Pytorch introduction | Why you need to learn pytorch
PyTorch Tutorial for Beginners | Deep Learning & Neural Networks from Scratch
In this tutorial, you will learn *PyTorch* from the ground up with a clear, hands-on approach. This video is designed for beginners as well as professionals who want to transition into *Deep Learning, AI Engineering, and ML research* using PyTorch.
We start with the *fundamentals of tensors**, move into **autograd and backpropagation**, and then build a **neural network step by step* using both low-level PyTorch APIs and `torch.nn.Sequential`. You will also understand *activation functions**, **loss functions**, and how to move your training workflow from **CPU to GPU* efficiently.
This tutorial emphasizes **practical understanding**, clean code structure, and concepts frequently tested in **interviews and real-world projects**.
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What you’ll learn in this video:
Why PyTorch is widely used in AI & research
PyTorch tensors and tensor operations
Automatic differentiation (Autograd)
Building neural networks from scratch
Activation functions (ReLU, Sigmoid, Softmax)
Loss functions and optimizers
GPU-based training using CUDA
Best practices for scalable model training
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Who this tutorial is for:
Beginners in Deep Learning & AI
Data Scientists & ML Engineers
Python developers moving into AI
Students preparing for AI/ML roles
Professionals working with TensorFlow who want to learn PyTorch
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Prerequisites:
Basic Python knowledge
High-school level mathematics
Curiosity to learn AI the right way
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Tools & Libraries:
Python
PyTorch
CUDA (optional for GPU acceleration)
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If you find this tutorial useful, *like the video**, **subscribe to the channel**, and **share it with fellow learners**. More advanced tutorials on **Neural Networks, Transformers, Trading AI, and Real-world ML systems* are coming soon.
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