But what is a neural network? | Deep learning chapter 1

3blue1brown is currently doing a terrific series on how neural networks work, which nicely compliments this blog post. [VIDEO]

This was very timely for me and for anyone else learning, here are the first few videos of the series: [VIDEO] [LINK] [LINK] (Original video) [LINK]

I'm in the same boat. For long time, I was interested in AI but at the same time intimidated by math. I'm relatively comfortable with discrete mathematics and classical algorithms and at the same time calculus and linear algebra is completely foreign to me. Also, I do not accept way to learn ML without good understanding of core principles behind it. So math is a must. A few months ago, I stumbled upon very amazing YouTube Channel 3Blue1Brown which explains math in very accessible way and at the same time I got feeling that I finally started understanding core ideas behind linear algebra and calculus. Just recently he published 4 videos about deep neural networks: [VIDEO] [LINK] [LINK] [LINK] So my fear of ML was gone away and I'm very excited to explore whole new world for neural networks and other things like support vector machines etc

[VIDEO] 3Blue1Brown’s playlist on neural networks. Like other 3Blue1Brown videos, the visualisations and explanations of the math/concepts are so amazing, they give you the intuition behind the neural networks naturally.

Yes, while I haven't done anything with ML myself, I heard via the 3Blue1Brown video on Deep Learning[0] that the sigmoid function isn't really that used anymore. But I figured allowing that being able to tweak the dynamic range could make it a good fit for the rescaling and recentering approach. You know what, I'll just go ahead and post a link to this article on the Unum google group, perhaps someone there can add some thoughts[1]. [0] [VIDEO] [1] [LINK]

The Stanford course is pretty good. [LINK] For something quicker & less applied, maybe the 3b1b series on neural networks: [VIDEO]

3blue1brown videos are probably the best equivalent. Maybe a little basic? But really, all his math videos are just ridiculously good quality. [VIDEO]

I can partly understand some this based on an EE101-level control theory, a High School level model of how neurons work, and 3blue1brown's intro to neural nets[1]. However, I have a strong personal interest in developing a much deeper understanding of dopamine neurons and their role in Executive Function. Can anyone recommend a good curriculum which can take a random web developer from "Knows what a myelinated axon, a sigmoid function, and a feedback loop are" to having a solid enough background to dive into the research on this? [1] [VIDEO]

If you want an extensive answer, I recommend the neural network playlist on 3Blue1Brown: [VIDEO] For a quick answer, watch this segment: [LINK]

Here's a 4-part video series by 3blue1brown : [VIDEO] Explains the big picture in an intuitive way

I think FastAI lesson 3 in "Practical Deep Learning for Coders", has one of the most intuitive buildups of gradient descent and loss that I've seen. * Lecture [1] Book Chapter [2] It doesn't go into the math but I don't think that's a bad thing for beginners. If you want mathematical, 3blue1brown has a great series of videos [3] on the topic. [1] [LINK] [2] [LINK] [3] [VIDEO] * I've been messing around with this stuff since 2016 and have done a few different courses like the original Andrew Ng course and more.

Essentially a computer neural network is just a lot of addition (and matrix multiplication) of floating point numbers. The parameters are the "strength" or "weights" of the connections between neurons on different layers and the "bias" of each neuron. If neuron Alice is connected to neuron Bob and Alice has a value of 0.7, and the weight of Alice's connection to bob is 0.5, then the value sent from Alice to Bob is 0.35. This value (and the values from all the other incoming connections) are summed at added to the neuron's negative bias. I highly recommend checking out 3blue1brown series on how neural nets, gradient descent, and the dot product (implemented as a matrix multiplication) all tie together: [VIDEO]

If a human brain doesn’t use maths, what does it use? It’s Magic? > "training", more accurately described as references Uh, no, that's not at all how the training process works. The training process very roughly mirrors evolution/natural selection except more more direct and faster. This is a good introduction to the concept of how a computer could learn: [LINK] This is a more technical look at how exactly it works in more modern AIs: [VIDEO] (it's a series) > If it encounters a problem it hasn't been programmed for, it cannot reason a new solution. But an LLM can? It's not as good at it as a human but it can Relevant: [LINK] (if you don’t want to click it it says “referring to AI models as "just math" or "matrix multiplication" is as uselessly reductive as referring to tigers as "just biology" or "biochemical reactions"”) > By your logic, video game characters must use reasoning, when no, they don't. It's maths. “By your logic, if tigers are dangerous, then bananas must be too, when no, they aren’t. It’s biology.”

Studying C or Python is studying C or Python. Rather, study AI. Pick up python along the way, skip C. [VIDEO] [LINK] There's tons of book, but I recommend this since it's hands on "Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow" [LINK] [LINK] download some local LLMs and play with them along the way. Good luck.

For me 3brown1blue series: [VIDEO] was an excellent introduction that made Andrej's videos understandable. Then I did 3 first chapters of fastai book, but found it too high level, while I was interested in how things works under the hood. Going through Andrej's makemore tutorials required quite a lot of time but it's definitely worth it. I used free tier of Google Colab until the last one. Pausing the video a lot after he explains what he plans to do and trying to do it by myself was a very rewarding way to learn, with a lot of "aha" moments.

>> YouTube has always been a mixed bag As an outsider but avid reader of this stuff linked from HN, I would recommend the channel 3blue1brown. He's got several NN and AI related videos, and the couple I've seen were pretty good. [VIDEO]

If anyone wants to delve into machine learning, one of the superb resources I have found is, Stanfords "Probability for computer scientists"( [LINK] ). It delves into theoretical underpinnings of probability theory and ML, IMO better than any other course I have seen. (Yeah, Andrew Ng is legendary, but his course demands some mathematical familarity with linear algebra topics) And of course, for deep learning, 3b1b is great for getting some visual introduction ( [VIDEO] ).

A while ago I had caught up with the basics thanks to the legendary 3blue1brown and his playlist on Neural Networks: [VIDEO] and other helpers like Artem Kirsanov: [LINK]

They can , sure...that's really just LLMs though. ML models to recognize handwriting have existed way before LLMs could call tools, though Identifying digits is like the "Hello World!" of ML [VIDEO]