Generative AI Fundamentals Specialization

Uplatz offers comprehensive training on Generative AI Fundamentals Specialization This is video-based online course (self-paced training). You will be awarded Course Completion Certificate at the end of the course.

Generative AI refers to a category of artificial intelligence techniques and models that are designed to generate new, original content. Unlike traditional AI models that are often used for tasks such as classification, prediction, or optimization based on existing data, generative AI focuses on creating new data or content that is similar to, but not directly copied from, the input data it was trained on.

There are several approaches to generative AI, including:

1. Generative Adversarial Networks (GANs): GANs consist of two neural networks, a generator and a discriminator, which are trained simultaneously in a competitive manner. The generator creates new data samples, such as images or text, while the discriminator tries to distinguish between real and generated data. Through this adversarial process, GANs can generate highly realistic content, such as images, music, or even text.

2. Variational Autoencoders (VAEs): VAEs are another type of generative model that learns to represent input data in a lower-dimensional latent space. By sampling from this latent space, VAEs can generate new data samples that are similar to the training data. VAEs are often used for tasks such as image generation, text generation, and data imputation.

3. Transformer Models: Transformer models, such as OpenAI's GPT (Generative Pre-trained Transformer) series, are large-scale neural network architectures that are trained on vast amounts of text data. These models can generate coherent and contextually relevant text based on a given prompt or input. They have been widely used for tasks such as text generation, language translation, and dialogue generation.

Generative AI has numerous applications across various domains, including:

• Content Generation: Generating images, text, music, or other forms of creative content.
• Data Augmentation: Creating synthetic data samples to augment training datasets for machine learning models.
• Creative Design: Assisting artists and designers in generating new ideas, designs, or concepts.
• Simulation and Prediction: Generating synthetic scenarios or predictions for planning and decision-making purposes.
• Personalization: Generating personalized recommendations, product suggestions, or user interfaces based on individual preferences and behaviors.

Overall, generative AI holds great promise for unlocking creativity, enhancing productivity, and enabling new forms of human-computer interaction across a wide range of applications.

• Generative AI Fundamentals Specialization - all lectures 5:19:21

  • 1: Part 1 - Introduction to Generative AI 34:55
  • 2: Part 2 - Introduction to Generative AI Preview 30:05
  • 3: Part 3 - Introduction to Generative AI 31:14
  • 4: Part 1 - Introduction to Large Language Models (LLMs) 23:54
  • 5: Part 2 - Introduction to Large Language Models (LLMs) 36:54
  • 6: Part 1 - Prompt Engineering Basics 17:13
  • 7: Part 2 - Prompt Engineering Basics 46:41
  • 8: Responsible AI 44:48
  • 9: Generative AI - Impact, Considerations, and Ethical Issues 53:37

Generative AI Fundamentals Specialization - Course Syllabus

Introduction to Generative AI

• What is Generative AI?

• Journey of Generative AI

• How does Generative AI works?

• Applications of generative AI in different sectors and industries

Introduction to Large Language Models (LLM)

• What is LLM?

• How do large language models work?

• General Architecture of Large Language Model

• What can a language model do?

• What are the challenges and limitations of LLM?

• LLM in the AI landscape

• LLM use cases/Application

Generative AI: Prompt Engineering Basics

• What is prompt Engineering?

• Relevance of prompt engineering in generative AI models

• Creating prompts and explore examples of impactful prompts

• Commonly used tools for prompt engineering to aid with prompt engineering

Introduction to Responsible AI

• What is Responsible AI?

• Why it's important?

• How Google implements responsible AI in their products?

• Google's 7 AI principles

Generative AI: Impact, Considerations, and Ethical Issues

• Limitations of generative AI and the related concerns

• Identify the ethical issues, concerns, and misuses associated with generative AI

• Considerations for the responsible use of generative AI

• Economic and social impact of generative AI

Everyone

Passion and determination to achieve your goals!

• Generative AI Scientist
• AI Scientist
• Generative AI Field Solutions Architect Manager
• Machine Learning Scientist
• Machine Learning Engineer
• Generative AI Specialist
• Generative AI - Senior Technical Product Owner
• Generative AI Developer
• Generative AI Expert
• Generative AI- Data Pipeline Engineer
• Deep Learning Engineer
• Data Scientist
• Generative AI Lead
• AI Strategist
• Data Science Manager (Generative AI)

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Reed courses certificate of completion

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Uplatz Certificate of Completion

Digital certificate - Included