Welcome to the second set of flashcards designed to help you build or revise your machine learning (ML) knowledge whenever needed. If you missed the first set covering Reinforcement Learning fundamentals, it's a great place to start. And the timing couldn’t be better – over the last two days, two Nobel Prizes (in Chemistry and Physics) were awarded for achievements rooted in Deep Learning! That’s why today’s flashcards dive into this crucial subset of machine learning.
As a mother of five, I always think about how I would explain these concepts to my kids. So you’ll find two explanations: one for adults and one for “explain me as if I was five”.
In these cards, we walk through the main types of deep learning you are likely to meet in modern ML: feedforward neural networks (FNNs), multilayer perceptrons (MLPs), convolutional neural networks (CNNs), recurrent neural networks (RNNs), long short-term memory networks (LSTMs), generative adversarial networks (GANs), autoencoders and variational autoencoders (VAEs), diffusion models, and transformer models with self-attention. The point is not to memorize every acronym. It is to understand what kind of data each architecture was built to handle, what problem it made easier, and why many newer AI systems combine several of these ideas instead of treating them as isolated families.
Nobel Prize part
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📚 AI 101 Concepts series: #1 Reinforcement Learning & Deep Learning · #2 Types of Deep Learning · #3 RLHF, RLAIF, RLEF, RLCF · #4 Supervised & Unsupervised Learning · #5 AI Learning Techniques
You can get it too →
Bonus: Resources
On the Origin of Deep Learning by Haohan Wang and Bhiksha Raj
Neural Networks for Pattern Recognition by Christopher Bishop
ImageNet Classification with Deep Convolutional Neural Networks by Alex Krizhevsky, Ilya Sutskever and Geoffrey E. Hinton
Recurrent Neural Networks (RNNs): A gentle Introduction and Overview by Robin Marc Schmidt
Long Short Term Memory by Sepp Hochreiter and Jurgen Schmidhuber
Auto-Encoding Variational Bayes by Diederik P. Kingma and Max Welling
Generative Adversarial Nets by Ian J. Goodfellow
Deep Unsupervised Learning using Nonequilibrium Thermodynamics by Jascha Sohl-Dickstein et al.
Attention Is All You Need by Ashish Vaswani et al.
Nobel Prize Part
Boltzmann Machines: Constraint Satisfaction Networks that Learn by Geoffrey Hinton et al.
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