Archive 124 issues
#51 Foundation PRO edition
Supervised ML: Decision Trees
#50 Training PRO edition
Neural Scaling Laws, Simply Explained
#49 Foundation PRO edition
Time Series Analysis, Simply Explained
#48 Multimodal PRO edition
Introduction to Computer Vision (CV)
#47 Safety PRO edition
AI Hallucinations, Simply Explained
#46 LLMs PRO edition
BERT: Bidirectional Encoder Representations From Transformers
#45 Architecture PRO edition
A Visual Guide To Variational Autoencoders (VAEs)
#44 Architecture PRO edition
Diffusion Transformers (DiTs), Simply Explained
#43 Architecture PRO edition
A Simple Guide To RNNs
#42 Foundation PRO edition
Recommender Systems, Simply Explained
#41 Training PRO edition
CNN Backpropagation, Mathematically Explained
#40 LLMs PRO edition
Language Models Are Few-Shot Learners: The Introduction of GPT-3
#39 Training PRO edition
Deep Learning's Top Pitfalls: Why Your Model Struggles (And How to Fix It!)
#38 Architecture PRO edition
Transformers and Self-Attention, Mathematically Explained
#37 Architecture PRO edition
Transformers and Multi-Head Attention, Mathematically Explained
#36 Architecture PRO edition
Neural Architecture Search (NAS)
#35 Training PRO edition
Self-Supervised Learning, Simply Explained
#34 Architecture PRO edition
Convolutional Neural Networks (CNNs) In Pytorch
#33 Foundation PRO edition
Logistic Regression, Mathematically Explained
#32 Architecture PRO edition
Vision Transformers (ViTs)
#31 Architecture PRO edition
Generative Adversarial Networks, Simplified
#30 Training PRO edition
Transfer Learning, Simplified
#29 Training PRO edition
Normalization In Deep Learning
#28 Training PRO edition
All About Activation Functions