Deep Learning with PyTorch

Overview

A comprehensive repository of Jupyter notebooks demonstrating how to use PyTorch for various deep learning tasks including image classification, object detection, NLP, and more. Covers data loading, model building, training, evaluation, and advanced techniques.

💻 GitHub: Deep-Learning-PyTorch
📚 Topics: CNNs, RNNs, Transformers, GANs, Transfer Learning
🎓 Level: Beginner to Advanced

Covered Topics

1. Fundamentals

• PyTorch Basics: Tensors, autograd, optimization
• Data Loading: DataLoader, Dataset, transforms
• Model Building: nn.Module, Sequential, custom architectures
• Training Loops: Forward pass, backward pass, optimization

2. Computer Vision

• Image Classification: MNIST, CIFAR-10, ImageNet
• CNN Architectures: LeNet, AlexNet, VGG, ResNet, EfficientNet
• Transfer Learning: Fine-tuning pre-trained models
• Object Detection: YOLO, Faster R-CNN, SSD
• Image Segmentation: U-Net, Mask R-CNN
• GANs: DCGAN, StyleGAN, Pix2Pix

3. Natural Language Processing

• Text Classification: Sentiment analysis, spam detection
• RNNs & LSTMs: Sequence modeling
• Transformers: BERT, GPT, attention mechanisms
• Named Entity Recognition: Token classification
• Machine Translation: Seq2Seq models

4. Advanced Techniques

• Mixed Precision Training: FP16 for faster training
• Distributed Training: DataParallel, DistributedDataParallel
• Pruning & Quantization: Model compression
• Gradient Accumulation: Larger effective batch sizes
• Learning Rate Scheduling: CosineAnnealing, OneCycleLR

Repository Structure

Deep-Learning-PyTorch/
├── 01_Fundamentals/
│   ├── tensor_operations.ipynb
│   ├── autograd_tutorial.ipynb
│   └── custom_datasets.ipynb
├── 02_Computer_Vision/
│   ├── cnn_mnist.ipynb
│   ├── transfer_learning_imagenet.ipynb
│   ├── yolo_object_detection.ipynb
│   └── unet_segmentation.ipynb
├── 03_NLP/
│   ├── rnn_sentiment_analysis.ipynb
│   ├── transformer_translation.ipynb
│   └── bert_fine_tuning.ipynb
├── 04_Advanced/
│   ├── mixed_precision.ipynb
│   ├── distributed_training.ipynb
│   └── model_quantization.ipynb
└── requirements.txt

Sample Code: ResNet Training

import torch
import torch.nn as nn
import torchvision.models as models
import torchvision.transforms as transforms
from torch.utils.data import DataLoader
from torchvision.datasets import CIFAR10

# Load pre-trained ResNet
model = models.resnet50(pretrained=True)
model.fc = nn.Linear(2048, 10)  # CIFAR-10 has 10 classes

# Data augmentation
transform = transforms.Compose([
    transforms.RandomCrop(32, padding=4),
    transforms.RandomHorizontalFlip(),
    transforms.ToTensor(),
    transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])

# Dataset & DataLoader
train_dataset = CIFAR10(root='./data', train=True, transform=transform)
train_loader = DataLoader(train_dataset, batch_size=128, shuffle=True)

# Training loop
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.AdamW(model.parameters(), lr=1e-3)
scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=200)

for epoch in range(100):
    for images, labels in train_loader:
        outputs = model(images)
        loss = criterion(outputs, labels)
        
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()
    scheduler.step()

Tech Stack

Framework: PyTorch, torchvision, torchaudio, torchtext
Visualization: Matplotlib, TensorBoard
Utilities: tqdm, wandb (experiment tracking)
Acceleration: CUDA, cuDNN

Learning Outcomes

✅ Master PyTorch fundamentals and best practices
✅ Implement state-of-the-art architectures from scratch
✅ Apply transfer learning for real-world tasks
✅ Optimize training with mixed precision and distributed computing
✅ Deploy models for production inference

Status: Active Educational Resource
Language: Python
License: MIT