Deep Learning is one of the most popular topics amongst both students and software developers. We have recently released a learning path on Microsoft Learn that will help you get started with Deep Learning, based on one of the most popular frameworks: PyTorch.
|TL;DR: If you want to jump straight into learning, go to http://aka.ms/learntorch. Learning Path includes topics on Computer Vision, Natural Language and Audio.|
If you want to learn how to use PyTorch, your most obvious destination would be to go to the official web site at http://pytorch.org, and navigate to Tutorials -> Learn the Basics. There, you will be able to get a fast start with using the library by going straight into the code to define, train and use a neural network model to recognize different clothing types using FashionMNIST dataset.
While you can browse the code on PyTorch web site, you would probably want to start and run pieces of code to see how it works, and maybe make a few tweaks to understand better. To do it, you can click on “Learn with Microsoft Learn” button at the top of the page to switch to a learning mode.
Of course, you can also head straight into Microsoft Learn!
We believe that code-first approach is very convenient for those who are familiar with the basics of Python and data science, and want to get going with their project right away. Therefore, tutorial on PyTorch web site is structured around a complete example of image classification, and the first page shows you complete code, with links to go deeper into more detailed sections of the tutorial. Microsoft Learn module, on the contrary, starts with the simple concepts, and builds the whole code from the ground up.
There are a few reasons why you definitely want to use Microsoft Learn, and not just look at the code:
While PyTorch web site contains just one tutorial, the Microsoft Learn has a whole learning path of 4 modules:
There are two video episodes available on PyTorch Learning Path, which have recently been streamed on Learn TV
|Discussion at Ask the Expert show, together
with Dr. Amir Pourabollah and MLSAs
|Learning Path introduction in The Launch Space Show
with Brian Benz.
First module in the learning path focuses on one example of image classification, but starts with the very basics: what are tensors, how you can build a neural network from them, what is computational graph, how to compute derivatives/gradient and to use that to optimize neural network parameters. It uses simple multi-layer perceptron to do image classification.
In computer vision module, we first re-visit the problem of image classification using single-layer and multi-layer perceptrons, and learn about several different ways to implement neural networks in PyTorch. We then explain what convolutions are, and how convolutional neural networks can be used for computer vision. We then talk about pre-trained models, transfer learning, and lightweight models such as Mobilenet.
NLP module start with the basics of natural language processing - different tokenization strategies, bag-of-word and TF/IDF models for text classification. We then switch to word embeddings and semantic embeddings (word2vec, GloVe), and show how we can achieve good results with pre-trained embeddings and simple linear classification.
We then cover specific neural models for sequence modeling - recurrent neural networks, and their more advanced versions: LSTM and GRU. We consider also text generation models and train character-level LSTM text generator. Finally, we move to more recent language models such as BERT, and train text classifier on top of pre-trained network using transformers library.
Working with audio signals is a bit less common scenario, so this module will be especially useful for those who are looking to deal with audio. It explains the nature of audio signal, and how it can be transformed into frequency domain to build spectrograms, which can be used as features for machine learning models. It shows how we can classify audio signals based on image representation of their spectrograms.
I found some great potentials of this learning path to be adapted within a university level module of machine learning (ML). It has also potential of being taught as a stand-alone course for the university students of computer science, as well as the students of other science/technology disciplines who may be interested to apply ML on their subject area.
The highlights of my observations are as follows:
Rishit Dagli, Microsoft Student Ambassador, TEDx/TED-Ed Speaker
|I had the chance to review this course early and share some feedback with the team, and I loved this PyTorch fundamentals course. One of the great aspects about this course is that most of the course content is a Jupyter Notebook, with some text followed by code, this course isn't one where you can just read stuff but focuses majorly on practical aspects; a great mix of theory and practice. On the same note, I also feel this course could teach you the basics on top of which you could most certainly work on other projects. What more? You could run all the code on the browser itself, without needing any special hardware or software! On top of that while taking this course, you could also play around with the code, probably make some changes to the existing code, and see how it affects the training, and also get a GPU to power your notebooks all in your browser itself. I also feel this course explains deep learning concepts in a very easy and novel manner accompanied with code to help you better understand these. If you are a beginner and are planning to learn the basics of PyTorch, I would definitely recommend you to take this course.|
|I love the way the course has been structured as interactive Microsoft Learn modules. It is beginner-friendly and at the same time, takes the learner on a thrilling journey of Computer Vision, NLP and Audio classification in a fun manner. The learners can resonate well with all the modules, and I believe that there is a high-demand and need for such courses in a rapidly growing field of Artificial Intelligence.||
Syed Farhan Ahmad, Microsoft Gold Student Ambassador
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