PyTorch

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pytorch
Why PyTorch is Important
in Data Science and AI

PyTorch is a leading deep learning framework known for its flexibility, ease of use, and dynamic computation graph.

It supports eager execution, making debugging and experimentation intuitive and Pythonic. PyTorch has become the de facto standard in academic research, especially for AI, ML, NLP, and vision tasks.

  • It powers production-ready systems with TorchScript, ONNX export, and mobile deployment capabilities.

  • PyTorch integrates seamlessly with Python libraries like NumPy, Pandas, and Scikit-learn for end-to-end ML workflows.

  • It supports automatic differentiation and GPU acceleration, making model training efficient and scalable.

  • PyTorch provides modular APIs—torch.nn, torch.optim, torchvision, torchtext—to build any ML/DL system.

  • It is used behind cutting-edge models like GPT, BERT, ResNet, YOLO, and in AI companies including Meta, OpenAI, and Tesla.

  • PyTorch’s open-source ecosystem and active community offer state-of-the-art models and reproducible research.

  • Learning PyTorch gives students strong foundations in building, training, and deploying AI models from scratch.

PyTorch
01

Module 1: Introduction to PyTorch

  1. What is PyTorch?

  2. PyTorch vs TensorFlow

  3. Installing PyTorch (CPU/GPU)

  4. Overview of key libraries: torch, torch.nn, torchvision

02

Module 2: Tensors and Operations

  1. Creating tensors: from lists, arrays, random values

  2. Tensor properties: shape, dtype, device

  3. Basic tensor operations: arithmetic, broadcasting

  4. Indexing, slicing, reshaping

  5. Converting between NumPy and Torch

03

Module 3: Automatic Differentiation with Autograd

  1. Introduction to torch.autograd

  2. Computing gradients with .backward()

  3. Computational graph explanation

  4. Using requires_grad, detach()

  5. Chain rule and gradient flow in networks

04

Module 4: Building Neural Networks

  1. Using torch.nn.Module

  2. Defining __init__ and forward() methods

  3. Layers: Linear, ReLU, Sigmoid, Softmax

  4. Model parameters and initialization

  5. Saving and loading models

05

Module 5: Training a Neural Network

  1. The training loop:

    1. Forward pass

    2. Loss computation

    3. Backward pass

    4. Weight updates

  2. Optimizers: SGD, Adam, RMSprop (torch.optim)

  3. Loss functions: MSE, CrossEntropy, BCE

  4. Evaluation metrics: accuracy, loss

06

Module 6: Working with Datasets and DataLoaders

  1. Using torch.utils.data.Dataset and DataLoader

  2. Built-in datasets from torchvision and torchtext

  3. Creating custom datasets

  4. Transformations and augmentation (transforms)

  5. Batching, shuffling, and prefetching

07

Module 7: Computer Vision with PyTorch

  1. Introduction to torchvision.models

  2. CNN layers: Conv2D, MaxPool2D, Dropout

  3. Building CNNs from scratch

  4. Transfer learning with ResNet, VGG

  5. Image classification pipeline (CIFAR-10, MNIST)

08

Module 8: NLP with PyTorch

  1. Tokenization and word embeddings

  2. Using torchtext and pre-trained embeddings (GloVe)

  3. Text classification using RNNs/LSTMs

  4. Building simple Seq2Seq models

  5. Sentiment analysis project

09

Module 9: Customizing and Debugging Models

  1. Writing custom layers and loss functions

  2. Registering parameters

  3. Using hooks and model introspection

  4. Model visualization with TensorBoard and torchviz

10

Module 10: Advanced Deep Learning

  • Building RNN, GRU, and LSTM networks

  • Attention mechanism basics

  • Transformers with torch.nn.Transformer

  • Training large models with gradient clipping

  • Layer normalization, batch normalization

11

Module 11: Deployment and Inference

  1. Model serialization (torch.save, torch.load)

  2. Exporting with TorchScript and ONNX

  3. Deploying models to mobile and edge devices

  4. Building APIs with Flask/FastAPI for inference

12

Module 12: GPU Acceleration and Performance Tuning

  1. Using .to(device) for GPU training

  2. Multi-GPU training with DataParallel and DDP

  3. Memory optimization tips

  4. Mixed precision training with torch.cuda.amp

13

Module 13: Reinforcement Learning

  1. Basic concepts: agents, rewards, environment

  2. Building an RL agent with PyTorch

  3. Using OpenAI Gym

  4. Policy gradients (REINFORCE)

14

Module 14: PyTorch Ecosystem Overview

  1. TorchVision for vision tasks

  2. TorchText for NLP

  3. TorchAudio for speech

  4. PyTorch Lightning (optional) for research workflows

15

Module 15: Capstone Projects

  1. Image classifier (CIFAR-10 or custom dataset)

  2. Text classification using LSTM

  3. Transfer learning for object detection

  4. Deploy a PyTorch model via Flask API

  5. Visualize filters and activations in CNNs

16

Module 16: Projects & Case Studies

  1. Sentiment analysis on real-world reviews

  2. Resume/job description matching engine

  3. Customer support chatbot using RAG

  4. Text summarizer using BERT

  5. Multilingual Q&A system

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