PyTorch to Transformer Roadmap (Beginner to Advanced)

This roadmap is designed to take you from PyTorch fundamentals to building and understanding Transformer models. It covers tensors, neural networks, CNNs, sequence models, attention mechanisms, and finally the Transformer architecture.

Phase 1: PyTorch Fundamentals

Module 1: Introduction to PyTorch

Topics

• What is PyTorch?
• Why PyTorch?
• PyTorch Ecosystem
• Installation (CPU & GPU)
• CUDA Support
• Verify GPU
• PyTorch Workflow
• Tensor vs NumPy Array
• Dynamic Computation Graph

Practice

• Install PyTorch
• Verify GPU
• Create First Tensor

Module 2: Tensor Basics

Topics

• Tensor Creation
• Tensor Data Types
• Tensor Shape
• Tensor Dimension
• Tensor Rank
• Tensor Attributes
• Scalar
• Vector
• Matrix
• Higher-Dimensional Tensor
• Tensor Initialization
• Random Tensor
• Ones
• Zeros
• Identity Matrix

Practice

• Create Different Tensors
• Tensor Shape Exercises

Module 3: Tensor Operations

Topics

• Arithmetic Operations
• Broadcasting
• Matrix Multiplication
• Dot Product
• Element-wise Operations
• Reduction Operations
• Concatenation
• Stack
• Split
• Reshape
• View
• Flatten
• Unsqueeze
• Squeeze
• Permute
• Transpose

Practice

• Matrix Calculator
• Tensor Manipulation

Module 4: Tensor Indexing

Topics

• Indexing
• Slicing
• Boolean Masking
• Advanced Indexing
• Tensor Selection
• Tensor Assignment

Practice

• Image Cropping
• Row and Column Selection

Module 5: CPU and GPU

Topics

• CUDA
• Device Management
• Moving Tensor to GPU
• GPU Memory
• Multi-GPU Basics

Practice

• CPU vs GPU Speed Comparison

Phase 2: Automatic Differentiation

Module 6: Autograd

Topics

• Computational Graph
• requires_grad
• Gradient
• backward()
• Gradient Accumulation
• detach()
• no_grad()
• stop_gradient
• Leaf Tensor

Practice

• Linear Regression from Scratch

Phase 3: Neural Networks

Module 7: torch.nn

Topics

• nn.Module
• Forward Method
• Parameters
• Buffers
• Sequential
• Custom Layers
• Parameter Initialization

Practice

• Build First Neural Network

Module 8: Activation Functions

Topics

• Sigmoid
• Tanh
• ReLU
• Leaky ReLU
• GELU
• ELU
• Softmax
• LogSoftmax

Practice

• Compare Activation Functions

Module 9: Loss Functions

Topics

Regression

• MSELoss
• L1Loss
• Huber Loss

Classification

• CrossEntropyLoss
• BCE
• BCEWithLogitsLoss
• NLLLoss

Practice

• Binary Classification
• Multi-class Classification

Module 10: Optimizers

Topics

• Gradient Descent
• SGD
• Momentum
• RMSProp
• Adam
• AdamW
• Learning Rate
• Weight Decay

Scheduler

• StepLR
• CosineAnnealingLR
• ReduceLROnPlateau
• OneCycleLR

Practice

• Optimizer Comparison

Phase 4: Data Pipeline

Module 11: Dataset & DataLoader

Topics

• Dataset
• TensorDataset
• Custom Dataset
• DataLoader
• Batch Size
• Shuffle
• Sampler
• num_workers
• pin_memory
• collate_fn

Practice

• Custom Image Dataset

Module 12: Data Augmentation

Topics

• torchvision.transforms
• Resize
• Normalize
• RandomCrop
• RandomFlip
• Rotation
• ColorJitter
• RandomErasing

Practice

• Image Augmentation Pipeline

Phase 5: Complete Training Pipeline

Module 13

Topics

• Training Loop
• Validation Loop
• Testing Loop
• Metrics
• Accuracy
• Precision
• Recall
• F1 Score
• Model Saving
• Checkpoints
• Early Stopping

Practice

• MNIST Classifier

Phase 6: Computer Vision

Module 14: CNN Fundamentals

Topics

• Convolution
• Kernel
• Filter
• Feature Map
• Padding
• Stride
• Pooling
• BatchNorm
• Dropout

Practice

• CNN from Scratch

Module 15: CNN Architectures

Topics

• LeNet
• AlexNet
• VGG
• GoogLeNet
• ResNet
• DenseNet
• MobileNet
• EfficientNet

