Learning path 03
Deep Learning & Neural Networks
Move from the perceptron to networks that learn representations for images, sequences, language, and transfer tasks.
Recommended progression
Start with perceptrons and feedforward networks, then study CNNs and transfer learning before sequence models, LSTMs, Seq2Seq, and attention.
Prerequisites
Complete the core machine-learning path or bring equivalent familiarity with vectors, loss functions, training loops, and Python.
Published tutorials
Attention Mechanisms: Let Models Focus Where It Matters
Toy Question–Answer Dataset → Customer Support Chatbots Why Attention Mechanisms? In long sequences, a model easily “forgets” early tokens or crams everything into a…
Seq2Seq Models: From Sentences to Sentences
Machine Translation Toy Dataset → Google Translate Why Seq2Seq Models? Machine translation is the canonical “sequence-in, sequence-out” problem: given a source sentence (e.g., English),…
LSTM (Long Short-Term Memory): Remembering What Matters in Sequences
Next-Word Prediction → Chat Autocomplete Why LTSM? If you’ve ever watched your phone suggest the next word as you type, or your IDE predict…
Recurrent Neural Networks (RNNs): Memory for Language
Text Sentiment Classification → Social Media Sentiment Tracking Why RNN? Customer reviews, tweets, and comments arrive as sequences of words where order matters. Recurrent…
Transfer Learning: Standing on Giant Shoulders
Pretrained ResNet on a Small Dataset → Medical Imaging Analysis Why Transfer Learning? When your dataset is small (hundreds to a few thousand images),…
Convolutional Neural Networks (CNNs): From Pixels to Decisions
CIFAR-10 Image Classification → Manufacturing Defect Detection Why CNNs? If your data is made of pixels—product photos, x-rays, traffic cameras, satellite images—CNNs are the…
Feedforward Neural Networks: From Pixels to Predictions
MNIST Handwritten Digits → Bank Check Digit Recognition Why this concept matters Feedforward neural networks (a.k.a. multilayer perceptrons, MLPs) are the canonical “hello world”…
Perceptron: The Linear Building Block of Neural Networks
AND/OR Logic Gate → Early AI Classifiers Why this concept matters The perceptron is the simplest neuron you can build: it takes a vector…