Backpropagation for Softmax and Multi Class Classification Complete Mathematical Derivation

Neural Network python, in this course we will learn about Neural Network Python course. This course provides a hands-on introduction to building, training, and evaluating neural networks using Python. You will start by understanding the foundational concepts of neural networks, such as neurons, weights, biases, activation functions, forward propagation, and loss functions. Then, you will implement these components step by step using Python and NumPy, without relying on high-level libraries. We’ll cover how to train the network using backpropagation and gradient descent, and apply it to real-world problems like binary classification and digit recognition. The course will also introduce you to best practices in network initialization, learning rate tuning, and model evaluation. By the end, you’ll not only understand how neural networks work internally but also be able to build your own from scratch. This course is ideal for learners who want to deepen their understanding of deep learning by coding neural networks manually. Learn With Jay