Deep Learning and Neural Networks with Keras

Deep learning drives the modern AI revolution, yet getting started with building neural networks can feel overwhelming. This text-based course simplifies the journey, guiding you from basic mathematical concepts to designing and training your own deep learning models using Keras. You will transition from understanding how artificial neurons process information to writing clean, functional code that solves complex classification and regression problems. You will learn how to configure layers, tune hyperparameters, and handle common training challenges like overfitting, all while leveraging the multi-backend capabilities of modern Keras. What you'll learn: - Understand the foundational mechanics of neural networks, including weights, biases, activation functions, and gradient descent. - Build and compile deep learning models using the intuitive Keras Sequential and Functional APIs. - Train neural networks effectively by managing learning rates, epochs, batch sizes, and optimization algorithms. - Apply modern transfer learning techniques to adapt pre-trained models for custom tasks. - Configure Keras to run seamlessly across different backends like JAX, PyTorch, or TensorFlow. - Evaluate model performance using validation splits, loss curves, and core evaluation metrics. The course begins with essential terminology and the core mechanics of how neural networks learn, before moving into step-by-step code implementations. You will progress through practical written explanations and code-based exercises that demonstrate how to optimize networks and apply them to real-world datasets. This course is designed for beginners with a basic understanding of Python programming who want to enter the field of artificial intelligence. No prior experience with machine learning or advanced mathematics is required. Start reading today to build a strong, practical foundation in deep learning.