Anatomy of Neural Networks

Ready to unravel the mysteries of feedforward neural networks? This episode explores their architecture, the core of many AI systems. We break down the key components: input, hidden, and output layers and the computational neurons within. Discover how neurons perform weighted sums and apply activation functions, like sigmoid, tanh, ReLU, and softmax, introducing non-linearity crucial for complex modeling. Learn about weights and biases, which are parameters optimized to minimize errors. We’ll trace the flow of information, from input propagation to weighted summation, then activation and output generation. We’ll also use mathematical notation to visualize the computations of each layer. Join us to see how these networks process information step by step, transforming raw data into meaningful results!

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.