Transformers & Self-Attention: Simple Complete Guide
Understand Transformers and Self-Attention without heavy math. Learn how models process words in parallel, how Query, Key, and Value work, and why Transformers power modern LLMs.
Kulbhushan Pukale
RNNs, LSTMs, and GRUs: Sequential Deep Learning Guide
Master sequential neural networks. Learn how Recurrent Neural Networks process time-series and text, why vanishing gradients crippled early RNNs, and how LSTM and GRU gates solve long-term memory.
Kulbhushan Pukale
Convolutional Neural Networks (CNNs): Architecture Guide
Understand how Convolutional Neural Networks see the world. Explore convolution kernels, stride, padding, pooling layers, receptive fields, and classic architectures like ResNet.
Kulbhushan Pukale
Batch Norm vs. Layer Norm: Deep Learning Normalization
Compare Batch Normalization and Layer Normalization. Learn the mathematical differences across tensor axes, why CNNs rely on BatchNorm, and why Transformers require LayerNorm for stability.
Kulbhushan Pukale
Vanishing and Exploding Gradients: Deep Learning Guide
Understand why deep neural networks fail to train due to vanishing and exploding gradients. Explore backpropagation chain rule math, activation functions, normalization, and residual fixes.
Kulbhushan Pukale
ML Optimizers: Momentum, RMSProp, and Adam Guide
Discover how neural networks navigate loss landscapes. Compare Momentum, RMSProp, and Adam, learn first and second moment math, and select the optimal optimizer for your ML and deep models.
Kulbhushan Pukale
Gradient Descent, SGD, Mini-Batches & Learning Rates
Demystify ML optimization. Compare Batch Gradient Descent, Stochastic Gradient Descent (SGD), and Mini-Batch SGD. Learn learning rate dynamics, loss landscapes, and momentum acceleration.
Kulbhushan Pukale
K-Means Clustering and PCA: Unsupervised ML Essentials
Uncover patterns in unlabelled data. Learn how K-Means partitions feature spaces, how PCA compresses high-dimensional vectors, and how to combine clustering and dimensionality reduction.
Kulbhushan Pukale
k-NN, SVMs, and Naive Bayes: Three Classical ML Pillars
Compare three foundational algorithms: k-NN, Support Vector Machines with the kernel trick, and Naive Bayes. Learn their mechanics, mathematical intuition, assumptions, and practical tradeoffs.
Kulbhushan Pukale