The explosive popularity of ChatGPT and the recent flurry of large-scale model developments have thrust the field of artificial intelligence into the spotlight. As a tech enthusiast keen on exploring various technologies, understanding the principles behind these advancements is a natural inclination. Starting with deep learning is a logical step in delving deeper into AI, especially since I've already studied Professor Andrew Ng's Machine Learning course. Now, through his Deep Learning Specialization, I am furthering my knowledge in this domain. This article aims to demystify deep learning, drawing insights from the first course of the specialization. 1. Neural Networks: An Overview and Fundamental Concepts Neural networks are algorithmic models that mimic the human brain's neural network for distributed and parallel information processing. Comprising a vast array of interconnected nodes (or neurons), each neuron processes incoming signals with simple signal processing functions and transmits the outcomes to subsequent neurons. The objective is to minimize the predictive error of the network by continually adjusting the network's parameters, namely the connection weights between neurons. Below is an illustration…