Deep Understanding of MNIST Problem and Its CNN Solution Using CNN Explainer

Gain a deep understanding of the classic MNIST problem and its Convolutional Neural Network (CNN) solution in this comprehensive tutorial. Learn to implement a Keras-based CNN model achieving nearly 99% accuracy for handwritten digit recognition. Explore the step-by-step process of building, training, and evaluating the model using Google Colab. Dive into key concepts such as dataset preparation, model configuration, and cross-validation. Utilize the CNN Explainer tool to visualize and comprehend the inner workings of each layer. Master essential techniques like flattening, bias setting, dropout, and dense output layers. By the end, acquire the skills to adapt this knowledge to solve your own image classification problems using deep learning and neural networks.

- MNIST Tutorial Starts
- Google Colab notebook
- Coding Keras Mnist solution
- Running Keras Solution
- Assistance for Beginners
- Model Compile
- Model Training
- Restarting from Top
- Understanding Classes
- Introducing CNN Explainer
- Source Dataset
- Loading Source Dataset
- Train and Test Dataset
- Source image dimension formatting
- Format Target Categorical
- CNN Config
- Batch Size and epochs
- Model Recompilation
- Model Re-training start
- Cross-Validation
- CNN Explainer Assistance
- Flatten
- Using Bias Setting
- Dropout
- Final Dense output layer
- Evaluate Model
- Source Code at GitHub
- RECAP