Deep Dive with PyTorch - The Complete Deep Learning Series

Embark on a comprehensive video series covering deep learning fundamentals and advanced techniques using PyTorch framework. Master tensor creation, manipulation, and visualization while building neural networks from scratch. Learn to create, train, and evaluate simple neural networks, then progress to implementing image classification models using ResNet-18 architecture. Explore model loading for inference and predictions, understanding how to deploy trained models for real-world applications. Discover the significance of optimizers in deep learning and their impact on model performance. Gain hands-on experience with one of the most powerful deep learning frameworks through practical implementations that bridge theory and practice. Transform your understanding of artificial intelligence by working through real-world problem-solving scenarios, from basic tensor operations to sophisticated computer vision tasks including object detection techniques.

Syllabus

L-1 Pytorch Introduction and Installation
L-2 Create, manipulate and visualize tensors | Pytorch tensors
L-3 | Create, train and evaluate the Simple neural network using Pytorch
L-4 Loading trained models for inference and predictions | Image classification
L-5 | Image Classification Using ResNet-18 and Pytorch
L-6 | Significance of Optimizers | Deep Learning