build the general architecture of a learning algorithm, including parameter initialization, cost function and gradient calculation, and optimization implemetation (gradient descent)
  • Implement computationally efficient and highly vectorized versions of models
  • Compute derivatives for logistic regression, using a backpropagation mindset
  • Use Numpy functions and Numpy matrix/vector operations
  • Implement vectorization across multiple training examples

Week 3 - Shallow neural networks

Week 4 - Deep neural networks

2. 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.

After 3 weeks, you will:

  • 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.

3. Structuring Machine Learning Projects​

After 2 weeks, you will:

  • 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 multitask learning I've seen teams waste months or years through not understanding the principles taught in this course.

I hope this two week course will save you months of time. This is a standalone course, and you can take this so long as you have basic machine learning knowledge.

4. Convolutional Neural Networks (CNN)​

This course will teach you how to build convolutional neural networks and apply it to image data. Thanks to deep learning, computer vision is working far better than just two years ago, and this is enabling numerous exciting applications ranging from safe autonomous driving, to accurate face recognition, to automatic reading of radiology images.

  • 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.

This is the fourth course of the Deep Learning Specialization.

5. Sequence Models​

This course will teach you how to build models for natural language, audio, and other sequence data. Thanks to deep learning, sequence algorithms are working far better than just two years ago, and this is enabling numerous exciting applications in speech recognition, music synthesis, chatbots, machine translation, natural language understanding, and many others.

  • 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.

Notes​

  • M - Number of training examples