Deep Learning
Posts and notes about deep learning.
Series & Posts
1
Neural networks: the basic building block
2 Forward pass and backpropagation
3 Training neural networks: a practical guide
4 Convolutional neural networks
5 Recurrent neural networks and LSTMs
6 Attention mechanism and transformers
7 Word embeddings: from one-hot to dense representations
8 Transfer learning and fine-tuning
9 Optimization techniques for deep networks
10 Regularization for deep networks
11 Encoder-decoder architectures
12 Generative models: an overview
13 Restricted Boltzmann Machines
14 Deep Belief Networks
15 Variational Autoencoders
16 Generative Adversarial Networks: training and theory
17 DCGAN, conditional GANs, and GAN variants
18 Representation learning and self-supervised learning
19 Domain adaptation and fine-tuning strategies
20 Distributed representations and latent spaces
21 AutoML and hyperparameter optimization
22 Neural architecture search
23 Network compression and efficient inference
24 Graph neural networks
25 Practical deep learning: debugging and tuning