Training Neural Networks

Ever wondered how neural networks learn? In this episode, we dive deep into the training process. First, the forward pass calculates the network's output. Then, backpropagation uses the chain rule to compute the gradients of a loss function, like MSE or cross-entropy. We explore how these gradients help to update parameters, driving the network towards optimal accuracy. We'll also discuss key optimization algorithms, such as Stochastic Gradient Descent (SGD) and Adam, which adjust these parameters to minimize loss. Join us as we unravel the magic behind network learning and reveal how choosing the right loss function and optimizer is key.

Om Podcasten

Explore the fascinating world of Artificial Intelligence, where big ideas meet clear explanations. From the fundamentals of machine learning and neural networks to advanced deep learning models like CNNs, RNNs, and generative AI, this podcast unpacks the tech shaping our future. Discover real-world applications, optimization tricks, and tools like TensorFlow and PyTorch. Whether you’re new to AI or an expert looking for fresh insights, join us on a journey to decode intelligence—one concept, one model, and one story at a time.