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…
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…
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…
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, activat…
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 t…
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 dynami…
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 co…
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, mathemat…
Kulbhushan Pukale