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Generative AI Applications

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May 2, 2024 4 minute read

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What is Generative AI?

Generative AI refers to the subset of Artificial Intelligence (AI) that is concerned with the generation of new data. Unlike traditional AI systems that are designed to process existing data and make predictions, generative AI systems are capable of creating entirely new data points, such as text, images, music, and even code.

Why Learn Generative AI Applications?

There are numerous reasons why individuals may want to consider learning about generative AI applications. These include:

Curiosity: Generative AI is a rapidly evolving field that presents new possibilities for data generation and creation.
Academic Requirements: Generative AI is increasingly becoming a topic of interest in computer science and related fields.
Career Development: Generative AI skills can enhance professional prospects in various domains, such as machine learning, data science, and software engineering.

Types of Generative AI Applications

Generative AI applications can be broadly classified into three main types:

Path to Generative AI Applications

Take the first step.

We've curated two courses to help you on your path to Generative AI Applications. Use these to develop your skills, build background knowledge, and put what you learn to practice.

Sorted from most relevant to least relevant:

Introduction to Large Language Models - Español

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How to Secure Cloud Infrastructure with Generative AI

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Reading list

We've selected four books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Generative AI Applications.

Deep Learning

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Comprehensive guide to generative adversarial networks (GANs), a type of generative AI that has been widely used for image generation, text generation, and other tasks.

Deep Learning

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This comprehensive guide provides a thorough introduction to generative AI, covering fundamental concepts, architectures, and applications. It is suitable for readers with a background in machine learning and deep learning.

Deep Learning (Adaptive Computation and Machine...

Hardcover

$$$

Deep Learning (Adaptive Computation and Machine...

Kindle Edition

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