Keras & TensorFlow Tutorial - Image classification with MLP - Code explained line by line

In this comprehensive tutorial, we’ll guide you step-by-step through a Python script that trains a Multi-Layer Perceptron (MLP) to recognize handwritten digits from the MNIST dataset. Perfect for beginners and intermediate practitioners alike, you’ll learn not only how to get a model up and running, but also how to monitor, debug, and extend it for real-world use. 📂 Source Code on GitHub: https://github.com/samugit83/TheGradientPath/tree/master/Keras/ImageClassificationWithMLP What You’ll Learn MNIST Dataset Fundamentals: Understanding the structure, purpose, and preprocessing needs of this classic benchmark. Data Preparation: Normalizing pixel values, one-hot encoding labels, and creating train/test splits. Keras Functional API: Building flexible architectures with Input, Dense, Activation, BatchNormalization, and Dropout layers. Model Compilation & Hyperparameters: Choosing optimizers (Adam), loss functions (categorical crossentropy), learning rates, batch sizes, and epochs. Regularization Techniques: How BatchNormalization and Dropout combat overfitting, and tips on tuning their rates. Advanced Callbacks: Configuring TensorBoard for metrics, histograms, graph visuals, image logging, and profiling. Architecture Visualization: Generating clear network diagrams with Visualkeras and logging them into TensorBoard. Training Strategies: Using validation splits, early stopping, and learning rate schedules to optimize performance. Evaluation Metrics: Beyond accuracy—introducing confusion matrices, precision, recall, and F1-score to get deeper insights. Model Saving & Loading: Best practices for exporting your trained model, loading it in a new script, and running inference. Sample Predictions: Automating random-sample inference, saving result images with true vs. predicted labels, and creating easy-to-read reports. 👍 Like & Subscribe If you found this deep dive valuable, hit the 👍 button and subscribe for more hands-on tutorials in TensorFlow, Keras, an