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I don't even have enough knowledge to grasp the first video. Is there a list of knowledge requirements to look at?
If you are struggling with the neural network mechanics themselves, though, I'd recommend just skimming them once and then going back for a second watch later. The high level overview will make some of the early setup work make much more sense in a second viewing.
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https://deeplearningwithpython.io/
Is it wise to start to with deep learning without knowing machine learning?
All the exciting innovation over the past 13 years comes from deep learning mainly in working with images and natural language.
Machine learning is good for tabular data problems, particularly decision trees, that work well to reduce uncertainty for business outcomes, like sales and marketing as one example.
Machine Learning Basics:
Linear regression - Y = Mx + B (predicts a future value) Classification (logistic regression) - Y = 1 / 1 + e^-(b0 + b1x) (predicts probability of a class or future event)
There is a common learning process between the two called gradient descent. It starts with the loss function, that measures the error between predictions and ground truth, where you backpropogate the errors as a feedback signal to update the learned weights which are the parameters of your ml model which is a more meaningful representation of your dataset that you train on.
In deep learning it's more appropriate for perception problems, like vision ,language and time sequences. It gets more complex where you are dealing with significantly more parameters in the millions, that are organized in hierarchical layer representation.
There are different layers for different types of learning representation, Convolutions for Images and RNN for Sequence to Sequence learning and many more examples of layers, which are the basis of all deep learning models.
So there is a small conceptual overlap; but I would say deep learning has a wider variety of interesting applications, is much more challenging to learn, but not impossible by any stretch.
There is no harm in giving it a try and diving in. If you get lost and drown in complexity, start with machine learning. It took me 3 years to grasp, so it's a marathon, not a sprint.
Hope this helps
Saving this
I'm not sure how it compares, but another option is the Hugging Face learning portal [0]. I'm doing the Deep RL Course and so far it's pretty straight forward (although when it gets math heavy I'm going to suffer). [0] - https://huggingface.co/learn
The grading system forces you to write specifically to pass their LLM grading system, terrible design. Maybe its gotten better I had to constantly look up how to write the correct answer just to pass their automatic grading system. Not a good way to learn and time wasted.
Karpathy videos posted here are GOLD.
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You're not alone. At this point I'm starting to recognise some by number as well.
A newly convicted criminal arrived in prison, and on the first night he was puzzled to hear his fellow inmates yelling numbers to each other. "36!" one would yell, and the rest would chuckle. "19!" went another, to uproarious laughter. "50," remarked a third wryly, which provoked groans and ironic cheers. Eventually his cellmate sat up and cried out "114" and it brought the house down. In a lull, he asked his cellmat…
Anyone got recommendations for learning resources for this type of math? Realizing now that I might be a bit behind on my intro-level math.