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What's a good next step after learning "classical" neural nets, i.e. backpropagation ANNs? I've been working with MNIST with C and CUDA with dynamic parallelis
by uses 5y ago
What's a good next step after learning "classical" neural nets, i.e. backpropagation ANNs?
I've been working with MNIST with C and CUDA with dynamic parallelism for a couple months and it's been extremely enlightening but I'm kind of ready to move on.
CNNs maybe?
- nefitty 5y agoWhat would you recommend for a complete beginner? I found these lectures that have helped me start sketching out the field in my mind, but it's still difficult: https://www.davidsilver.uk/teaching/ https://www.davidsilver.uk/teaching/
- jstx1 5y agoDavid Silver's lectures are on reinforcement learning which is different from deep learning; I definitely wouldn't start there. One good starter book is Hands-On ML by Aurelien Geron - https://learning.oreilly.com/library/view/hands-on-machine-learning/9781492032632/ https://learning.oreilly.com/library/view/hands-on-machine-l... but there's tonnes of others (and the recommendations will kind of vary based on your background).
- nefitty 5y agoSweet, that helps. Thank you!
- ausbah 5y agolinear regression is what most classes start with
- nefitty 5y agoThanks! I'll look into this.
- mcbuilder 5y agoCNNs are a classic class of ANNs, still widely used in computer vision applications. You will want to look into the transformer architecture though after that, as they are all the rage these days, especially in NLP tasks.
- jstx1 5y agoCNN, RNN, generative models, autoencoders, transformers