Book Image

AI for Absolute Beginners: A Clear Guide to Tomorrow

By : Oliver Theobald
4 (1)
Book Image

AI for Absolute Beginners: A Clear Guide to Tomorrow

4 (1)
By: Oliver Theobald

Overview of this book

The course begins with an engaging introduction to the world of Artificial Intelligence, making it approachable for absolute beginners. We unravel the mysteries of AI's evolution, from its historical roots to the cutting-edge technologies shaping our future. By explaining complex concepts in simple terms, this course aims to illuminate the path for those curious about how AI impacts our world. The course focuses on the core components of AI, including machine learning, deep learning, and natural language processing, before advancing to more specialized topics like generative AI and computer vision. Each module is designed to build a comprehensive understanding, emphasizing why these technologies are crucial for solving real-world problems and how they're transforming industries. The course wraps up by exploring the ethical considerations and privacy concerns associated with AI, along with a visionary look at the future of work in an AI-driven world. It offers a treasure trove of further resources, ensuring learners have everything they need to continue their exploration of AI.
Table of Contents (13 chapters)
Free Chapter
A Brief History
AI Building Blocks
The 3 Stages of AI Development
Machine Learning
Deep Learning
Natural Language Processing
Generative AI
Recommender Systems
Computer Vision
Privacy & Ethical Considerations
The Future of Work
Further Resources

Deep Learning


From self-driving cars to large language models, the ability of deep learning to learn from raw data and process large datasets has dramatically broadened the use cases for artificial intelligence. However, like all technologies, deep learning has its own unique limitations and challenges that we will examine in this chapter.

Before we dive in, it’s important to explain the connection between deep learning and machine learning. Machine learning, as discussed, involves algorithms and models that improve with experience and exposure to data. As a subfield of machine learning, deep learning takes the foundational principles of machine learning and applies its own techniques to even larger and more complex datasets. This involves the use of artificial neural networks with deep and multiple layers stacked together to form a model.

While artificial neural networks are not a direct replica of the human brain, (with the human brain estimated to contain 100 billion...