fast.ai

I'd start by clarifying what you mean by "an understanding on AI" what do you mean by AI? Do you mean machine learning as a scientific discipline? or something else. You can probably start with Wikipedia to get a map at that level. I often see https://www.fast.ai/ recommended as an answer to this question, but I have not used their courses so I can't comment on their utility. You could get a map of current industrial capabilities by looking at the white papers and documentation for the AI web services offered by Google, AWS, Azure. Two podcasts with interviews with researchers and developers: MLST, Latent Space

https://fast.ai is hands down the best resource for learning more about deep learning as a normal software person.

If you want to learn A.I. try https://www.fast.ai/ Its probably one of the best places to start. Highly recommended.

Two things I forgot to mention. If you want to see what the curriculum of a "Math for ML" from a top research uni looks like, you should check out the website of Math for ML course offered by Universitat Tubingen [0]. If you know those, you will be able to read the math of most papers that you will come across. Secondly, the best way to get started with ML is to do the Andrew Ng classic on Coursera. Then move on to FastAI [1]. Fastai is a fantastic learning resource and you will learn many nice things from Jeremy Howard that will help you make your own models. But do NOT limit yourself with fastai. It's a crappy software- too many limitation, syn sugars, API anti patterns, etc. Learn PyTorch for full-fledged projects. [0]: https://www.tml.cs.uni-tuebingen.de/teaching/2020_maths_for_ml/ [1]: https://fast.ai

So there's https://fast.ai MOOCs+library which don't require a big theoretical background I heard about a program that looked great to do data science from just data a while ago, but I can't find it :/ It was on Show HN I think I found that tho, don't know if it's gonna be helpful: https://towardsdatascience.com/top-8-no-code-machine-learning-platform… https://www.obviously.ai/

> Are there any resources this comprehensive for any other field of study? ... I was specifically interested in biotech I recommend the FastAI course on deep learning. Several of their lectures relate to things their students have done in biotech and medical. The main lecturer Jeremy Howard has worked for years at the crossroads of medicinal technology and AI, and routinely discusses this. The full fastai course is here[1] and free. Here is a blog post and associated video[2] as an example of fastai incorporating biotech into their work. In this example they use AI to upsample the resolution and quality of microscopes. [1] https://www.fast.ai/ [2] https://www.fast.ai/2019/05/03/decrappify/

This is not a practical recommendation by any means... but fast ai has a lecture on image restoration with deep learning. https://www.fast.ai/ https://youtu.be/9spwoDYwW_I?t=2913

A lot of people probably don’t know that Fastmail was started by Jeremy Howard, one of the creators of https://www.fast.ai which is by far the best way to learn deep learning IMHO :)

For people who want to dive into Deep Learning, I highly recommend checking these resources: 1. Fast.ai: https://www.fast.ai/ 2. Open AI Spinning Up: https://spinningup.openai.com/en/latest/index.html Read: 1. A Google Brain engineer’s guide to entering AI https://news.ycombinator.com/item?id=18421422 2. How I became a machine learning practitioner https://blog.gregbrockman.com/how-i-became-a-machine-learning-practiti…

Contrary to popular opinion, I find that Andrew Ng's Intro to ML (on Coursera/Stanford) focuses too much on basic math and theory - which doesn't detract from the course's quality, but makes the course a drudgery to go through. Programming exercises involve a single line or two, and that too in Octave - which was all the rage back when the course was launched, but it's not so useful now. Instead, start with this - https://www.fast.ai It emphasizes practicality to the extreme - you are only taught theory/domain knowledge when needed. The instructor's amazing, the massive scale of knowledge imparted boggles the mind, and you feel like you've accomplished something when you're finished with it. Best of all, it's free. And you can start Deep Learning there too when you're done with ML, if you feel the need (or interest).

If you're interested in a very practical, explain-it-clearly, no marketing type of resource for deep learning, I can highly recommend https://www.fast.ai/ I did the first version of the course, and I felt that at the end I legitimately knew enough to read current papers and see through any hype presented to me anywhere else.

https://www.fast.ai/ The only online course I've undertaken, so of course it's the best one. But it's exceptionally well made, it's up to date, teaches about a wide area of machine learning and AI field. Not just the theory, but about Kaggle, how professional AI designers work, what they do, "what it's all about", and so forth. The community is helpful, the forums are active. All free btw. Very good!

