Abstract:

Here is the Special Annexure 4: PyTorch Real-World Case Studies — complete, structured, practical, and suitable for inclusion in book as a professional annexure.
These case studies are written in a way that readers can directly apply PyTorch to real-world industry problems.

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Special Annexure 4: PyTorch Real-World Case Studies

Practical Applications Across Industries Using PyTorch

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PyTorch is widely used in real-world industries such as healthcare, automotive, robotics, finance, natural language processing, and computer vision.
This annexure presents five high-impact case studies, each demonstrating how PyTorch was used to build, train, and deploy models for practical applications.

Each case study includes:

• Problem Statement

• Dataset

• Model Architecture

• Implementation Outline

• Key Insights

• Industry Relevance

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Case Study 1: Medical Image Classification for Early Disease Detection

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1. Problem Statement

Develop a PyTorch-based system to detect pneumonia from chest X-ray images, supporting radiologists in early disease diagnosis.

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2. Dataset

• NIH Chest X-ray Dataset or Kaggle Pneumonia Dataset
  Contains labeled X-ray images categorized into:

• Normal

• Pneumonia (Bacterial/Viral)

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3. Model Architecture

A fine-tuned pre-trained model such as:

• ResNet50

• EfficientNet-B0

• DenseNet121

Transfer learning provides high accuracy with limited medical data.

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4. Implementation Outline

1. Image preprocessing

2. Data augmentation (random crop, flip, normalization)

3. Load pretrained CNN

4. Freeze initial layers; train only classifier

5. Train and evaluate using accuracy, F1-score, AUC

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5. Key Insights

• Transfer learning significantly boosts performance for small datasets.

• Grad-CAM helps explain model predictions for clinical trust.

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6. Industry Relevance

Hospitals and medical AI companies use similar models for:

• Radiology triage

• Remote healthcare

• Early disease prediction

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Case Study 2: Object Detection System for Autonomous Vehicles

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1. Problem Statement

Build a PyTorch-based object detection system to identify:

• Pedestrians

• Vehicles

• Traffic signs

• Obstacles

for use in autonomous driving.

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2. Dataset

Common datasets:

• KITTI

• COCO

• BDD100K

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3. Model Architecture

Object detection models:

• YOLOv5

• Faster R-CNN

• SSD

• DETR (Transformer-based)

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4. Implementation Outline

1. Preprocess images (resize, normalize)

2. Use torchvision detection models

3. Train on annotated bounding boxes

4. Evaluate using mAP (mean Average Precision)

5. Deploy using TorchScript for real-time inference

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5. Key Insights

• Real-time performance requires GPU optimization.

• DETR reduces reliance on anchor boxes.

• Data augmentation significantly impacts detection accuracy.

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6. Industry Relevance

Used in:

• Self-driving cars

• Traffic analytics

• Smart CCTV surveillance

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Case Study 3: NLP Text Classification for Customer Support Automation

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1. Problem Statement

Automate customer support ticket classification based on text queries.

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2. Dataset

Custom datasets from:

• Support centers

• E-commerce platforms

• Public datasets like AG News, DBpedia

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3. Model Architecture

NLP Models:

• LSTM / GRU

• Transformer Encoder

• BERT / DistilBERT fine-tuning

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4. Implementation Outline

1. Preprocessing using torchtext (tokenization, vocab)

2. Embedding layer or Transformer embeddings

3. Train classifier

4. Evaluate using precision, recall, F1

5. Deploy with FastAPI

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5. Key Insights

• Transformers outperform traditional RNNs.

• Domain-specific fine-tuning improves accuracy.

• FastAPI + TorchServe provides scalable deployment.

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6. Industry Relevance

Used in:

• Chatbots

• CRM analytics

• Email classification

• Support ticket routing

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Case Study 4: Time Series Forecasting for Financial Markets

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1. Problem Statement

Predict future stock prices using deep learning with PyTorch.

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2. Dataset

• Historical stock prices

• Technical indicators

• Yahoo Finance API (OHLC data)

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3. Model Architecture

• LSTM (Long Short-Term Memory)

• GRU

• Transformer-based Time Series Forecaster

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4. Implementation Outline

1. Prepare sliding window sequences

2. Normalize features

3. Train LSTM predictor

4. Use MSE loss

5. Evaluate using RMSE, MAPE

6. Visualize predictions

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5. Key Insights

• Feature scaling is crucial

• Avoid data leakage

• Transformer models capture long-term dependencies better

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6. Industry Relevance

Used by:

• Hedge funds

• Banks

• Algorithmic trading firms

• Investment research organizations

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Case Study 5: Speech Recognition Using torchaudio

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1. Problem Statement

Build a speech-to-text system similar to basic ASR (Automatic Speech Recognition).

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2. Dataset

• LibriSpeech

• CommonVoice

• Custom microphone recordings

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3. Model Architecture

Models for ASR:

• CNN + RNN hybrid

• Transformer Encoder-Decoder

• Wav2Vec 2.0 Fine-Tuning

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4. Implementation Outline

1. Load audio with torchaudio

2. Convert to spectrogram / Mel Spectrogram

3. Train sequence model

4. Use CTC loss (Connectionist Temporal Classification)

5. Decode output using greedy or beam search

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5. Key Insights

• CTC makes alignment unnecessary

• Spectrogram augmentation improves robustness

• Transformer models outperform traditional RNNs

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6. Industry Relevance

Used in:

• Voice assistants

• Call center automation

• Meeting transcription systems

• Smart home devices

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Case Study 6 (Bonus): Recommendation System Using Embeddings

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Problem Statement

Build a movie recommendation system using user-item interactions.

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Dataset

• MovieLens 100k or 1M

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Model

• User embedding

• Item embedding

• Dot-product similarity

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Pipeline

1. Preprocess interactions

2. Build embedding matrix

3. Train on user-rating data

4. Predict top-N recommendations

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Industry Use

Streaming platforms like:

• Netflix

• Amazon Prime

• Hotstar

use similar recommender architectures.

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