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new fast.ai course: A Code-First Introduction to Natural Language Processing

Today we are releasing a new course (taught by me), Deep Learning from the Foundations, which shows how to build a state of the art deep learning model from scratch.

It takes you all the way from the foundations of implementing matrix multiplication and back-propogation, through to high performance mixed-precision training, to the latest neural network architectures and learning techniques, and everything in between.

It covers many of the most important academic papers that form the foundations of modern deep learning, using “code-first” teaching, where each method is implemented from scratch in python and explained in detail (in the process, we’ll discuss many important software engineering techniques too).

The whole course, covering around 15 hours of teaching and dozens of interactive notebooks, is entirely free (and ad-free), provided as a service to the community.

It is the latest in our ongoing commitment to providing free, practical, cutting-edge education for deep learning practitioners and educators—a commitment that has been appreciated by hundreds of thousands of students, led to The Economist saying “Demystifying the subject, to make it accessible to anyone who wants to learn how to build AI software, is the aim of Jeremy Howard… It is working”, and to CogX awarding fast.ai the Outstanding Contribution in AI award.

huge amount of work went into the last two lessons—not only did the team need to create new teaching materials covering both TensorFlow and Swift, but also create a new fastai Swift library from scratch, and add a lot of new functionality (and squash a few bugs!) in Swift for TensorFlow.

It was a very close collaboration between Google Brain’s Swift for TensorFlow group and fast.ai, and wouldn’t have been possible without the passion, commitment, and expertise of the whole team, from both Google and fast.ai.

We’ll then use this to create a basic neural net forward pass, including a first look at how neural networks are initialized (a topic we’ll be going into in great depth in the coming lessons).

Finally, we develop a new kind of normalization layer to overcome these problems, compare it to previously published approaches, and see some very encouraging results.

We’ll look closely at each step: Next up, we build a new StatefulOptimizer class, and show that nearly all optimizers used in modern deep learning training are just special cases of this one class.

We develop a new GPU-based data augmentation approach which we find speeds things up quite dramatically, and allows us to then add more sophisticated warp-based transformations.

We implement some really important training techniques in lesson 12, all using callbacks: We also implement xresnet, which is a tweaked version of the classic resnet architecture that provides substantial improvements.

Finally, we show how to implement ULMFiT from scratch, including building an LSTM RNN, and looking at the various steps necessary to process natural language data to allow it to be passed to a neural network.

He shares insights on its development history, and why he thinks it’s a great fit for deep learning and numeric programming more generally.

Thanks to the compilation and language design, basic code runs very fast indeed - about 8000 times faster than Python in the simple example Chris showed in class.

He shows how to use this to quickly and easily get high performance code by interfacing with existing C libraries, using Sox audio processing, and VIPS and OpenCV image processing as complete working examples.

So be sure to study the notebooks to see lots more Swift tricks… We’ll be releasing even more lessons in the coming months and adding them to an attached course we’ll be calling Applications of Deep Learning.

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