Here are some others that are also commonly recommended on HN: Andrew Ng's Machine Learning MOOC: https://www.coursera.org/learn/machine-learning Andrew Ng's Deep Learning Courses: https://www.deeplearning.ai/ David Silver's Reinforcement Learning Youtube Series: http://www0.cs.ucl.ac.uk/staff/d.silver/web/Teaching.html Fast.ai Courses on ML and Deep Learning: https://www.fast.ai/

Markov chains are cool and text generation is fun for experiments. I would definitely recommend looking at deep learning language models, for a more fruitful investment of time to improve further. (courses on http://www.fast.ai/ are a good starting place)

You might find http://www.fast.ai/ useful. Depending on your learning style, their courses can either be amazing or somewhat annoying. Their library includes jupyter notebooks so that you can work through the examples.

Hey! I'd suggest starting with this excellent course: https://www.coursera.org/learn/machine-learning And then dive into competitions on kaggle.com. Then following up with the deeplearning.ai specialization here: https://www.coursera.org/specializations/deep-learning and the http://www.fast.ai/ courses. You'll be up to speed in no time :)

For now, I'd recommend you start the two courses on: http://fast.ai/ They will guide you through machine learning and deep learning basics without being overwhelming with math. Good luck!

I'll give another recommendation for http://www.fast.ai/ I went through the first phase of the course as an intro to AI/DL and thought it was really great from a high-level perspective. If you have a decent understanding of Python you'll have a working model running on AWS within the first few hours of the course which is very rewarding. It does a better job than I expected explaining the underlying intuition of the math, but doesn't dive deep into the actual formulas. There are obviously tradeoffs to this approach and if you want to continue in the field you'll need to do something to fill in this background, but as far getting your hands dirty and understanding the basics I really liked the fast.ai approach.

Another resource worth mentioning is the fast.ai course: http://www.fast.ai/

You could try http://fast.ai They show all the steps necessary to train an image classifier (for cats vs. dogs, but it should be possible to adapt to other classes easily) and the explanations are excellent. But if you are trying to do OCR on complete texts, maybe even handwritten, you're getting close to research-level problems very quickly.

Try http://www.fast.ai/ Probably this is the best course form non-mathematical side.

Watch the very first 1 or 2 videos from http://fast.ai . They show how to do this in about seven lines of python and five minutes worth of model training.

Thanks! I saw your earlier question related to working with AI at FAANG, from the context of being 30ish, and self-taught. I was around 27 when I started learning software engineering and AI. I made a free e-book here (https://emilwallner.gumroad.com/l/no-ml-degree), and I believe most of the key points are still valid, however, when I learned software engineering and ML, ML was a rather small field and tools like chatGPT and claude didn’t exist. Imo, asking about a curriculum is the wrong framing, for me, it was more about how to find resources to focus full-time and being in an environment that increases my motivation. I started learning software engineering at home taking courses, but I procrastinated too much to be effective, maybe I did around 10 hours of effective learning per week. For me, studying C at 42 (https://www.42network.org/42-schools/), a free peer-to-peer school was crucial, and I recommend something similar. It enabled me to focus 70-90 hours a week, and after 6 months I was good enough to get competitive startup job offers. During my time, the FastAI course (https://www.fast.ai/) was the best practical AI course. I'd probably spend a week looking for ambitious projects made by recent autodidacts, and ask them which course they think is best now. And spend max 1-2 months taking the course. As for picking projects and building a portfolio, the advice in my e-book is still valid. An ambitious but realistic timeframe for landing a FAANG job is 3-5 years.…

We have open positions for putting LLMs to use (won't link, not a work account). Seems difficult to hire people with sufficient knowledge of our niche and of LLMs. Whether they will continue to grow is hard to say. Putting open models to productive use is difficult. Like stable diffusion can make a pretty image but can it make the image you want? For example, can it make a 2d video game sprite, can it make the same character consistently in different poses. That's a personal project problem I have but it is the same vibe as LLMs with code. Like they can produce some code, but a more complex algorithm or larger program is tough. I'm not sure if there will be an upper limit on what they can do. Right now it is a lot of engineering to get them to do stuff with no human in the loop. Not a lot of companies are training models. There are probably a number that are trying to integrate OpenAI's API in some way. They can do powerful things and it is a good skill to learn, but it feels like the market right now is for PhDs from top schools or low paid data labelers. Maybe others will have different experience. For resources, check out [1], [2], and [3]. The third being my least favorite, but others like it. 1. https://karpathy.ai/zero-to-hero.html 2. https://deeplearning.ai 3. https://fast.ai

