A simpler path tomachine learning
Follow a clean vertical roadmap from foundations to transformers using the articles already available on DevLifted.
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 ML Intuition
A future bridge from vectors and matrices to model intuition.
Stage 02
Classical ML
Learn strong baselines before moving into deeper models.
TF-IDF + Logistic Regression: The Classical ML Baseline You Should Try First
See how a practical text classification baseline works.
Evaluation Metrics for Beginners
A future article on accuracy, precision, recall, and F1.
Stage 03
PyTorch Core
Understand gradients, autograd, and training loops.
Stage 04
Neural Networks
Move from linear models into hidden layers and learned representations.
From Words to Intelligence: Building an MLP Classifier on Pretrained Sentence Embeddings
See a practical deep learning workflow for text problems.
Backpropagation by Hand
A future article for building stronger intuition.
Stage 05
Transformers
Finish with 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.