From Logistic Regression to Transformers: Learning Path
Are you fascinated by deep learning's transformative power but unsure how to navigate the journey from logistic regression to mastering transformer architectures? You’re not alone. Transformers are the backbone of modern AI, power innovations in natural language processing, computer vision, and beyond, but getting there can feel daunting.
In this blog, I outline a structured, week-by-week learning path that takes you from the foundational concepts of machine learning to building and fine-tuning your transformer models. Whether you're a beginner or looking to deepen your expertise, this roadmap combines key concepts, curated resources, hands-on projects, and practical tips to make your progress achievable and rewarding.
Here's a detailed week-by-week learning path. Each week we will build upon previous knowledge:
Week 1: Linear Models
Topics:
Logistic regression (binary classification)
Cross-entropy loss
Softmax function for multi-class problems
Deep dive into gradient descent variants (SGD, Mini-batch)
Resources:
Project:
Implement logistic regression from scratch using NumPy.
Use
sklearnfor logistic and softmax regression on sample datasets.
Week 2: Neural Network Foundations
Topics:
Single-layer and multi-layer perceptrons
Activation functions (ReLU, tanh)
Forward and backward propagation
Derivation of backpropagation
Resources
Deep Learning" by Ian Goodfellow – Chapter 6 (Feedforward Neural Networks)
TensorFlow Playground to visualize FFNNs
Project
Implement a basic FFNN from scratch (with one hidden layer).
Using a framework like PyTorch or TensorFlow, create a simple feedforward neural network (FFNN) to classify the MNIST digits dataset.
Experiment with different activation functions (ReLU, sigmoid) and compare performance.
Week 3: Deep Neural Networks
Topics:
Multiple hidden layers
Advanced activation functions
Initialization techniques
Basic optimization algorithms (Momentum, RMSprop)
Resources:
FastAI Deep Learning Course Part 1
Neural Networks and Deep Learning by Michael Nielsen
Project:
Image classification on CIFAR-10 with deep neural network
Apply gradient descent with different learning rates and optimizers (SGD, Adam).
Week 4: Advanced Optimization & Regularization
Topics:
Batch normalization
Dropout
L1/L2 regularization
Learning rate scheduling
Resources:
Project:
Build a deep network for sentiment analysis with regularization techniques
Week 5: Sequential Data & RNNs
Topics:
RNN architecture
Backpropagation through time
Vanishing/exploding gradients
LSTM cells
Resources:
Project:
Character-level text generation using LSTM
Week 6: Introduction to Attention Mechanisms
Topics:
Encoder-decoder architecture
Teacher forcing
Beam search
Basic attention mechanisms
Resources:
Project:
Implement Bahdanau (additive) or Luong (multiplicative) attention.
Implement a basic sequence-to-sequence model for translating English to French using Bahdanau attention (use small parallel corpora).
Week 7: Self-Attention and Multi-Head Attention
Topics:
Score functions
Query-Key-Value concept
Self-attention
Dot-product attention vs. additive attention
Resources:
Project:
Manually compute self-attention for a toy example and build a self-attention layer using PyTorch.
Extend the implementation to a multi-head attention mechanism and validate its performance on sequence data.
Week 8: Multi-Head Attention and Positional Encoding
Topics:
Multi-head attention
Positional encodings (sinusoidal functions)
Resources:
Project:
Write code for multi-head attention.
Implement positional encoding and visualize it.
Implement a custom multi-head attention module and add positional encodings. Use this to classify sequences of text (e.g., positive/negative sentiment).
Week 9: Transformers Block (Encoder-Decoder Structure)
Topics:
Encoder and decoder architecture
Residual connections and layer normalization
Resources:
Projects
Build a simple transformer encoder layer.
Build a transformer encoder for a language modelling task using PyTorch or TensorFlow.
Train the encoder on a small text dataset (e.g., Shakespeare sonnets).
Week 10: Full Transformer Model
Topics:
End-to-end implementation of the original transformer
Complete transformer architecture
Resources:
Projects
Implement a transformer-based sequence classification task.
Implement a simplified transformer model from scratch and apply it to text summarization or machine translation.
Use performance metrics (BLEU score for translation, ROUGE score for summarization) to evaluate results.
Week 11: Transformer Variants (BERT, GPT)
Topics:
BERT (masked language modelling)
GPT (causal language modelling)
Resources:
Projects:
Fine-tune a pre-trained BERT or GPT model using HuggingFace.
Implement a chatbot using a GPT model for conversational responses.
Additional Project Ideas
Once you complete the core projects, reinforce your learning with larger, integrative projects:
Sentiment Analysis on Movie Reviews: Use transformers for sentiment classification on the IMDB dataset.
Named Entity Recognition (NER): Implement NER using transformers and fine-tune the CoNLL-2003 dataset.
Question Answering System: Use BERT or RoBERTa to create a question-answering application on a custom dataset.
Week 12: Advanced Techniques and Optimization
Topics:
Model distillation
Reducing memory consumption (efficient transformers)
Resources:
Projects
Experiment with efficient transformer architectures (e.g., Reformer or Longformer) for a custom dataset with long sequences.
Apply model distillation to compress a large transformer model into a smaller, faster one for inference.