That Tim Urban piece is great. It's also an interesting time capsule in terms of which AI problems were and were not considered hard in 2015 (when the post was written). From the post: > Build a computer that can multiply two ten-digit numbers in a split second—incredibly easy. Build one that can look at a dog and answer whether it’s a dog or a cat—spectacularly difficult. Make AI that can beat any human in chess? Done. Make one that can read a paragraph from a six-year-old’s picture book and not just recognize the words but understand the meaning of them? Google is currently spending billions of dollars trying to do it. Hard things—like calculus, financial market strategy, and language translation—are mind-numbingly easy for a computer, while easy things—like vision, motion, movement, and perception—are insanely hard for it. The children's picture book problem is solved; those billions of dollars were well-spent after all. (See, e.g., DeepMind's recent Flamingo model [1].) We can do whatever we want in vision, more or less [2]. Motion and movement might be the least developed area, but it's still made major progress; we have robotic parkour [3] and physical Rubik's cube solvers [4], and we can tell a robot to follow simple domestic instructions [5]. And Perceiver (again from DeepMind [6]) took a big chunk out of the perception problem. Getting a computer to carry on a conversation [7], let alone draw art on par with human professionals [8], weren't even mentioned as…

I haven't experienced any real issues with GPU availability on Colab, I suggest that you just go ahead and use it and wait with the premature optimization until you actually hit a wall and need it. For general advice focused on beginners and ESPECIALLY practical, cheap and efficient methods and hacks to do DL, I recommend searching in https://www.fast.ai/ and their forums https://forums.fast.ai/ I'll try to search inside fast.ai if there is a more specific link to give. I know that one of their chief pieces of advice has been to use Colab and take advantage of the 300$ free credit you get (per credit card) when signing up to Google Cloud, which you can use for DL. Disclaimer - I'm one of the creators of DagsHub, we created the platform especially to help people like you with the difficulties of managing things like data and model versioning, experiment tracking, labeling, etc. we'd love to have you onboard, and thanks for reading until the end :)

Ah, now that really is random! One idea for sifting parking pages that comes to my mind is using image recognition. I can easily tell when something is a parking page by looking at it, I bet you could get an ML model to do so with pretty good accuracy too. There is a class called fast.ai [1] which I personally found to be an amazing introduction to machine learning / deep learning. The first hour and a half course gets you using their library to make an image classifier that can differentiate dog and cat breeds. I bet by simply gathering training data (sites that are and are not parking pages) and reusing the same architecture used in that first class you could train a model that does a pretty good job of it. Once you could classify pages you could just pre-compute a large list, classify, throwaway all the parking pages, and make the remainder available to your user. 1 - https://www.fast.ai/

Any love for https://www.fast.ai/?

I have also started learning NLP this year. Here is what have followed. If you are completely new to deeplearning I would recommend course from https://www.fast.ai. Since it will teach you the concepts in a more practical way and also the next courses will more theory than practical. For theory on NLP with deeplearning you can follow Stanford course by Christopher Manning. http://web.stanford.edu/class/cs224n/index.html#coursework it will give you a good understanding of how deeplearning is used in a certain area of NLP. But remember deeplearning is one of the techniques for solving NLP problems if you are more interested in Understanding NLP then I would recommend the following book. https://web.stanford.edu/~jurafsky/slp3/ed3book.pdf Although, Stanford course should give you a great high-level understanding of how GPT models work but if you really need to go under the hood then I would suggest after finishing this course you should learn Unsupervised Learning in DeepLearning.

> I love it. It feels like it did back then. I’m chasing the midnight hour and not getting any sleep. I highly recommend this blog post about vibe coding, gambling, and flow. Glad you're having a great time! Just something to consider. https://www.fast.ai/posts/2026-01-28-dark-flow/