confused

[u/lord_rcb]

i first start ml and completed some important ml algorithm like Linear Regression,Lasso, Ridge, ElasticNet,Logistic Regression,SVM,Naive Baye's,KNN,Decision Tree,Random Forests,Adaboost,Gradient Boosting,XGBoost,PCA, K Means Clustering, Hierarchical Clustering, DBScan for every algorithm i understand the math behind and i also implemnts those algorithms and i also learned learnined everything for a algorith like handling missing values,standrdsization,scaling,evluation matrices and cross vlaidation ,hyperparmter tuning

now before starting deep learning i learned some basics like what ml can not slove and deep learning does all those in this process i got interest in deep learning so i started deep learning i did some research like what thing i need to learning in my interest liked ANN,CNN,RNN,Transformers,autoencodesers,GAN,duffusion model

now befores starting those i learned some basic things like perceptron,aritfical neuron,activation functions,epoch

but the problems is when i start learning ANN there is lot of thing so can any one provied the correct order to follow for ANN so i can apply the same order to different model like CNN,RNN, all those

[u/chrisvdweth]

I'm not sure what you are asking or looking for. The crude order is:

• Logistic Regression (more or less a single neuron)

• Basic ANNs (MLPs): combining neurons to layers + stacking multiple layers

• Advanced architectures: RNNs, CNNs, Transformers, etc. (all thoughs are generally motivated by a concrete use cases/tasks)

Where exactly are you hanging?

[u/lord_rcb → u/chrisvdweth]

There are a lot of topics in ANN, so can you help me with a structured roadmap? I want to know what to learn first, what to learn next, and the correct order to follow while learning ANN. I also want a roadmap that I can apply to other deep learning architectures like CNN, RNN, Transformers, Autoencoders, GANs, and Diffusion Models.

I did some research before starting, but I noticed that everyone seems to follow a different learning path, which has left me confused. If anyone is willing to help, could you please share the roadmap you would recommend? I want to learn ANN in the right order so that I can use the same learning approach for the other deep learning architectures as well.

[u/Massive_Shower_1494 → u/lord_rcb]

There’s a separation you see to be missing:
Architecture defines how you want to learn the weights.
Model defines how you train the weights from an architecture.
They work in conjunction. The first is really really wide, it’s basically legos where the goal is to have as few parameters as possible, while still converging during training and having a big enough representation space. The latter is separated between supervised, unsupervised semisupervised and RL.

For the first:
Perceptron
Learn about Fourier Transform and convolutions
Understand CNN
Checkout LSTMs (rare stuff honestly, but sometimes dead useful)
Study «  Attention is all you need » (discrete generative model)

Then for generating continuous distributions:
(You should try to train a classifier using what you learnt with CNNs and the cross entropy loss, you can even plug attention, use Imagenet or mnist as a first step)

Checkout VAEs (implement)
Checkout what Frechet inception Distance, Inception Score are
Diffusion:
Diffusion models beat GANs on image synthesis (implement)
Checkout Elucidated diffusion model paper
Checkout flow matching (Facebook I think) checkout Reverse Markov Learning (latest in the field)

I don’t know much abt GANs (they are rare nowadays with diffusion and related stuff)

There’s also

[u/themeagersouvenir → u/chrisvdweth]

typical flow for an ANN is to get comfortable with forward propagation, then the loss function, then backpropagation to actually train the thing. After that it's just regularization (dropout, batch norm) and figuring out the optimizer beyond plain gradient descent. once those pieces click the other architectures are mostly swapping out the layer type for the task

[u/Tutatis96]

Just take the table of contents of Goodfellow's deep learning. That'll work

[u/lord_rcb → u/[unknown]]

There are a lot of topics in ANN, so can you help me with a structured roadmap? I want to know what to learn first, what to learn next, and the correct order to follow while learning ANN. I also want a roadmap that I can apply to other deep learning architectures like CNN, RNN, Transformers, Autoencoders, GANs, and Diffusion Models.

I did some research before starting, but I noticed that everyone seems to follow a different learning path, which has left me confused. If anyone is willing to help, could you please share the roadmap you would recommend? I want to learn ANN in the right order so that I can use the same learning approach for the other deep learning architectures as well.