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Generative AI courses

911 Courses

California Institute of Technology Coursera

Introduction to Large Language Models - Deutsch

Introduction to Large Language Models - Deutsch In diesem Einführungskurs im Microlearning-Format wird untersucht, was Large Language Models (LLM) sind, für welche Anwendungsfälle sie genutzt werden können und wie die LLM-Leistung durch Feinabstimmung von Prompts gesteigert werden kann. Darüber hinaus werden Tools von Google be.
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California Institute of Technology AWS Skill Builder

AWS ML Engineer Associate Curriculum Overview (Japanese)

AWS ML Engineer Associate Curriculum のこの入門コースでは、機械学習 (ML) の基礎を復習し、ML と AI の進化について確認します。ML ライフサイクルの最初のステップとして、ビジネス目標を特定し、そのビジネス目標に基づいて ML の問題を定式化します。最後に、ML モデルの構築、トレーニング、デプロイに使用できるフルマネージド型 AWS サービスである Amazon SageMak.
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California Institute of Technology AWS Skill Builder

AWS ML Visão geral do curso de engenheiro associado (Português) | AWS ML Engineer Associate Curriculum Overview (Portuguese)

Neste curso introdutório à grade curricular de engenheiros de ML associados da AWS, você analisa os conceitos básicos de machine learning (ML) e examina a evolução do machine learning e da IA. Você explora as primeiras etapas do ciclo de vida do ML, identificando uma meta de negócios e formulando um problema de ML com base nessa meta de negócios. F.
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California Institute of Technology AWS Skill Builder

Essentials of Prompt Engineering (Korean)

Essentials of Prompt Engineering (Korean) 이 과정에서는 효과적인 프롬프트를 만드는 데 필요한 기본 사항을 소개합니다. 다양한 사용 사례에 맞게 프롬프트를 세분화하고 최적화하는 방법을 이해할 수 있습니다. 또한 zero-shot, few-shot 및 chain-of-thought 프롬프팅과 같은 기법도 살펴볼 수 있습니다. 마지막으로 프롬프트 엔지니어링과 관련된 잠재적 위험을 식별.
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California Institute of Technology AWS Skill Builder

Amazon Q Business Getting Started (Indonesian)

Amazon Q Business Getting Started (Indonesian) Amazon Q Business adalah asisten yang didukung kecerdasan buatan generatif (AI generatif) yang dapat menjawab pertanyaan, membuat konten, membuat ringkasan, dan menyelesaikan tugas, semua berdasarkan informasi di korporasi Anda. Dalam kursus Getting Started ini, Anda akan mempelajari manfaat, fitur,.
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California Institute of Technology AWS Skill Builder

Amazon Q Introduction (Indonesian)

Amazon Q Introduction (Indonesian) Kursus ini memberikan gambaran umum tingkat tinggi tentang Amazon Q, asisten yang didukung kecerdasan buatan (AI) generatif. Anda akan mempelajari kasus penggunaan dan manfaat menghubungkan Amazon Q ke informasi, kode, dan sistem perusahaan Anda. Anda juga akan menemukan informasi tambahan untuk melanjutkan perj.
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California Institute of Technology AWS Skill Builder

Amazon Q Business Getting Started (Japanese) (Sub) 日本語字幕版

Amazon Q Business Getting Started (Japanese) (Sub) 日本語字幕版 *このコースは機械翻訳で対応されています。 Amazon Q Business は、生成人工知能 (生成 AI) を活用したアシスタントで、質問への回答、コンテンツの生成、要約の作成、およびタスクの完了をすべて企業内の情報に基づいて行うことができます。この入門コースでは、Amazon Q Business を使用するメリット、機.
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California Institute of Technology AWS Skill Builder

Amazon Q Business Getting Started (Japanese)

*このコースは機械翻訳で対応されています。 Amazon Q Business は、生成人工知能 (生成 AI) を活用したアシスタントで、質問への回答、コンテンツの生成、要約の作成、およびタスクの完了をすべて企業内の情報に基づいて行うことができます。この入門コースでは、Amazon Q Business を使用するメリット、機能、一般的なユースケース、技術概念、コストについて学びます。.
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California Institute of Technology AWS Skill Builder

Amazon Q Introduction (Korean)

이 과정에서는 생성형 인공 지능(AI) 기반 어시스턴트인 Amazon Q에 대해 개략적으로 설명합니다. Amazon Q를 회사 정보, 코드 및 시스템에 연결할 때의 사용 사례와 이점에 대해 알아봅니다. 또한 특정 사용 사례에 대한 관심을 기반으로 학습 여정을 발전시킬 수 있는 추가 정보도 찾을 수 있습니다. 기술 학습자와 비기술 학습자 모두 어떻게 Amazon Q가 안전한 방식으로 생.
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California Institute of Technology AWS Skill Builder

Amazon Q Introduction (Japanese)

Amazon Q Introduction (Japanese) *このコースは機械翻訳で対応されています。 このコースでは、生成人工知能 (AI) 搭載アシスタントである Amazon Q の概要を説明します。Amazon Q を会社の情報、コード、システムにリンクすることのユースケースと利点について学びます。また、特定のユースケースへの関心に基づいて、学習を進めるための追加情報も記載されています。技術.
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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!