Deep neural network python,
in this course we will learn how to build, train, and evaluate deep neural networks using Python and popular libraries like TensorFlow and PyTorch. We’ll begin by understanding the core concepts of neural networks, including layers, activation functions, loss functions, and backpropagation. Then, we’ll dive into building real-world models, from simple feedforward networks to more advanced deep architectures. The course covers data preprocessing, model optimization, regularization techniques, and performance evaluation. You’ll also gain hands-on experience training models on image and text datasets. Through step-by-step coding tutorials and practical examples, you’ll develop the skills needed to create efficient and accurate deep learning models in Python. By the end of this course, you’ll be confident in designing and deploying deep neural networks to solve real-world problems in classification, regression, and beyond. Learn With Jay
