Train ResNet50, ResNet101 & ResNet152 on Custom Multi-Class Dataset | Confusion Matrix Explained 📊🤖

🎯 Master ResNet Models: ResNet-50, ResNet-101, and ResNet-152 | Train & Test Like a Pro! In this deep learning tutorial, we'll walk through ResNet-50, ResNet-101, and ResNet-152 — powerful convolutional neural networks (CNNs) that revolutionized computer vision. Whether you're a beginner or an advanced AI enthusiast, this video will help you understand, train, and evaluate ResNet models using TensorFlow and Keras. 💡 🔥 What you'll learn: ✅ What ResNet is and how it works ✅ Differences between ResNet-50, ResNet-101, and ResNet-152 ✅ How to train and test these models on custom and Kaggle flower datasets ✅ Image classification using deep learning ✅ A step-by-step guide for beginners and experts 🛠️ Required Python packages: pip install tensorflow matplotlib scipy plotly scikit-learn seaborn 📁 Resources and links: 🔗 Full playlist: https://youtube.com/playlist?list=PLdzJ1uLokgLZ19RS9swDrUer_D1DuaGbz&si=jFtRwz8TkRXlpO2s 💻 GitHub code repository: https://github.com/nomanpust/Image-classification-using-Deep-Learning 🌼 Small flower dataset: https://shorturl.at/ggQzF 🌸 Kaggle flower dataset: https://shorturl.at/I6MbC 📹 Virtual environment setup: https://youtu.be/EwTdKrvvbYQ 🚀 Perfect for: 🔹 Machine learning students 🔹 AI and data science enthusiasts 🔹 Computer vision engineers 🔹 Anyone wanting to explore transfer learning with ResNet 📌 Topics covered: Deep learning with TensorFlow and Keras Transfer learning and fine-tuning Image classification project CNNs and convolutional layers Evaluation metrics: accuracy, confusion matrix Avoiding overfitting and underfitting Model training and optimization 📢 Don’t forget to like 👍, comment 💬, and subscribe 🔔 for more in-depth tutorials on AI, ML, and deep learning. Tags: Deep Learning, ResNet, ResNet-50, TensorFlow, Keras, Machine Learning, Image Classification, CNN, Computer Vision, AI Projects, ML Projects, Transfer Learning, Artificial Intelligence, Python, Neural Networks, Data Science, Model Training, Custom Dataset, Model Evaluation, Flower Classification, Deep Learning Tutorial, Smart Tech, OpenCV, AI Research