Practice

• CIFAR-10 Classification

Module 16: Transfer Learning

Topics

• Pretrained Models
• Freeze Layers
• Fine Tuning
• Feature Extraction

Practice

• Cats vs Dogs Classification

Phase 7: Sequence Models

Module 17: Sequence Data

Topics

• Sequential Data
• Time Steps
• Sequence Length
• Padding
• Masking
• Tokenization
• Vocabulary

Practice

• Word Tokenizer

Module 18: Word Embeddings

Topics

• One-Hot Encoding
• Embedding Layer
• Word2Vec
• GloVe
• FastText
• Learned Embeddings

Practice

• Train Embedding Layer

Module 19: Recurrent Neural Networks

Topics

• RNN
• Hidden State
• Vanishing Gradient
• Exploding Gradient

Practice

• Character Prediction

Module 20: LSTM

Topics

• Cell State
• Forget Gate
• Input Gate
• Output Gate
• Bidirectional LSTM

Practice

• Sentiment Analysis

Module 21: GRU

Topics

• Reset Gate
• Update Gate
• Bidirectional GRU

Practice

• Text Classification

Phase 8: Encoder-Decoder Models

Module 22

Topics

• Seq2Seq
• Encoder
• Decoder
• Teacher Forcing
• Greedy Decoding
• Beam Search

Practice

• Machine Translation

Phase 9: Attention Mechanism

Module 23

Topics

• Why Attention?
• Attention Score
• Alignment
• Context Vector
• Additive Attention
• Dot Product Attention
• Scaled Dot Product Attention

Practice

• Attention Visualization

Module 24: Multi-Head Attention

Topics

• Query
• Key
• Value
• Self Attention
• Cross Attention
• Multi-Head Attention
• Attention Mask

Practice

• Implement Multi-Head Attention

Module 25: Positional Encoding

Topics

• Why Positional Encoding?
• Sinusoidal Encoding
• Learned Positional Encoding
• Position Embeddings

Practice

• Build Positional Encoding Layer

Phase 10: Transformer Architecture

Module 26: Transformer Basics

Topics

• Transformer Overview
• Encoder Stack
• Decoder Stack
• Residual Connection
• Layer Normalization
• Feed Forward Network
• Skip Connections

Practice

• Draw Transformer Architecture

Module 27: Transformer Encoder

Topics

• Encoder Block
• Self Attention
• Feed Forward Layer
• LayerNorm
• Residual Connections

Practice

• Implement Transformer Encoder

Module 28: Transformer Decoder

Topics

• Masked Self Attention
• Cross Attention
• Decoder Block
• Output Projection

Practice

• Implement Transformer Decoder

Module 29: Building a Transformer

Topics

• Build Transformer from Scratch
• Training Pipeline
• Mask Generation
• Token Embeddings
• Position Embeddings
• Output Layer

Practice

• English-to-French Translator

Module 30: PyTorch Transformer APIs

Topics

• nn.Transformer
• TransformerEncoder
• TransformerEncoderLayer
• TransformerDecoder
• TransformerDecoderLayer
• MultiheadAttention
• Embedding Layer
• LayerNorm
• Dropout

Practice

• Text Translation with nn.Transformer

Phase 11: Mini Projects

Beginner

• Tensor Calculator
• Linear Regression
• MNIST Digit Recognition
• Fashion-MNIST Classifier

Intermediate

• CIFAR-10 Image Classification
• Cats vs Dogs Classification
• Sentiment Analysis (LSTM)
• Text Classification (GRU)

Advanced

• Machine Translation (Seq2Seq + Attention)
• Transformer from Scratch
• English-to-French Translator
• Transformer-based Text Classifier

Learning Flow

textCopy
1PyTorch Introduction
2        ↓
3Tensors
4        ↓
5Tensor Operations
6        ↓
7Autograd
8        ↓
9Neural Networks
10        ↓
11Activation Functions
12        ↓
13Loss Functions
14        ↓
15Optimizers
16        ↓
17Dataset & DataLoader
18        ↓
19Training Pipeline
20        ↓
21CNN Fundamentals
22        ↓
23CNN Architectures
24        ↓
25Transfer Learning
26        ↓
27Sequence Data
28        ↓
29Word Embeddings
30        ↓
31RNN
32        ↓
33LSTM
34        ↓
35GRU
36        ↓
37Seq2Seq
38        ↓
39Attention Mechanism
40        ↓
41Multi-Head Attention
42        ↓
43Positional Encoding
44        ↓
45Transformer Encoder
46        ↓
47Transformer Decoder
48        ↓
49Build Transformer from Scratch
50        ↓
51PyTorch Transformer APIs

This roadmap provides a structured progression from basic PyTorch concepts to implementing a complete Transformer architecture. By the end, you will understand the theory behind Transformers, implement core components such as self-attention and positional encoding, build encoder-decoder models from scratch, and use PyTorch's built-in Transformer modules for real-world natural language processing tasks.