Deep Learning Foundations with TensorFlow and Keras

Understanding the mechanics of neural networks is essential for anyone looking to enter the field of artificial intelligence. By focusing on the core logic behind deep learning models, you will gain the skills necessary to handle complex data and automate decision-making processes. This text-based course guides you through the transition from basic data manipulation to constructing complex models for image recognition and regression. You will build a strong theoretical and practical base in one of the most popular ecosystems in the AI world. What you'll learn: - Understand the core concepts of tensors and how they represent data in deep learning. - Build and train neural networks using the intuitive Keras API. - Implement linear regression models to identify patterns in numerical datasets. - Create Convolutional Neural Networks (CNNs) for computer vision tasks like image classification. - Practice using modern TensorFlow 2.x workflows, including eager execution and high-level model building. - Learn to evaluate model performance and tune basic hyperparameters for better accuracy. The course moves from foundational definitions to practical model building, covering both the low-level logic of tensors and high-level neural network design. You will read through clear explanations and study code snippets to understand how these systems work under the hood. This course is designed for beginners who want a clear, text-based introduction to deep learning; no prior specialized knowledge is required beyond basic Python familiarity. Start your journey into artificial intelligence today.