Neural Network from Scratch in Python

Discusses whether neural networks act as black boxes, their ability to approximate any function, and introduces the concept of algorithms.View

Explains the rationale behind learning neural networks from scratch, emphasizing a deeper understanding of the fundamentals and avoiding dependencies.View

Outlines essential mathematical concepts like dot products and the rules of matrix multiplication relevant for deep learning.View

Describes the role of activation functions in neural networks, specifically focusing on the sigmoid function and its characteristics.View

Details on the derivative of the sigmoid function, crucial for backpropagation in training neural networks.View

Provides context about using diabetes data, particularly metrics like glucose and blood pressure for neural network training.View

Explains the chain rule in calculus, important for calculating the derivatives used in training neural networks.View

Lists the step-by-step process for implementing a neural network, from data loading to model training and prediction.View

Wraps up the presentation, providing additional learning resources and thanking the audience.View