Neural Networks & Real World AI Projects Using PyTorch

This Tutorial course provides a complete, step by step journey into Deep Learning and Neural Networks using PyTorch, starting from basic machine learning concepts and progressing to advanced AI architectures used in real world applications. You will begin with foundational models such as Linear Regression and the Perceptron, gradually advancing through Multi-Layer Perceptron (MLPs), implementing real life problems such as digit recognition on the MNIST dataset.As the course progresses, you will learn how modern AI systems understand images through Convolutional Neural Networks (CNNs) and how they process sequences using Recurrent Neural Networks (RNNs). Finally, you will combine CNN, RNN models to build an Image Captioning system, one of the most popular and practical applications of deep learning.Every module is taught with hands on PyTorch implementation, real datasets, clear explanations, and real world examples to help you truly understand how AI systems work end to end.This tutorial course ensures that professionals gain both solid theoretical knowledge and practical skills to build and deploy deep learning models confidently.The Tutorial Course Primarily Focuses onUnderstanding neural network fundamentalsImplementing deep learning models in PyTorchApplying deep learning to real-life datasets and projectsThis Course Is Ideal forAnyone wanting to learn PyTorch from scratchLearners who want strong conceptual & practical deep learning understandingProfessionals preparing for ML & DL interviewsDevelopers wanting to build deep learning projects for their portfolioBy the end of this course, Professionals will be able toBuild Linear Regression & Perceptron models using PyTorchImplement MLP networks and solve classification problemsTrain CNN models for image classification tasksUnderstand and build RNN architect