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7.1 Introduction to Neural Networks

Introductory Video

Lecture Video 1

What Is a Neural Network?

Neurons, Weights, Biases, and Activation Functions

Types of Activation Functions

Perceptrons

Building a Simple Neural Network

7.2 Backpropagation

Lecture Video 2

Static Backpropagation

Error and Loss

Gradient Descent and Backpropagation

Training a Neural Network Using Backpropagation in Python using TensorFlow

Recurrent Neural Networks

7.3 Introduction to Deep Learning

Lecture Video 3

Neural Networks with Many Layers

Loss Functions and Their Role in Training Neural Networks

Example: Using Deep Learning to Classify Handwritten Numerals

7.4 Convolutional Neural Networks

Lecture Video 4

The Components of Convolutional Neural Networks

Feature Maps

Image Analysis Using CNNs

7.5 Natural Language Processing

Lecture Video 5

A Brief History of NLP

NLPs in Generative Art

Video Editing and Captioning

Computer Coding

Speech Recognition and Text-to-Speech

Future Directions for NLP and LLM

ChatGPT and Ethical Considerations of Using NLP

Limitations of NLPs

Malicious Uses of AI

knowledge check

Knowledge Check:

Knowledge Check: Neural Network Fundamentals

Knowledge Check:: Neural Network Components

Knowledge Check: Activation Functions

Knowledge Check: Perceptrons

Knowledge Check: Data Preparation for Neural Networks

Knowledge Check:Loss and Error

Knowledge Check: Gradient Descent

Knowledge Check: Backpropagation

Knowledge Check: Recurrent Neural Networks

Knowledge Check:Deep Learning

Knowledge Check: Handwritten Digit Classification

Knowledge Check: CNN Components

Knowledge Check: Feature Maps

Knowledge Check:NLP Applications

Knowledge Check:NLP Ethics and Limitations

Welcome to "Deep Learning and Artificial Intelligence Basics," your comprehensive gateway into the fascinating world of modern AI. Whether you are an aspiring data scientist, a curious software developer, or a tech enthusiast, this course is designed to demystify the complex concepts behind artificial intelligence and deep learning.

Welcome to "Deep Learning and Artificial Intelligence Basics," your comprehensive gateway into the fascinating world of modern AI. Whether you are an aspiring data scientist, a curious software developer, or a tech enthusiast, this course is designed to demystify the complex concepts behind artificial intelligence and deep learning. We start from the ground up, introducing you to the foundational building blocks of neural networks, including neurons, weights, biases, and activation functions. From there, you will dive into the mechanics of how machines learn through backpropagation and gradient descent, even getting hands-on experience training a neural network using Python and TensorFlow.

As you progress, the course will guide you through the architecture of deep learning models with multiple layers, exploring practical applications like classifying handwritten numerals. You will also uncover the power of Convolutional Neural Networks (CNNs) for image analysis and feature mapping. Finally, we will explore the rapidly evolving field of Natural Language Processing (NLP). You will learn how AI is revolutionizing generative art, video editing, computer coding, and speech recognition. We also tackle the critical ethical considerations, limitations, and potential malicious uses of AI technologies like ChatGPT. By the end of this course, reinforced by 15 comprehensive quizzes, you will have a solid, practical understanding of deep learning and AI basics, empowering you to take the next step in your tech career. Join us today and unlock the potential of artificial intelligence!

Chapter 9: Deep Learning and Artificial Intelligence Basics

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            <p>Artificial Intelligence and Deep Learning are rapidly transforming the technological landscape, powering everything from advanced image recognition to sophisticated conversational agents. This course provides a comprehensive foundation in these groundbreaking fields, beginning with the fundamental building blocks of artificial intelligence: neural networks. Learners will explore the intricate mechanics of neurons, weights, biases, and various activation functions, culminating in the construction of simple neural networks and perceptrons. Building upon this base, the curriculum delves into the critical process of backpropagation, demystifying how networks learn and adapt through error calculation, loss functions, and gradient descent. Practical applications, such as training models using Python and TensorFlow, ensure that theoretical knowledge is firmly grounded in real-world coding practices.</p>

<p>As the journey progresses, the focus shifts to more complex architectures, introducing the principles of Deep Learning with multi-layered networks and Recurrent Neural Networks (RNNs). Students will discover how Convolutional Neural Networks (CNNs) revolutionize image analysis through feature maps and specialized components, highlighted by practical examples like classifying handwritten numerals. Furthermore, the course offers an extensive exploration of Natural Language Processing (NLP). From its historical roots to cutting-edge applications in generative art, speech recognition, and Large Language Models (LLMs) like ChatGPT, learners will gain a holistic view of how machines process human language. Crucially, this exploration is balanced with a rigorous examination of the ethical considerations, inherent limitations, and potential malicious uses of AI, ensuring a responsible approach to technology. A comprehensive suite of quizzes at the conclusion of the course will test and solidify your understanding across all these vital topics.</p>
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