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Generative AI courses

1062 Courses

California Institute of Technology Udemy

Social Engineering with AI: Advanced Techniques

Join our course, "Social Engineering with AI: Advanced Techniques," offered by Udemy, and delve deep into the world of advanced social engineering. Learn to master various techniques, from phishing to deepfakes, and discover how artificial intelligence is revolutionizing these modern manipulation tactics. This course is perfect for those who.
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California Institute of Technology Coursera

Introduction to Generative AI with Snowflake

Explore the innovative realm of generative AI with a focused course using Snowflake. The program begins by introducing essential AI concepts, followed by a guided setup to create a functional learner environment and build a preliminary application. Progress to mastering Cortex LLM functions for executing diverse AI tasks, and culminate y.
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California Institute of Technology AWS Skill Builder

Responsible Artificial Intelligence Practices (日本語)

このコースでは、責任ある AI の実践について学びます。最初に、責任ある AI とは何かを説明します。責任ある AI の定義方法、直面している課題、その主な要素について学習します。 次に、責任ある AI システムを開発するためのトピックをいくつか取り上げます。責任ある AI をサポートするために AWS が提供しているサービスとツールを紹介します。また、AI システム用に.
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California Institute of Technology AWS Skill Builder

Exploring Artificial Intelligence Use Cases and Applications (日本語)

このコースでは、様々な産業領域における人工知能 (AI)、機械学習 (ML)、および生成AIの具体的なユースケースを探ります。これにはヘルスケア、金融、マーケティング、エンターテインメント業界が含まれ、AI技術の能力と限界、モデルの選択手法、主要なビジネスメトリクスを学びます。 コースレベル: 基礎 所要時間: 1時間 アクティビティこのコースは、インタラクテ.
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California Institute of Technology AWS Skill Builder

Developing Generative Artificial Intelligence Solutions (日本語)

Developing Generative Artificial Intelligence Solutions (日本語) このコースでは、生成人工知能 (生成 AI) によるアプリケーションライフサイクルについて学びます。詳細は以下のとおりです。 ビジネスユースケースの定義 基盤モデル (FM) の選択 FM のパフォーマンスの改善 FM のパフォーマンスの評価 デプロイとビジネス目標への影響 このコースは生成 A.
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California Institute of Technology AWS Skill Builder

Developing Generative Artificial Intelligence Solutions (繁體中文)

在本課程中，您將會探索生成式人工智慧 (生成式 AI) 應用程式生命週期，其中包括以下內容： 定義商業使用案例 選取基礎模型 (FM) 改善 FM 的效能 評估 FM 的效能 部署及其對業務目標的影響 本課程是生成式 AI 課程的入門，其中深入探討使用提示詞工程、檢索增強生成 (RAG) 和微調來自訂 FM 的相關概念。 課程等級：基礎 課程時長：1 小時 注意：本課.
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California Institute of Technology AWS Skill Builder

Responsible Artificial Intelligence Practices (繁體中文)

Responsible Artificial Intelligence Practices (繁體中文) 在本課程中，您將學習 AI 實踐。首先，將為您介紹什麼是負責任的 AI。您將學習如何定義負責任的 AI、瞭解負責任的 AI 嘗試克服之挑戰，以及探索負責任 AI 的核心維度。 然後，您將深入探索一些主題，瞭解開發負責任的 AI 系統。將為您介紹由 AWS 提供，以協助您使用負責任的 AI 之服務和工具。您也將瞭解在為 AI.
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California Institute of Technology AWS Skill Builder

Exploring Artificial Intelligence Use Cases and Applications (繁體中文)

在本課程中，您將探索各種行業中人工智慧 (AI)、機器學習 (ML) 和生成人工智慧 (生成式 AI) 的實際使用案例。這些領域包括醫療保健、金融、行銷、娛樂等。您還將了解 AI、ML 和生成式 AI 功能和限制、模型選取技術，以及關鍵業務指標。 課程等級：基礎 課程時長：1 小時 注意：本課程具有本地化的註釋/字幕。旁白保留英語。要顯示字幕，請按一下播放器右下角的.
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California Institute of Technology AWS Skill Builder

Fundamentals of Machine Learning and Artificial Intelligence (日本語)

Fundamentals of Machine Learning and Artificial Intelligence (日本語) - AWS Skill Builder このコースでは、機械学習 (ML) と人工知能 (AI) の基礎について学びます。AI、ML、深層学習、そして生成人工知能 (生成 AI) という新たな分野の関係を探ります。基本的な AI 用語をしっかりと理解し、これらの概念をより深く掘り下げるための基礎を築きます。さらに、AI と ML.
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California Institute of Technology Coursera

Generative AI and Blockchain

The "Generative AI and Blockchain" course offers a deep dive into the transformative potential of artificial intelligence and blockchain technology, specifically within the Web3 era. Start by exploring the Internet's evolution, from Web1 to the emerging Web3, and understand the Web3-AI stack's various layers. In Module 2, discover how blockch.
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A generative ai course is a fast-growing field of machine learning that can create new content, translate languages, write different types of creative content, and answer your questions in an informative way. It has great potential to revolutionize the way we create and use products.

A generative ai course refers to any artificial intelligence model that generates new data, information, or documents.

For example, many companies record their meetings, both live and virtual. Here are a few ways generative AI could transform these recordings:

And this is only a small part of all processes.

Generative AI Model Examples

There are a number of products using generative ai courses already available on the market – we'll give you a few examples below. The underlying principle of the generative ai courses at AI Eeducation varies depending on the specific model or algorithm used, but some common approaches include:

  1. Variational Autoencoders (VAEs) are a type of generative model that learns to encode input data into a latent space and then decode it back into the original data. The "variational" part of the name refers to the probabilistic nature of the latent space, allowing the model to generate a variety of outputs.

  2. Generative Adversarial Networks (GaN): GaNs consist of two neural networks, a generator and a discriminator, that are trained simultaneously through adversarial learning. The generator creates new data, and the discriminator evaluates how well the generated data matches the real data. The competition between the two networks causes the generator to improve over time in producing realistic outputs.

  3. Recurrent Neural Networks (RNNS) and Long Short-Term Memory (LSTM): These types of neural networks are often used to generate sequences such as text or music. RNNS and LSTM have memory that allows them to process a series of events over time, making them suitable for tasks where the order of elements is important.

  4. Transformer models: Transformer models, especially those with attention mechanisms, are very successful in various generative tasks. They can remember long-term dependencies and relationships in data, making them effective for tasks such as language translation and text generation.

  5. Autoencoders: Autoencoders consist of an encoder and a decoder, and they are trained to reconstruct the input data. Although they are primarily used for learning to represent and compress data, variations such as denoising autoencoders (e.g. in images) can be used for generative tasks.

An ai generative course involves feeding a model a large data set and optimizing its parameters to minimize the difference between the generated output and the real information. A model's ability to produce realistic and rich content depends on the complexity of its architecture, the quality and quantity of training data, and the optimization techniques used during training!