Fundamentals of AI, ML and DL | How Deep Learning Really Works

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In this lesson on the Fundamentals of AI, ML and DL, we focus on Deep Learning — what it is, how it works, and why it’s such a powerful branch of machine learning.

We explain how artificial neural networks are inspired by the human brain, how they process inputs through layers and weights, and why adding multiple hidden layers leads to deep neural networks. You’ll also see a practical example of how neural networks learn from data, using house price prediction as an intuitive case study.

🔹 Key Topics Covered:

Fundamentals of AI, ML, and Deep Learning

Why Deep Learning is inspired by the human brain

Biological neurons vs artificial neurons

Input layers, hidden layers, and output layers

Weights, parameters, and learning through adjustment

Shallow vs deep neural networks

Why multiple hidden layers = Deep Learning

Practical example: predicting house prices

What you need to know about Deep Learning for the exam

📘 Perfect for:
AWS exam takers, AI & ML beginners, cloud architects, and anyone learning the core foundations of AI, Machine Learning, and Deep Learning.

All of our courses available at: https://www.cloudwolf.com/

⏱️ Timestamps

00:00 – Intro: Deep Learning in the AI/ML/DL landscape
00:30 – Why neural networks matter (Nobel Prize context)
01:05 – What Deep Learning is
01:40 – Biological neurons vs artificial neurons
02:20 – How a neural network processes inputs
03:05 – Weights, parameters, and learning
03:50 – Training with examples (house price prediction)
04:45 – Shallow vs deep neural networks
05:15 – Exam summary & key takeaways

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#ArtificialIntelligence #MachineLearning #DeepLearning
#AWSExamPrep #CloudWolf #NeuralNetworks #AIConcepts