Practical Deep Learning with TensorFlow and Keras

Deep learning is driving the most exciting breakthroughs in technology today, but getting started with neural networks can feel overwhelming. This text-based guide demystifies the core concepts, helping you write your first machine learning models with confidence. You will transition from understanding basic mathematical concepts to implementing and training neural networks. By reading through step-by-step code walkthroughs, you will gain a solid foundation in modern machine learning workflows. What you'll learn: - Understand the fundamental structure of neural networks, including neurons, activation functions, and layers. - Build neural network architectures using the Keras Sequential and Functional APIs. - Train machine learning models with TensorFlow using backpropagation and gradient descent. - Apply preprocessing techniques to clean and prepare data for deep learning models. - Evaluate model performance and prevent overfitting using regularization techniques. - Implement modern transfer learning strategies to adapt pre-trained models for custom tasks. The course starts with essential machine learning terminology and mathematical foundations before guiding you through hands-on coding scenarios. You will progress from simple regression models to complex multi-layer neural networks. This course is designed for beginners in machine learning and Python programmers who want to learn deep learning without any prior artificial intelligence experience. Start your journey into the world of artificial intelligence today.