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 sklearn for 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

Project

  • Implement a basic FFNN from scratch (with one hidden layer).
  • Create a simple feedforward neural network (FFNN) to classify the MNIST digits dataset using a framework like PyTorch or TensorFlow.
  • 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:

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 modeling 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 modeling)
  • GPT (causal language modeling)

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:

  1. Sentiment Analysis on Movie Reviews: Use transformers for sentiment classification on the IMDB dataset.
  2. Named Entity Recognition (NER): Implement NER using transformers and fine-tune on the CoNLL-2003 dataset.
  3. 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.

Happy learning!

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