Python has been the primary language for most Deep/Machine Learning enthusiasts, but there are quite a few JavaScript libraries that bring the magic of ML directly to the browser. Brain.js is one of the most popular JavaScript ML libraries known for its simple and easy usage. The library greatly simplifies building and training Neural Networks to just a few lines of code eliminating much of the math and jargon needed to fully understand the theoretical aspects of the model. In addition, the library has pros and cons like any other that we will explore in the end. NOTE(s): I am much more of a Python developer and am still in the process of getting proficient with JavaScript, so feel free to point out any best practices coding wise.

Would you recommend a masters in Data Science (as offered by most Universities) or a masters in some specialised field? Kindly do advise from the point of view of job opportunities in the respective domains as well. I would recommend getting an MSc in Data Science if you are confident that you are only interested in employment opportunities within Data Science roles. There still seems to be a high demand for Data Scientists despite the current pandemic and the difficulties some industries are experiencing. At the same time, there is also an increased supply of data scientists, which translates to more competitions for roles.

There are many ML frameworks for professionals to start working on. Some of them are TensorFlow, Caffe, Microsoft CNTK, PyTorch and others. These are required for building and deploying ML models. With a wide range of options available, it might often confuse beginners on what to begin with their project with. Our past interactions with ML professionals have shown that beginners often tend to incline towards TensorFlow because of its programmatic approach for creation of networks.

In this post, I will demonstrate how to use Google Colab for fastai. Colab is a Google internal research tool for data science. They have released the tool sometime earlier to the general public with a noble goal of dissemination of machine learning education and research. Although it's been for quite a while there is a new feature that will interest a lot of people. You can use GPU as a backend for free for 12 hours at a time.