StBash

'Pressure, Vol.1' All around nice guy.

[StBash @StBashSA]

@hkproj Technically correct, some of the guys from DeepSeek are Quantitative Analysts (Quants)

[StBash @StBashSA]

@EasyEquities How do I activate my crypto account?

[StBash @StBashSA]

How can I fund my account, since the Capitec widget is not working? @CapitecBankSA
@EasyEquities

[StBash @StBashSA]

@YoungInvestor03 @EasyEquities @CapitecBankSA Been having this issue for two weeks now

[StBash @StBashSA]

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[X @TheMsterDoctor1]

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[StBash @StBashSA]

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[Akshay 🚀 @akshay_pachaar]

The Simplest Guide to Prompting Engineering!

You realise the power of prompt engineering only once you understand and start using it properly.

Today, we will understand & explore various types of prompting techniques with illustrative examples:

- Zero-shot prompting
- Few-shot prompting
- Chain of thought prompting
- Tree of thought prompting

1️⃣ Zero-shot prompting

Zero-shot prompting refers to the ability of an AI model to generate meaningful responses or complete tasks without any prior training on specific prompts.

Here's an example👇

2️⃣ Few-shot prompting

In contrast to zero-shot prompting, few-shot prompting involves training an AI model with only a small amount of data or examples.

This technique allows the model to quickly adapt and generate responses based on limited examples provided by the user.

Here's an example👇

3️⃣ Chain of thought prompting

Chain of thought prompting is a method where user provides prompts in a sequential manner, building upon previous responses.

By following this approach, the AI model can generate more coherent and contextually relevant outputs, mimicking human-like conversation flow.

Here's an example👇

4️⃣ Tree of thought prompting (ToT)

Similar to chain of thought prompting, tree of thought prompting utilizes branching pathways & encourages exploration over various chain of thoughts.

Users can explore different possibilities or directions within the conversation by structuring their prompts as branches in a tree-like structure.

This technique enables greater flexibility, exploration & backtracking during interactions with the AI model.

Broadly speaking ToT involves two components:
1. Thought generation
2. Thought Evaluation

Let's understand this with an example!

We use ToT for a Game of 24:

It's is a mathematical reasoning challenge, where the goal is to use 4 numbers and basic arithmetic operations (+-*/) to obtain 24.

For example, given input “4 9 10 13”, a solution output could be “(10 - 4) * (13 - 9) = 24”.

(Refer the image below as you read ahead)

To frame Game of 24 into ToT, we decompose the thoughts into 3 steps, where each step is an intermediate equation.

🔹Figure 2(a): Though Generation

Decompose the thoughts into 3 steps, each an intermediate equation.

What happens at each step (tree node):

- Extract the “left” numbers.
- Prompt the LM (Language Model) to propose possible next steps.
- Use the same “propose prompt” for all 3 thought steps.
- Note: Only one example with 4 input numbers is provided.

Breadth-first search (BFS) in ToT:
- At each step, retain the best b = 5 candidates.

🔸Figure 2(b): Evaluation

- Prompt LM to evaluate each thought candidate as “sure/maybe/impossible” with regard to reaching 24.
- The goal is to promote correct partial solutions that can be confirmed with few lookahead trials.
- Eliminate impossible partial solutions
- Retain the rest labeled as “maybe”.
- Sample values 3 times for each thought.

Check this out👇

If you Enjoyed reading this & are interested in

- ML/MLOps 🛠
- CV/NLP 🗣
- LLMs 🧠
- AI Engineering ⚙️

Find me → @akshay_pachaar ✔️
Everyday, I share tutorials on the above topics!

You can read more about this article & more on AI Engineering in my weekly Newsletter MLSpeing!

It's FREE, link to subscription in next tweet!

Cheers! 🥂

[ASUS_za @ASUS_za]

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[Het Mehta @hetmehtaa]

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[Valeriy M., PhD, MBA, CQF @predict_addict]

428 page course 🔥🔥🔥🔥🔥 on Causal Inference from @UCBerkeley 🌟 🌟 🌟🌟 🌟

#causalinference #causality #machinelearning

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[10-K Diver @10kdiver]

1/

Get a cup of coffee.

In this thread, I'll walk you through the key differences between *earnings* and *cash flows*.

The punch line: Just because a company reports $1 of *earnings*, it does NOT mean the company has $1 more *cash* to distribute to owners.

[Dr MuVenda @Ndi_Muvenda_]

Graduation ceremony of the year 🔥🔥🔥🔥🔥

I see this as motivation to those boys and girls there 🔥🔥

[StBash @StBashSA]

@WoganMay 😂😂😂

[Nick Huber @sweatystartup]

A thread on how real estate investors, developers and operators can make millions a year and pay almost nothing in TAXES by using depreciation, bonus depreciation, and 1031 exchanges.

A thread on how it works:

[Sahil Bloom @SahilBloom]

Lifelong learning is a competitive advantage.

But contrary to what you’ve been told, lifelong learners are built, not born.

THREAD: 20 lifelong learning habits you can start developing today.

[IG:Joy-Zelda @joy_zelda]

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