OPENAI_API_KEY

gpt.tar.gz

Python

Golang

Java

Sandbox

Python_textGen

In this course, you’ll start a transformative learning journey exploring the applications of GPT-3 in AI and gaining hands-on experience with this powerful language model.

You’ll start the course by understanding the basics of GPT-3 and its applications across domains and then learn to use the OpenAI API with Python, Go, and Java. Next, you’ll dive into prompting techniques with GPT-3, exploring essential topics to understand how to use GPT-3 for next-gen startups with real-world use cases and applications. You’ll explore its role in prominent companies like GitHub, Algolia, and Microsoft’s Azure. Lastly, you will navigate the ethical considerations of GPT-3, addressing issues like AI bias, anti-bias countermeasures, and the environmental impact of LLMs.

After completing this course, you’ll gain a deep understanding of GPT-3. Whether you are an aspiring developer, entrepreneur, or professional transitioning to AI-focused roles, this course equips you with the skills to advance your career.

Unleashing the Power of AI with OpenAI’s GPT-3

Deep learning requires labeled data for training.

Deep learning involves neural networks with multiple layers.

Machine learning only uses supervised learning algorithms.

Machine learning is limited to linear models.

What is a key characteristic that differentiates deep learning from traditional machine learning?

What is a key characteristic that differentiates deep learning from traditional machine learning?

Image recognition tasks with a large amount of labeled data.

Natural language processing (NLP) tasks, such as sentiment analysis or language translation.

Tasks involving unstructured data, such as audio and speech processing.


Tasks where the dataset is relatively small and the interpretability of the model is crucial.

In which scenario would traditional machine learning (ML) methods be more suitable compared to deep learning (DL)?

In which scenario would traditional machine learning (ML) methods be more suitable compared to deep learning (DL)?

The system is designed to classify whether a given text belongs to a specific genre, such as mystery or romance.

The system analyzes a dataset containing images of various animals and learns to identify and label the species of each animal.



The system is trained on a dataset consisting of sentences from novels and aims to generate new, coherent text passages that resemble the writing style of the training data.

The system processes a dataset of audio recordings and learns to recognize and transcribe spoken words accurately.

Consider a scenario where you are working on a project to develop an intelligent system to generate realistic human-like text based on the patterns it is trained on. Which statement is most aligned with the concept of generative models?

Consider a scenario where you are working on a project to develop an intelligent system to generate realistic human-like text based on the patterns it is trained on. Which statement is most aligned with the concept of generative models?

The system is designed to identify specific facial features, such as eyes, nose, and mouth, in given images.

The system is trained on a dataset of facial images and aims to generate new, lifelike images of human faces that resemble those in the training data.



The system analyzes a dataset of labeled images to categorize whether a person in the photo is smiling or not.



The system processes a dataset of voice recordings and learns to recognize and differentiate between various speech patterns.

Imagine you are working on a project to develop an artificial intelligence system for generating realistic images of human faces. Which statement best describes the nature of generative models in this scenario?

Imagine you are working on a project to develop an artificial intelligence system for generating realistic images of human faces. Which statement best describes the nature of generative models in this scenario?

To process input images and extract visual features.

To take the context vector and produce a natural language caption for the input image.

To generate a fixed-size context vector representing the content of the input image.

To convert natural language captions into images.

In a sequence-to-sequence model designed for image captioning, what is the role of the Decoder?

In a sequence-to-sequence model designed for image captioning, what is the role of the Decoder?

To process input images to extract visual features.

To processes input images, extracts visual features, and creates a context vector.

To take the context vector and produce a natural language caption for the input image.


In a sequence-to-sequence model designed for image captioning, what is the role of the Encoder?

In a sequence-to-sequence model designed for image captioning, what is the role of the Encoder?

To generate a concise summary of the input document.

To convert the context vector into a document.


To process input audio signals and extract relevant features.

To receive a lengthy document and convert its important information into a context vector.

For a sequence-to-sequence model used in text summarization, what is the primary function of the Encoder?

For a sequence-to-sequence model used in text summarization, what is the primary function of the Encoder?

To generate a fixed-size context vector representing the content of the input document.

To use the context vector to generate a summary of the input document, capturing its key points.

To process the content of the document to generate graphs. 

To create a context vector representing the semantic information of the input text.

For a sequence-to-sequence model used in text summarization, what is the primary function of the Decoder?

For a sequence-to-sequence model used in text summarization, what is the primary function of the Decoder?

Let’s test your knowledge of the concepts covered in this chapter.

Course Introduction

The Large Language Model Revolution

Getting Started with OpenAI API

GPT-3 and Programming

GPT-3 Enabler for Next-Gen Startups

GPT-3 as the Next Step in Corporate Innovation

GPT-3—The Good, the Bad, and the Ugly

Conclusion—Democratizing Access to AI

GPT-3: Prompt Engineering and Uses

Quiz: The Large Language Model Revolution