Fast.ai has just published a follow-on to its free online course  Practical Deep Learning for Coders. At advanced level, Deep Learning from the Foundations is also free. How do these two courses stack up.

Fast.ai was founded last year by Jeremy Howard and Rachel Thomas and has as its slogan:

 "the world needs everyone involved with AI, no matter how unlikely your background"

and has an:

ongoing commitment to providing free, practical, cutting-edge education for deep learning practitioners and educators.

Currently it has two courses, each with around 15 hours of content. At introductory level Practical Deep Learning for Coders, now at version 3, requires minimal knowledge of Python, high school math, a GPU and the appropriate software; a humble Jupyter notebook for starters. That holds true for its new counterpart, Deep Learning from the Foundations, just released on the June 28th,  with the difference of being a bit wiser both with regard to Python and Deep Learning's workings. You also need to be comfortable moving to more powerful backend server platforms such as Crestle, Gradient, Google Cloud and Microsoft Azure.

Part 1 starts by teaching the ways to train a state-of-the-art image classification model, ending up with building and training a “resnet” neural network from scratch.

Using PyTorch and the fastai library as its tools, it covers the following key applications:

• Computer vision (e.g. classify pet photos by breed)
     Image classification
     Image localization (segmentation and activation maps)
     Image key-points
  
  
• NLP (e.g. movie review sentiment analysis)
    Language modeling
    Document classification
  
  
• Tabular data (e.g. sales prediction)
    Categorical data
    Continuous data
  
  
• Collaborative filtering (e.g. movie recommendation)

Pretty hefty for an introductory course...

Part 2 takes it from there, looking into more advanced concepts, beginning with matrix multiplication and back-propagation, to high performance mixed-precision training and the latest in neural network architectures and learning techniques.

The tools still included PyTorch and fastai, but at the end you also get to use Swift for TensorFlow.

Unlike the introductory one, this course goes behind the scenes looking into the underlying theory that drives Deep Learning. Building on those foundations, students will not just become capable of building a state of the art deep learning model from scratch, but also able to re-implement parts of the fastai library.
Part 2 involves the following material:

• Lesson 8: Matrix multiplication; forward and backward passes
  
  
• Lesson 9: Loss functions, optimizers, and the training loop
  
  
• Lesson 10: Looking inside the model
  
  
• Lesson 11: Data Block API, and generic optimizer
  
  
• Lesson 12: Advanced training techniques; ULMFiT from scratch
  
  
• Lesson 13: Basics of Swift for Deep Learning
  
  
• Lesson 14: C interop; Protocols; Putting it all together

It's important to note that throughout the course noteworthy research papers such as "Understanding the difficulty of training deep feed forward neural networks", "Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification" or "Three Mechanisms of Weight Decay Regularization" are explored and drawn upon. But that's not all.The course continuously updates with new lessons expected to be added in the coming months.

As AI becomes more and more pervasive, even being taught in schools as part of the Artificial Intelligence for K-12 curriculum, you'll have to deal with it sooner or later. So why not enhance your understanding on the ways it works or even use it for your own applications by simply joining this easy-to-follow and free course, altruistically built as a service to the community and the generations to come?

More Information

fast.ai

Practical Deep Learning for Coders, v3

Part 2: Deep Learning from the Foundations

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