Deep Learning Fundamentals

This lesson explains one of the fundamental concepts of neural networks — weights. You will learn how weights affect an AI model's decision-making, using the example of Gmail's spam filter, and get acquainted with their real-world applications across various fields.

This lesson explains how a neural network learns from mistakes by comparing its own predictions to actual outcomes. Using the example of Netflix, we'll discuss how an AI model improves through continuous feedback and why error is an integral part of learning.

This lesson explains the concept of deep layers in neural networks, showing how they process visual information from simple signals to complex concepts. Using the example of Google Photos, we'll see how AI uses this principle to recognize objects, places, and people, which underlies 'smart search' and other practical applications.

In this lesson, you'll learn why real-world complexity is nonlinear and how neural networks use nonlinear transformations to decode this complexity. Using the example of Google Maps, we'll see how Deep Learning can analyze many interconnected factors to make accurate predictions.

This lesson helps you understand when using Deep Learning is justified and when it isn't. You'll learn the key criteria that influence the choice of AI technology, and distinguish the strengths of Deep Learning from the advantages of classical machine learning in the context of specific tasks.

In this lesson, you'll learn about the Convolutional Neural Network (CNN) architecture, one of the most effective methods for working with images. You'll learn how AI 'sees' an image in parts and combines them into an overall visual meaning, using the example of Midjourney.

In this lesson, you'll learn how a Convolutional Neural Network (CNN) uses structural patterns to recognize objects regardless of changes in their position, angle, or lighting. Using the example of Face ID, you'll understand why the relative arrangement of facial features matters more than any single specific image.

This lesson discusses the concept of sequence in Deep Learning, emphasizing the crucial importance of element order in text, audio, and time-based data. We'll explore how AI handles these tasks and their real-world applications.

In this lesson, you'll learn about the concept of the attention mechanism in Deep Learning, especially in sequence processing. You'll learn how this mechanism helps AI models dynamically focus on the most important parts of context, which improves translation, summarization, and chatbot performance.

This lesson explores the Transformer architecture, which revolutionized text-based Deep Learning. You'll learn how it connects different parts of text through an attention mechanism and how it forms the foundation for systems like ChatGPT.

This lesson explains how ChatGPT builds responses token by token, rather than generating the entire text at once. You'll learn about the significance of tokens, the major impact of small changes in a prompt, and the real-world application of this process.

This lesson shows how AI uses embeddings to discover hidden similarities between users and content. You'll learn how this multi-dimensional 'taste map' creates personalized recommendations on platforms like YouTube and Spotify, going beyond simple categories.

This lesson discusses how AI systems like Siri process human speech. We'll explore the complexity of the voice signal, the role of Deep Learning in handling this complexity, and the two-stage process by which voice assistants not only recognize what was said but also understand its intent.

This lesson discusses how generative Deep Learning models like Midjourney work. We'll learn that they don't copy existing images but instead build entirely new visual combinations from learned patterns. The lesson also covers their practical applications and common misconceptions.

This lesson discusses business decisions around the use of Deep Learning. You'll learn what factors to consider when implementing this powerful technology, when it's most effective, and when a simpler approach is preferable.