This highly-rated course boasts a 2.5-star-star rating from 6 reviews and has successfully guided 3,193 students in mastering IT Certifications skills. Featuring 1 hour(s) 30 minute(s) of expert-led content delivered in English, this course offers thorough training to enhance your Social Science expertise. The course details were last updated on December 24, 2024. This coupon code is brought to you by Anonymous.
- Expired on September 10, 2025
- Last Update: September 09, 2025
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Price:
19.99 $0 $
About This Course
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.
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