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.