Here are ten recent standout articles from Towards Data Science – Medium:
1. “New ChatGPT Prompt Engineering Technique: Program Simulation” by Giuseppe Scalamogna explains a prompt-engineering technique that simulates a program to improve the performance of ChatGPT.
2. “How to Program a Neural Network” by Callum Bruce provides a step-by-step guide for coding neural networks from scratch, helping readers gain both understanding and practical knowledge.
3. “Don’t Start Your Data Science Journey Without These 5 Must-Do Steps” by Khouloud El Alami offers actionable insights for data scientists at the early stages of their careers.
4. “How to Design a Roadmap for a Machine Learning Project” by Heather Couture presents a framework to streamline the design of machine learning projects.
5. “Machine Learning’s Public Perception Problem” by Stephanie Kirmer reflects on the limitations of advanced machine learning and the importance of educating the public about its use.
6. “How to Build an LLM from Scratch” by Shawhin Talebi discusses the practical considerations involved in creating a foundation LLM (like GPT-3 and Falcon).
7. “Your Own Personal ChatGPT” by Robert A. Gonsalves provides an overview of how to fine-tune OpenAI’s GPT-3.5 Turbo model for new tasks using custom data.
8. “How to Build a Multi-GPU System for Deep Learning in 2023” by Antonis Makropoulos offers a tutorial on deep-learning hardware and infrastructure, including guidance on choosing the right components.
9. “Meta-Heuristics Explained: Ant Colony Optimization” by Hennie de Harder introduces the ant-colony optimization algorithm and explains its inspiration from ants’ foraging behaviors.
10. “Falcon 180B: Can It Run on Your Computer?” by Benjamin Marie explores the feasibility of running the Falcon 180B model on consumer-grade hardware, providing insights for those considering local machine vs. cloud services.
(Source: Towards Data Science – Medium)
Prompt Engineering Tips, a Neural Network How-To, and Other Recent Must-Reads
We’ve been feeling a nice jolt of energy in the past month, as many of our authors switched gears from summer mode into fall, with a renewed focus on learning, experimenting, and launching new projects.
We’ve published far more excellent posts in September than we could ever highlight here, but we still wanted to make sure you don’t miss some of our recent standouts. Below are ten articles that resonated strongly with our community—whether it’s by the sheer number of readers they attracted, the lively conversations they inspired, or the cutting-edge topics they covered. We’re sure you’ll enjoy exploring them.
New ChatGPT Prompt Engineering Technique: Program Simulation
It’s fairly rare for an author’s TDS debut to become one of the most popular articles of the month, but Giuseppe Scalamogna’s article pulled off this feat thanks to an accessible and timely explainer on program simulation: a prompt-engineering technique that “aims to make ChatGPT operate in a way that simulates a program,” and can lead to impressive results.
How to Program a Neural Network
Tutorials on neural networks are easy to find. Less common? A step-by-step guide that helps readers gain both an intuitive understanding of how they work, and the practical know-how for coding them from scratch. Callum Bruce delivered precisely that in his latest contribution.
Don’t Start Your Data Science Journey Without These 5 Must-Do Steps — A Spotify Data Scientist’s Full Guide
If you’ve already discovered Khouloud El Alami’s writing, you won’t be surprised to learn her most recent post offers actionable insights presented in an accessible and engaging way. This one is geared towards data scientists at the earliest stages of their career: if you’re not sure how to set yourself on the right path, Khouloud’s advice will help you find your bearings.
Continue reading the rest of the reviews…
Based on the meeting notes provided, I have identified the following action items:
1. Assign Giuseppe Scalamogna:
– Explore and implement the prompt engineering technique of program simulation in ChatGPT.2. Assign Callum Bruce:
– Develop a step-by-step guide for readers to gain an intuitive understanding of neural networks and the practical know-how for coding them from scratch.3. Assign Khouloud El Alami:
– Create a comprehensive guide for data scientists at the early stages of their career, focusing on the necessary steps to set oneself on the right path.4. Assign Heather Couture:
– Present a helpful framework for streamlining the design of machine learning projects, covering all aspects from literature review to post-deployment maintenance.5. Assign Stephanie Kirmer:
– Research and provide insights on the public perception problem surrounding machine learning, with a focus on the need for explanation and education.6. Assign Shawhin Talebi:
– Review and understand the key aspects of creating a foundation LLM (Language Learning Model), similar to models like GPT-3 and Falcon.7. Assign Robert A. Gonsalves:
– Develop a detailed overview of the fine-tuning process for OpenAI’s GPT-3.5 Turbo model using custom data, enabling readers to build and tinker with language models.8. Assign Antonis Makropoulos:
– Produce a tutorial on deep-learning hardware and infrastructure, specifically focused on building a multi-GPU system for deep learning in 2023.9. Assign Hennie de Harder:
– Deep dive into ant-colony optimization as an algorithm and showcase its real-world problem-solving capabilities.10. Assign Benjamin Marie:
– Investigate the feasibility of running the Falcon 180B model on consumer-grade hardware, addressing the pros and cons of using local machines vs. cloud services.Note: These action items are suggestions based on the content of the meeting notes provided. It is important to discuss and confirm the responsibilities with the individuals involved before finalizing the assignments.
