[100% Off] 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:
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Image Classification: Build a ResNet model for multi-class image classification tasks.
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Object Detection: Integrate ResNet with YOLO or similar frameworks for object detection.
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Medical Image Analysis: Develop a ResNet model for detecting diseases from medical imaging datasets.
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Image Segmentation: Use ResNet as a backbone for segmenting objects in complex images.
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Facial Recognition System: Train a ResNet model for accurate facial recognition.
This course is ideal for:
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AI and Machine Learning Practitioners: Professionals seeking hands-on experience in applying ResNet to real-world problems.
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Software Developers: Developers wanting to transition into AI or enhance their skills in computer vision projects.
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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.
Author(s): ARUNNACHALAM SHANMUGARAAJAN
