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Complete 5 ResNet Deep Learning Project From Scratch 2025
Rating: 2.7 out of 5(9 ratings)
3,431 students

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
Created byARUNNACHALAM SHANMUGARAAJAN
Last updated 5/2025
English

What you'll learn

  • Understanding ResNet architecture
  • Preparing and augmenting datasets
  • Fine-tuning ResNet for various applications.
  • Evaluating model performance with metrics and techniques.

Course content

4 sections • 25 lectures • 1h 19m total length

  • Introduction To Project0:42
  • Face Class 1 : Import Packages4:51

    Create a Google Colab notebook with a t4 gpu for facial image prediction using ResNet 50, then import cv2, os, numpy, and TensorFlow Keras tools.

  • Face Class 2 : Import Dataset6:33
  • Face Class 3 : Build ResNet Model4:13

    Build a ResNet 50 model for the dataset by defining a buildResNet function with input shape and classes, using ResNet 50 with ImageNet weights and include_top false, then freeze base.

  • Face Class 4 : Train Dataset Using ResNet Model8:13
  • Face Class 5 : Output & Conclusion3:52

    save the trained model in keras format as face.h5, reload it, and predict face images using a predict function with cv2, numpy, and a label map.

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:

  1. Image Classification: Build a ResNet model for multi-class image classification tasks.

  2. Object Detection: Integrate ResNet with YOLO or similar frameworks for object detection.

  3. Medical Image Analysis: Develop a ResNet model for detecting diseases from medical imaging datasets.

  4. Image Segmentation: Use ResNet as a backbone for segmenting objects in complex images.

  5. Facial Recognition System: Train a ResNet model for accurate facial recognition.


This course is ideal for:


  1. AI and Machine Learning Practitioners: Professionals seeking hands-on experience in applying ResNet to real-world problems.

  2. Software Developers: Developers wanting to transition into AI or enhance their skills in computer vision projects.

  3. 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

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