CVML Experiment 11: CNN for Digit Recognition Using MNIST | TensorFlow/Keras @TeamGalatFamily

Get the complete lab materials, programs & resources: https://yt.galatfamily.in/ In this CVML Experiment 11, we’ll learn how to train and evaluate a Convolutional Neural Network (CNN) for handwritten digit recognition using the MNIST dataset. MNIST is one of the most popular datasets for learning image classification and Deep Learning. In this experiment, we’ll build a CNN model that learns from handwritten digit images and predicts digits from 0 to 9. What you'll learn: • What is a Convolutional Neural Network (CNN)? • Introduction to the MNIST dataset • Understanding handwritten digit images • Loading and preprocessing the MNIST dataset • Normalizing image pixel values • Building a CNN using TensorFlow/Keras • Understanding Convolution and Pooling layers • Using Flatten and Dense layers • Training the CNN model • Evaluating model performance • Understanding accuracy and loss • Making predictions on unseen handwritten digits • Visualizing classification results In this Experiment: 1. Load the MNIST Dataset Load the MNIST handwritten digit dataset containing images of digits from 0 to 9. 2. Preprocess the Images Prepare the images for the CNN by reshaping and normalizing the pixel values. 3. Build the CNN Model Create a CNN architecture using TensorFlow/Keras with convolution, pooling, flattening, and dense layers. 4. Train the Model Train the CNN on the MNIST training dataset so it can learn important visual patterns from handwritten digits. 5. Evaluate the Model Test the trained model using unseen MNIST test data and measure its classification performance. 6. Predict Handwritten Digits Use the trained CNN to recognize new digit images and compare the predicted results with the actual labels. The complete Deep Learning workflow covered in this experiment: MNIST Dataset → Preprocessing → CNN Architecture → Training → Evaluation → Prediction CNNs are widely used in Computer Vision applications such as handwritten digit recognition, image classification, object detection, facial recognition, medical imaging, and many other AI systems. This video is part of our Computer Vision & Machine Learning Lab (CVML Lab) series for CSE (AI & ML) – H23 Regulation. Explore Galat Family: https://www.galatfamily.in/ Discover our projects, developer guides, experiments, and practical technology content. Plan. Build. Ship. Iterate. Subscribe to Galat Family for practical content on Computer Vision, Machine Learning, Deep Learning, AI, Full Stack Development, programming, and real-world projects. #CVML #CNN #MNIST #DigitRecognition #TensorFlow #Keras #DeepLearning #ComputerVision #MachineLearning #ImageClassification #Python #CVMLLab #AI #GalatFamily