Created by **Meta AI**, PyTorch has quickly become a favorite among researchers and practitioners in **artificial intelligence (AI)** and **deep learning** due to its flexibility, ease of use, and strong community support.

### Deep Learning Research

   PyTorch's flexibility and dynamic computation graph make it ideal for experimenting with novel architectures and advancing research in deep learning.

### Natural Language Processing (NLP)
   PyTorch powers tasks like text classification, machine translation, and sentiment analysis, leveraging state-of-the-art models like transformers.

### Computer Vision
   PyTorch is widely used for image classification, object detection, and image segmentation due to its rich library support and pre-trained models.

## PyTorch vs TensorFlow

**TensorFlow** is another open-source machine learning framework developed by **Google**. Known for its **scalability** and **production-ready features**, TensorFlow has long been a preferred choice for deploying machine learning models in real-world applications. 

However, PyTorch has gained rapid popularity due to its **flexibility** and **ease of use**, particularly in research and experimentation.


In summary, PyTorch has established itself as a leader in AI and deep learning by combining **flexibility**, **ease of use**, and **strong community support**. Its focus on research and production-readiness ensures it will remain a top choice for AI development in years to come.


What is one of the key reasons PyTorch is preferred for deep learning research?

Learn the fundamental and advanced concepts needed to work with PyTorch efficiently. Gain a solid understanding tensors, including creation, operations, and reshaping. Explore the essentials of gradients, backpropagation, and linear regression before moving on to handling datasets. Master the skill needed build, train, and evaluate neural networks.

Explore the fundamentals of PyTorch, focusing on tensors—the core data structure used for computations. You will learn about tensor creation, random initialization, mathematical operations, and shape manipulation.

Explore key concepts for training models in PyTorch, including computing gradients and performing multi-step backpropagation. Master linear regression as a foundational machine learning model and introduces handling datasets efficiently.

Discover how to build, train, and evaluate neural networks using PyTorch. You will learn how to define a simple feedforward neural network, optimize its parameters through training, and assess its performance.

PyTorch Essentials

What is PyTorch?

Applications of PyTorch

Deep Learning Research

Natural Language Processing (NLP)

Computer Vision

PyTorch vs TensorFlow