Complete 5 ResNet Deep Learning Project From Scratch 2025

Complete Deep Learning Project with ResNet | 5 Deep Learning Projects From Scratch | Hands-On Deep Learning Project

What you'll learn
Understanding ResNet architecture
Preparing and augmenting datasets
Fine-tuning ResNet for various applications.
Evaluating model performance with metrics and techniques.

Requirements
Basic Python & Deep Learning Is Required

Description
Welcome to the ultimate course on Deep Learning Project focused on ResNet architecture – master 5 complete Deep Learning Projects from scratch.This course guides you step-by-step through building and training 5 powerful Deep Learning Projects using ResNet models. Whether you are a beginner or have some experience, this course covers practical techniques and project implementations for real-world Deep Learning Projects.You will gain hands-on experience in designing, training, and evaluating ResNet-based Deep Learning Projects applicable to image recognition and computer vision tasks.By the end of this course, you will have successfully completed 5 advanced Deep Learning Projects and gained the confidence to tackle more complex deep learning challenges.Projects Covered:Image Classification: Build a ResNet model for multi-class image classification tasks.Object Detection: Integrate ResNet with YOLO or similar frameworks for object detection.Medical Image Analysis: Develop a ResNet model for detecting diseases from medical imaging datasets.Image Segmentation: Use ResNet as a backbone for segmenting objects in complex images.Facial Recognition System: Train a ResNet model for accurate facial recognition.This course is ideal for:AI and Machine Learning Practitioners: Professionals seeking hands-on experience in applying ResNet to real-world problems.Software Developers: Developers wanting to transition into AI or enhance their skills in computer vision projects.Data Scientists: Experts looking to expand their knowledge of ResNet for image analysis and related applications.By the end, you’ll have a robust understanding of ResNet and the ability to implement it in diverse applications.

Who this course is for:
Students and Researchers, Aspiring Deep Learning Enthusiasts