Deep Learning
Deep learning interviews, tutorials, and notes
Einops - Part 1
Introduction to einops - readable tensor operations with rearrange, reduce, and repeat replacing cryptic transposes.
Einops - Part 2: Deep Learning Architectures
Building ConvNets and Multi-Head Attention with einops - comparing traditional PyTorch vs einops implementations.
Environment & Distribution Shift
Notes on covariate shift, label shift, and concept shift - how to detect and correct distribution changes.
Loss Functions
Cross entropy loss explained with NumPy implementation for multiclass classification tasks.
Tensorflow Tutorial
Hands-on TensorFlow v1.0 tutorial - variables, sessions, training algorithms, and building a neural network.
🧑🏻💻 DL Interviews
DL interview prep - maximum entropy, data augmentation, overfitting, and probability of success problems.