CS231n on YouTube. It’s a little bit older but it has just about everything i’d ask if I was giving you a job interview.

https://arxiv.org/pdf/2201.00650.pdf

They are both regularization techniques, so no, they have the same purpose.

Don’t they have two different purposes? As I understand it: The batchnorm is used to maintain activations along deep neural networks so that non linear activations do not kill the neurons whose probability distributions would have flattened out while a dropout is only meant to train a network uniformly to prevent overfitting.

Dropout is less effective in CNNs and Batch Normalization replaces it.

How is the neural engine invoked in your typical MacBook training session on PyTorch ? Is that what automatically gets used if you train in cpu ? Gpu ?

Kaggle Titanic, we have all been there.

Have a look at FastAI

This is actually the best answer here. Diving straight into DL will pretty quickly demotivate and make it seem like an impossibility shortly after switching from an online toy example to something real world. I can confirm, this is a great book that also includes the necessary basics. Since it was published it has a well deserved space on my office table.

Kaggle seems like a good start. I am exploring it for projects

Corrections:

1. The weights are set to random only at the beginning (i.e. before iter=0). Every iteration onwards, the optimization algorithm (some form of gradient descent) kicks in and nudges the weights slightly in a way to make the whole network perform incrementally better at the task it’s being trained for. After hundreds of thousands of iterations, it is hoped that the weights reach an optimal state, where more nudging does not optimize the weights any further (and by extension it does not make the neural network learn any better). This is called convergence.

2. Coming to your example of path finding, first of all this is a reinforcement learning (RL) problem. RL is different from DL. DL or deep learning is a subset of machine learning algorithms which is mostly concerned with the training of deep neural networks (hence the name). RL is a particular method of training ‘any’ learning algorithm (doesn’t always have to be neural networks) using what are called reward functions. Think of it like training a dog (an agent) to perform tricks (a task) using biscuits (as rewards). Every time your dog does what you ask him to do and then you follow up by giving him a biscuit, you basically ‘reinforce’ his behavior, so he will do more of it when you ask him to do it again.

3. Now, the example of the path finding agent that you gave is silly. No RL agent is trained on one single scenario. If you do train an RL agent on just a single scenario, you get a condition called overfitting, meaning that your agent learns perfectly well on how to navigate that one scenario but it doesn’t generalize to any other unseen scenarios. In practice, we train an RL agent on hundreds of thousands of different scenarios, with each scenario being slightly different from the rest. Many of these scenarios can have different conditions like different lighting, differently structured environment, different geometries and different obstacles, etc. etc. What we hope to achieve is that after training, the RL agent learns a generalized navigation function that is adaptive to any scenario.

I suggest you watch some TwoMinutePapers videos on YT, of some OpenAI’s RL papers. There are some videos in which RL agents learn to fight in a boxing match, and in another one, several agents collaborate to play hide and seek. You’d get a feel for how RL works.

Well you will presumably not be labeling this with humans but probably Astar, so it's all unsupervised learning anyways

Got your point, and have another question: So let continue with path finding example, if we do not give the enough AB pairs the as you said it will fail, But then what about unsupervised learning as there won't be any data given right?

"From what I understand is the repeated iteration will take random weights and at some point those weights will be kinda perfect for the given task (plz correct me if i'm wrong)"

You're at least somewhat wrong - it's not all random. The weights are indeed initialized randomly, but then adjusted to fit batches of training data. The weights are updated to more closely match the data. This is usually done through stochastic gradient descent and leverages the difference between your network's current predictions and the known ground truth as calculated using the chosen loss function (e.g. the mean square error or binary cross-entropy).

https://www.deeplearningbook.org/ "The online version of the book is now complete and will remain available online for free."

It depends. If it only learned A to B we say it is overfit. If you give it enough different A to Bs, it might learn to generalize, and then for any A to B pair it will be able to find the path.

If it learned on paths without obstacles, it will not be able to deal with obstacles. Which means that it will go right through them, or run into them, if your environment does not alloe an agent to go through them.

Read academic papers to find challenges and then dive deep into it. Also deep learning is very broad, narrow it down a bit down the road

Andrew Ng has some great lectures and courses on Coursera.

Maybe start with machine learning first. Projects in machine learning will help. Then go through MOOCs. Then projects again. Then pick your field and then projects again.

I am sure! But i have similar interest dm?

Like?

I guess you can do some project

What? Is there a second slide?

Also I’d imagine that in the reinforcement learning route each ‘entity’ is a separate model which would have steep computational overhead implications or you just use one model in the environment and it is vanilla reinforcement learning which still isn’t very vanilla btw.

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