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| File Name: | LangChain & OpenAI ChatGPT API for No-Code Python Developers |
| Content Source: | https://www.udemy.com/course/langchain-openai-chatgpt-api-for-no-code-python-developers/ |
| Genre / Category: | Other Tutorials |
| File Size : | 336MB |
| Publisher: | udemy |
| Updated and Published: | October 28, 2023 |
Future of Coding: Let AI do your Python Projects with Zero Code
The goal of this course is to teach you how to build a tool that helps increasing programmers productivity by utilizing LLMs for zero-code development. This tool is called PyGenX which stands for Python Generator and Executor. This tool basically generates Python code and executes it based on a user’s prompt or command. Now, there has been increasing concerns about data privacy and security when using AI models like ChatGPT for various purposes including data analysis, machine learning and and other data related tasks. Concerns often revolve around data storage and retention, data usage, data sharing with third-party, and other ethical considerations. With such concerns in mind, PyGenX can take care of data privacy when zero-code developing in Python. This is done through data-agnostic techniques which are designed to perform the same programmers’ tasks without accessing, storing, processing, or utilizing user-specific data. In this way, LLMs vendors are not exposed to your data even if those LLMs run in the cloud such as GPT-4 or GPT-3.5-turbo. Running PyGenX locally—i.e., on your own hardware—offers several advantages, especially if you’re concerned about privacy, latency, and data control. Here are the main benefits as a result of using PyGenX:
Data Privacy: Running the application locally ensures that your data never leaves your machine, offering a high level of data security and privacy.
Low Latency: Local execution can eliminate the round-trip time to the cloud server, resulting in faster response times. This is important for real-time applications.
Customizations: Running locally can offer greater flexibility in terms of customization and integration with other local systems and data sources.
Resource Utilization: When you run the LLM-generated code on your local machine, you have more control over hardware resource utilization of CPU, GPU and memory.
PyGenX greatly increases programmers productivity for various application such as: data analysis and visualization, machine learning and deep learning development, code documentation and refactoring, automation and scripting, file operations, web development, and other software development applications. Basically, the strength and effectiveness of zero-code programming in Python using PyGenX depends on the power of the LLM used with it.
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