Intro to Deep Learning with PyTorch

Facebook has a course on Udacity that teaches Deep Learning with PyTorch

https://www.udacity.com/course/deep-learning-pytorch--ud188

It sounds a lot nicer than the reality. You'll be able to stitch pieces of code together and use basic models. It will not teach you how to build your own models. Let's put it this way. I attempted the PyTorch Udacity course after MSDA and got stuck in Lesson 2 because I didn't know Python well enough to go further than that. https://www.udacity.com/course/deep-learning-pytorch--ud188

I've covered the items in the Udacity PyTorch DL course https://www.udacity.com/course/deep-learning-pytorch--ud188

First I'd say stay away from Tensor Flow. Pytorch is much more user friendly.

Since you like video-style learning like Khan academy I'd say:

• Udacity's course on pytorch . It explains a lot of the basic concepts, of machine learning

Once you're done there, it's time to get a bit hands on with the pytorch library itself, and write some code

• Follow the Pytorch tutorials on their website

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These should be enough to teach you the basics, and once you're past that it should be a balance between learning more advanced topics, and getting hands on with more advanced code

For the topics end, a lot of universities & research groups are making their deep learning courses available for free online.

My favourites for general deep learning were:

• Fast AI's course: they really go into the current state of the art in computer vision and NLP
• UCL & Deepmind's course on advanced deep learning & rl (skip the reinforcement learning lessons if you want; they aren't tied to the deep learning bits).

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For the "hands-on" stuff, I'd say look for examples of other people creating open-source projects on github similar to what you want to build.

Read through their code and try to get it running on your machine

It's important to start simple and build up. You're probably not going to invent Jarvis in a month ;)

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P.s. it's most convenient to have your own GPU when you're learning so you can quickly try out ideas, but if you don't, Google Colab offers free GPU-accelerated machines in the cloud.

This looks like the program many of the people in the scholarship are doing for Pytorch, you might consider signing up here to get notified if they do it again or if it ever becomes a free course (for the 2 month part):
https://www.udacity.com/course/deep-learning-pytorch--ud188

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two free courses you might find interesting

https://www.udacity.com/course/intro-to-tensorflow-for-deep-learning--ud187

https://www.udacity.com/course/deep-learning-pytorch--ud188

Udacity has a good free one.

https://www.udacity.com/course/deep-learning-pytorch--ud188

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Their full nanodegree is also based on pytorch and is pretty good.

check this awesome free course by udacity https://www.udacity.com/course/deep-learning-pytorch--ud188

This is the best one I found, from Facebook it'self

https://www.udacity.com/course/deep-learning-pytorch--ud188

Deep Learning with Python by Francois Chollet, the creator of Keras, and is a good, quick way to get started with neural networks.

There's also a free Udacity Intro to Deep Learning with PyTorch course.

Keras is a wrapper for TensorFlow, which is very popular, but I personally find PyTorch more intuitive and fun to work with.

Neither of those teach you the fundamentals of machine learning though, which IMO is crucial to fully understand deep learning/neural networks. The free Udacity Intro to Machine Learning course will give you good foundation without too much theory or math.

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Here are some resources that you might find useful: -https://www.udacity.com/course/deep-learning-pytorch--ud188 -https://www.edx.org/course/deep-learning-with-python-and-pytorch -https://pytorch.org/tutorials/ -https://github.com/spro/practical-pytorch -https://github.com/ritchieng/the-incredible-pytorch -https://fleuret.org/ee559/ -https://www.youtube.com/playlist?list=PLZbbT5o_s2xrfNyHZsM6ufI0iZENK9xgG

Its pretty much normal. I was also in the same situation before. I realized that while doing a project, it was the tools behind which I was spending most of my time rather than theory. There's a huge gap between understanding a theory and its implementation. You have to cover that bridge. For that I recommend you to watch fast.ai course and also check out deeplizard channel on youtube. Neural network demystified is also a cool playlist on youtube. You can also check out O'Reilly's book : Hands on Machine Learning with scikit learn and tensorflow. Then there's also a course on pytorch in Udacity. At last you can also check out Deep Learning A-Z course on Udemy. These are some good resources to follow.

TL DR : follow the links -:

Fast.ai

https://www.youtube.com/channel/UC4UJ26WkceqONNF5S26OiVw - deeplizard

https://www.youtube.com/playlist?list=PLiaHhY2iBX9hdHaRr6b7XevZtgZRa1PoU - neural network demystified

https://www.oreilly.com/library/view/hands-on-machine-learning/9781491962282/ - O'Reilly's book

https://www.udacity.com/course/deep-learning-pytorch--ud188 - Udacity course

https://www.udemy.com/share/10004kAkAYd15TQno=/ - Udemy course

https://www.udacity.com/course/deep-learning-pytorch--ud188

edit: I can almost bet these will be part of the challenge course too https://www.udacity.com/course/deep-learning--ud730

https://www.udacity.com/course/machine-learning--ud262

All the challenge course lessons are free.

Honestly, there are so many people coming out of data science bootcamps and Masters programs, most companies won't hire junior level data scientists. To become an actual data scientist, you'll be best off getting a data analyst role first and gain at least a year of experience there. The best option from there is to try to move up to a DS role at your current company. If that isn't possible, then you should look elsewhere.

Otherwise the advice is similar.

• Put everything on GitHub.
• Focus on building SQL & Python skills. SQL is more important.
• Develop a basic understanding of statistics & probability. Focus on linear models and A/B tests. That is most of the statistics people actually do in data analyst/scientist roles.
• Work on tons of projects, write them up as blog posts.
• There are a lot of datasets you can use on Kaggle: https://www.kaggle.com/datasets. Take a dataset, clean it up, make some exploratory visualizations. Find interesting patterns in the data. If there are opportunities to make predictive models, try that out. Write it up as a blog post.
• Get some experience with Tableau or similar data viz software. Tableau is on \~50% of DA/DS job descriptions.
• Once you get a data analyst role, start focusing on machine learning. Machine learning is a whole thing with a bunch of different topics.
• We're building short courses at Kaggle that'll help you get started with machine learning: https://www.kaggle.com/learn/overview. Also, here's a free deep learning course I built at Udacity: https://www.udacity.com/course/deep-learning-pytorch--ud188