Neural-Networks

This document explains the key differences between machine learning and deep learning, using practical analogies and examples. It covers their relationship, data requirements, feature engineering, and the role of neural networks in deep learning.

This document introduces neural networks, their structure, types, and training process. It explains how neural networks are inspired by the human brain and highlights their applications in pattern recognition, image analysis, and sequential data processing.

This document provides an overview of deep learning, its key concepts applications in various fields, and the different types of models used. It also covers the training process and recent advancements in the field.

Overview of machine learning techniques, including supervised, unsupervised and reinforcement learning, with a focus on regression, classification, neural networks, and the training process.