Learn the fundamentals of Neural Networks, covering topics such as feedforward and recurrent architectures, backpropagation and deep learning applications in this comprehensive course. Gain practical experience through hands-on projects and explore advanced techniques for optimizing network performance and solving complex tasks.
Welcome to our comprehensive course on Neural Networks! Covering everything from fundamental concepts to practical implementations, this course provides a thorough understanding of neural network architecture, training methodologies and application development. Beginning with an introduction to neural networks and their significance, we delve into architectural components, weight initialization techniques and essential hyperparameters. You'll explore various activation and loss functions, alongside training algorithms like Gradient Descent and Adam. Practical sessions include data explanation, numerical examples and hands-on implementation using MATLAB and Python. By the end, you'll be equipped to develop neural networks for diverse applications, making this course essential for both beginners and experienced practitioners.