A simpler path tomastering AI
Follow structured roadmaps designed to take you from fundamentals to production-ready skills. Choose your path and start learning.
ML & Deep Learning
Master machine learning fundamentals, PyTorch, neural networks, and transformers
Stage 01
Foundations
Start with the core concepts and training vocabulary.
What Is a Tensor? A Beginner's Guide with Real Examples
Understand the basic data structure behind deep learning.
ML Hyperparameters Explained for Beginners: Learning Rate, Epochs, Batch Size, L2, and Seed
Learn the knobs that control model training.
Linear Algebra for Machine Learning: A Complete Intuitive Guide
Complete guide from vectors and matrices to backpropagation and gradients.
Stage 02
Classical ML
Learn strong baselines before moving into deeper models.
Stage 03
PyTorch Core
Understand gradients, autograd, and training loops.
PyTorch Autograd: Automatic Differentiation from the Ground Up
Learn how gradients and computational graphs work.
Logistic Regression from Scratch in PyTorch: Every Line Explained
Build a simple classifier from tensors and updates.
Adam Optimizer Explained: Why It's Better Than Plain Gradient Descent
Why adaptive learning rates work better than plain gradient descent.
Stage 04
Neural Networks
Move from linear models into hidden layers and learned representations.
Backpropagation and the Chain Rule: A Simple Visual Guide
Simple, step-by-step guide to understanding how backprop works.
ReLU Explained: The Simple Activation Function That Changed Deep Learning
Understand the most popular activation function in deep learning.
Batch Normalization Explained: Why Your Neural Network Needs It
Learn how to stabilize and speed up neural network training.
Dropout Explained: The Surprisingly Simple Trick That Prevents Overfitting
Prevent overfitting by randomly turning off neurons during training.
Early Stopping Explained: Knowing When to Stop Training
Know when to stop training to prevent overfitting.
Stage 05
NLP Fundamentals
Learn how to process and understand text with neural networks.
Word Embeddings Explained: From One-Hot to Dense Vectors
From one-hot encoding to dense vector representations.
Text Preprocessing and Tokenization for NLP: A Complete Guide
Prepare text data for neural networks.
Transfer Learning in NLP: Standing on the Shoulders of Giants
Leverage pretrained models for practical NLP tasks.
Understanding Neural Networks: From Word Counting to Meaning Understanding
From word counting to meaning understanding.
From Words to Intelligence: Building an MLP Classifier on Pretrained Sentence Embeddings
See a practical deep learning workflow for text problems.
Stage 06
Sequence Models
Master RNNs and LSTMs for sequential text processing.
Understanding RNNs and LSTMs: The Foundation of Sequence Modeling
Learn how neural networks process sequences.
BiLSTM for Text Classification: Understanding Sequential Deep Learning
Build a complete sequence model from scratch.
Building a BiLSTM Intent Classifier in PyTorch: Vocab, Packing, and Pooling
Master packing, pooling, and efficient sequence processing.
Stage 07
Transformers
Understand the architecture behind modern language models.
Understanding Transformers: The Architecture Behind Modern AI
Get a clean introduction to attention and transformers.
Fine-Tuning Transformers for Classification
A future practical guide for downstream tasks.