Archive 124 issues
#27 Foundation PRO edition
Introduction to Deep Learning
#26 Foundation PRO edition
Linear Regression, Mathematically Explained
#25 Training PRO edition
Efficient Optimization Algorithms: Mathematically Explained
#24 Training PRO edition
Optimization Algorithms in Deep Learning: Mathematically Explained
#23 Training PRO edition
Mathematics of Recurrent Neural Networks: Backpropagation
#22 Architecture PRO edition
Recurrent Neural Networks, Explained Mathematically
#21 Foundation PRO edition
Deep Learning Common Terms and Misconceptions
#20 Architecture PRO edition
Convolutional Neural Networks, Explained Mathematically
#19 Architecture PRO edition
Transformers and the Attention Mechanism
#18 Foundation
MLP Learning Process: The Math Behind Deep Learning
#17 Foundation
Multilayer Perceptrons: The Math Behind Deep Learning
#16 Foundation
Deep Unsupervised Learning: Autoencoders
#15 Architecture
Recurrent Neural Networks, Revisited
#14 Architecture
Modern Convolutional Neural Network Architectures
#13 Foundation
Unsupervised Machine Learning: Clustering
#12 Training
Batch Normalization
#11 Foundation
Neural Networks in Pytorch
#10 Foundation
Supervised Machine Learning, Simplified
#9 RL
Fundamentals of Reinforcement Learning
#8 Architecture
Recurrent Neural Networks, Simplified
#7 Training
Regularization in Deep Learning
#6 Training
Hyperparameter Tuning for Deep Learning
#5 Training
Optimizers in Deep Learning: A simple explanation
#4 Architecture
Convolutional Neural Networks, Simplified