This hands-on deep learning course focuses on building AI applications from scratch using TensorFlow. Starting with the classic MNIST dataset, learners create a digit recognition app that introduces them to fundamental concepts such as neural networks, model training, and evaluation. Step by step, the course demonstrates how to structure deep learning projects, preprocess data, and implement models that achieve high accuracy.

The second project, DogNet, is a Dog Breed Identifier app that teaches more advanced techniques, including convolutional neural networks (CNNs) for image classification, handling complex datasets, and optimizing models for better performance. Students learn how to prepare datasets, apply data augmentation, and train models effectively while monitoring results with TensorBoard.

By the end of the course, learners gain practical experience in developing end-to-end deep learning applications, from data preprocessing to model deployment. This course is ideal for beginners and intermediate learners who want to move from theory to real-world AI projects, equipping them with the skills needed to build their own AI-powered applications confidently.