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Building a Foundation in Machine Learning: A Twitter Thread Synopsis

This Twitter thread started with a user named Jason Liu (@jxnlco) asking for recommendations on resources for building a foundation in machine learning, specifically deep learning. The thread received numerous responses, suggesting various courses, books, and online materials. A few advertisements related to land management and book writing also appeared in the thread.

Resources

Courses:

• fast.ai: Recommended by multiple users, particularly for practical deep learning. https://t.co/oV6KBk7Sms

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import os
import asyncio
import gradio as gr
from groq import AsyncGroq
import time

# Initialize Groq client
client = AsyncGroq(api_key=os.environ.get("GROQ_API_KEY"))

# Define model

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1. Each Video has its Own Page

1. Database: Ensure you have a videos table with fields like:

   Schema::create('videos', function (Blueprint $table) {
       $table->id();
       $table->string('title');
       $table->text('description');

$table->string('url'); // Link to video file

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AI Engineering Job Categories: Skills, Salaries, and Opportunities

1. AI/ML Engineer

Description: AI/ML Engineers develop and implement machine learning models and artificial intelligence systems. They work on cutting-edge technologies like large language models (LLMs), natural language processing (NLP), and computer vision.

Key Skills:

• Strong programming skills (Python, Java, C++)
• Deep understanding of machine learning algorithms
• Experience with ML frameworks (TensorFlow, PyTorch)

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Exciting AI Engineering Opportunities in 2024

1. AI Engineer at Raylu: Revolutionizing Financial Workflows

Are you passionate about applying AI to the world of finance? Raylu is seeking an AI Engineer to help automate financial workflows using cutting-edge LLM technology. This role offers a unique opportunity to work on decision-support AI systems for buy-side finance, combining domain-trained financial LLMs with custom workflow and knowledge base builders.

Location: New York Compensation: $135K-$200K, plus 0.15%-0.8% equity Apply here: Raylu Careers

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import os
from collections import deque
from typing import Dict, List
import time
import openai

# Configuration
OBJECTIVE = "Solve world hunger"
YOUR_API_KEY = "your-openai-api-key-here"
INITIAL_TASK = "Develop a comprehensive plan to solve world hunger"

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You will be provided with raw data on various TikTok influencer profiles in the following format:

<tiktok_profiles_data>
{{TIKTOK_PROFILES_DATA}}
</tiktok_profiles_data>

The fields are:
email, unique_id, url, bio_link, first_name, bio, region, language, follower_count, is_affiliate, number_of_ads, brands_worked_with, category_1, category_2, hashtag_1, hashtag_2, hashtag_3, hashtag_4, hashtag_5

You will also be given a list of keywords to look for:

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function downloadDocWithImagesInZip() {
  const doc = DocumentApp.getActiveDocument();
  const docName = doc.getName();
  const docBody = doc.getBody();
  const zip = Utilities.newBlob('', 'application/zip', `${docName}.zip`).getAs('application/zip');
  let content = '';
  let imageCount = 0;
  const totalElements = docBody.getNumChildren();
  
  for (let i = 0; i < totalElements; i++) {

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<prompt>
<html-content>
<!-- Paste the HTML content of the webpage here -->
</html-content>

Now that the HTML content of the webpage is provided above, please analyze the webpage and evaluate the following features to determine if the content is likely to be helpful based on Google's guidelines. For each feature, indicate "Yes" if the feature is present and "No" if it is not. Use the following chain of thought process for each feature:

1. Identify the specific HTML elements and attributes related to the feature within the provided HTML content.
2. Determine if the feature is present on the page based on the identified elements and attributes.

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<prompt>
Please analyze the following 10 examples of web spam content and 10 examples of good web content:
Web Spam Examples:
<spam_example1>
[URL, title, meta description, h1, h2, h3, h4 for web spam example 1]
</spam_example1>
<spam_example2>
[URL, title, meta description, h1, h2, h3, h4 for web spam example 2]
</spam_example2>
...