Deep Learning with PyTorch

Deep Learning with PyTorch

A comprehensive, hands-on curriculum for mastering PyTorch and deep learning fundamentals.

By Shaswat Gupta — Email

View Deep Learning with PyTorch Course

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Why PyTorch?

PyTorch is the foundation of modern deep learning research and production. This course bridges the gap between theory and practice—helping you:

• Build and optimize neural networks from scratch
• Design robust data pipelines for real-world datasets
• Understand and extend PyTorch’s computational graph and autograd
• Deploy models with production-ready training infrastructure

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What’s Inside

Six progressive modules, each with runnable code, best practices, and real-world examples:

• Tensors - Core operations, memory management, GPU acceleration
• Data Pipeline - Custom datasets, DataLoader patterns, production data flows
• Model Architecture - MLPs, CNNs, attention, modular design
• Loss & Optimization - Loss selection, custom losses, training strategies
• Autograd - Computational graphs, custom gradients, debugging
• Production Training - Checkpointing, logging, distributed training

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Course Structure

1. Tensors /Tensors

• Creation, manipulation, broadcasting, device management
• Memory optimization, GPU acceleration, autograd integration
• 100+ exercises with solutions

# Tensor operations fundamentals
x = torch.randn(3, 4, device='cuda')
y = torch.softmax(x, dim=-1)

2. Data Pipeline /Data

• Map-style vs Iterable datasets, lazy/eager loading
• DataLoader batching, multiprocessing, transforms
• Production-ready patterns and error handling

class SmartDataset(Dataset):
    def __init__(self, root_dir, strategy='auto'):
        self.strategy = self._detect_loading_strategy(root_dir)
    def __getitem__(self, idx):
        return self._load_with_retry(idx, max_attempts=3)

3. Model Architecture /Model

• MLPs, CNNs, attention mechanisms, modular components
• Transfer learning, normalization, pooling

class ModernCNNWithAttention(nn.Module):
    def __init__(self, num_classes, use_attention=True):
        super().__init__()
        self.extractor = self._build_backbone()
        self.attention = SpatialAttention(256) if use_attention else nn.Identity()
        self.classifier = self._build_classifier(num_classes)

4. Loss & Optimization /Loss

• Loss selection frameworks, custom losses, label smoothing
• Learning rate scheduling, gradient management

def get_loss_function(task_type, class_weights=None, label_smoothing=0.1):
    if task_type == 'classification' and class_weights is not None:
        return nn.CrossEntropyLoss(weight=class_weights, label_smoothing=label_smoothing)
    return _get_default_loss(task_type)

5. Autograd /Autograd

• Forward/backward passes, gradient accumulation
• Custom autograd functions, graph visualization

def analyze_computation_graph(model, input_tensor):
    output = model(input_tensor)
    make_dot(output, params=dict(model.named_parameters())).render("computation_graph")

6. Production Training /Production

• Training loops, checkpointing, logging, reproducibility
• Mixed precision, distributed training, monitoring

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Learning Paths

Beginner (2-3 weeks):

• Tensors, Data, Model basics, Loss fundamentals

Intermediate (3-4 weeks):

• Advanced models, custom losses, autograd, scalable training

Expert (4-6 weeks):

• Performance optimization, research extensions, MLOps integration

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Key Features

• 100% Tested Code - Every example runs out of the box
• Progressive Complexity - From basics to advanced concepts
• Production Ready - Industry best practices throughout
• Interactive Learning - Jupyter notebooks, quizzes, challenges
• Comprehensive Coverage - Everything needed for real projects
• Modern PyTorch - Latest features and patterns (PyTorch 2.0+)

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What You’ll Build

By the end of this course, you’ll have implemented:

• Flexible MLP and CNN architectures with attention and regularization
• Production data pipelines with error handling and optimization
• Custom loss functions for specialized tasks
• Complete training infrastructure with checkpointing and monitoring
• Memory-efficient models ready for deployment

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Quick Start

Prerequisites

pip install torch torchvision torchaudio matplotlib jupyter

Run Your First Example

python Tensors/tensors.py         # Test tensor operations
python Data/example.py           # Custom dataset demo
python Model/practice_CNN.py     # CNN with attention

Interactive Learning

jupyter notebook Tensors/practice.ipynb   # Launch tensor practice notebook
python -m pytest Data/quiz.md --doctest-modules  # Complete the quiz challenges

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Contributing

This course is a living resource. Contributions welcome!

• Bug Reports: Open an issue with reproducible code
• Documentation: Improve explanations, add examples
• Code: Add new architectures, optimize implementations
• Exercises: Create new practice problems and solutions

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License

Released under the MIT License. See LICENSE for details.

View PyTorch: Zero to Hero Course

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Build real-world deep learning skills with modern PyTorch.