AI Lesson 17 – Intro to PyTorch | Dataplexa

AI Course

I. AI Foundations
1. Introduction to AI 2. History of AI 3. AI vs ML vs DL 4. Search Algorithms 5. Intelligent Agents 6. Informed & Uninformed Search 7. Constraint Satisfaction 8. Adversarial Search 9. Logic in AI 10. Planning in AI 11. Knowledge Representation 12. Probabilistic AI 13. Bayesian Networks 14. Markov Models 15. AI Ethics 16. AI Applications 17. Intro to PyTorch 18. Intro to TensorFlow 19. AI Workflows 20. Data for AI
II. Machine Learning
21. Introduction to ML 22. ML Workflow 23. Supervised Learning 24. Unsupervised Learning 25. Reinforcement Learning 26. Linear Regression 27. Logistic Regression 28. SVM 29. Decision Trees 30. Random Forest 31. Gradient Boosting 32. XGBoost 33. K-Means 34. Hierarchical Clustering 35. Dimensionality Reduction 36. Feature Engineering 37. Feature Selection 38. Model Evaluation 39. Overfitting vs Underfitting 40. Cross Validation
III. Deep Learning
41. Intro to Deep Learning 42. Neural Networks 43. Activation Functions 44. Backpropagation 45. Loss & Optimizers 46. Regularization 47. CNN Basics 48. CNN Architectures 49. RNN, LSTM, GRU 50. Seq2Seq 51. Transformers 52. Attention 53. Positional Encoding 54. Training DL 55. Hyperparameter Tuning 56. Transfer Learning 57. Autoencoders 58. GAN Basics 59. GAN Applications 60. Model Serving
IV. Natural Language Processing
61. Intro to NLP 62. Text Processing 63. Tokenization 64. Stopwords & Lemmatization 65. BoW & TF-IDF 66. Word Embeddings 67. Sentence Embeddings 68. NLP Classification 69. Sequence Modeling 70. RNN in NLP 71. Transformers in NLP 72. BERT Basics 73. BERT Fine-tuning 74. RoBERTa & DistilBERT 75. Sentiment Analysis 76. NER 77. Machine Translation 78. Text Generation 79. NLP Evaluation 80. NLP Use Cases
V. Computer Vision
81. Intro to CV 82. Image Processing 83. Filters & Edges 84. Convolutions 85. Object Detection 86. Object Tracking 87. Image Segmentation 88. Face Recognition 89. Feature Detection 90. OCR 91. Image Augmentation 92. Generative Vision 93. Vision Transformers 94. Multimodal Models 95. CV Use Cases
VI. LLM Engineering
96. Intro to LLMs 97. Tokenization 98. LLM Architecture 99. Pretraining 100. Fine-tuning 101. RLHF 102. Prompt Engineering 103. Embedding Models 104. Vector Databases 105. LLM Agents 106. Guardrails & Safety 107. AI in Production 108. End-to-End Project

Introduction to PyTorch

As AI models became more complex, developers needed a flexible and efficient framework to build, train, and experiment with neural networks. PyTorch is one of the most widely used deep learning frameworks today, trusted by researchers and industry teams alike.

In this lesson, we will understand what PyTorch is, why it is used, and how it fits into real AI workflows. We will also write a small piece of code to see how PyTorch works in practice.

What Is PyTorch?

PyTorch is an open-source deep learning framework developed by Facebook AI Research. It allows developers to build neural networks using tensors and automatic differentiation.

Unlike traditional machine learning libraries, PyTorch is designed to be dynamic. This means models are built and executed step by step, making debugging and experimentation much easier.

Real-World Connection

PyTorch is used in:

  • Image recognition systems
  • Natural language processing models
  • Speech recognition
  • Large Language Models (LLMs)

Companies prefer PyTorch because it feels similar to Python, supports GPU acceleration, and integrates easily with research workflows.

Core Building Block: Tensors

At the heart of PyTorch is the tensor. A tensor is similar to a NumPy array but optimized for deep learning and GPU computation.

Tensors can represent:

  • Single values (scalars)
  • Lists of numbers (vectors)
  • Tables of data (matrices)
  • Multi-dimensional data (images, videos)

Simple PyTorch Example

Let’s create a basic tensor using PyTorch and perform a simple operation. 


import torch

# Create a tensor
x = torch.tensor([1, 2, 3])

# Perform an operation
y = x * 2

print(y)
tensor([2, 4, 6])

Understanding the Code

Here’s what happens step by step:

  • torch.tensor() creates a tensor from a Python list
  • The tensor supports mathematical operations like multiplication
  • The operation is applied element-wise

This simplicity allows developers to focus on model logic instead of low-level computations.

Why PyTorch Is Popular

PyTorch offers several advantages over older frameworks:

  • Dynamic computation graphs
  • Easy debugging
  • Strong community support
  • Seamless transition from research to production

Because of this, most modern AI research and production systems rely on PyTorch.

Practice Questions

Practice 1: What is the basic data structure used in PyTorch?



Practice 2: PyTorch uses which type of computation graph?



Practice 3: PyTorch can accelerate computations using what hardware?



Quick Quiz

Quiz 1: Which object represents data in PyTorch?





Quiz 2: PyTorch was originally developed by which organization?





Quiz 3: What makes PyTorch easier to debug?





Coming up next: TensorFlow — another powerful deep learning framework and how it compares with PyTorch.

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