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🌈SPOTO just dropped a bunch of 100% free study kits for 2026 – covering #Cisco, #AWS, #PMP, #AI, #Python, #Excel, and #Cybersecurity
💥No signup traps, no hidden fees – just click and download.
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🪜 Online FREE Course → https://bit.ly/4vHFJSz
☁️ FREE AI Materials → https://bit.ly/4wdu7X6
📊 Cloud Study Guide → https://bit.ly/4y0HyeW
🧠 Free Mock Exam → https://bit.ly/4ff8jos
Tag a friend who's also on this journey – Get certified together! 💪
🌐 Join the community: https://chat.whatsapp.com/FmbIbbqm2QhKglVpVTSH4d/
📲 Need personalized help? → https://wa.link/6k7042
❤3
🔖 One of the most useful books on Agentic AI
This is not just a textbook, but a comprehensive overview of modern LLMs, model training, RL, inference, quality assessment, and building AI agents.
It's an excellent option to get a holistic picture and understand which topics deserve deeper study.
⛓️ Link to the book
https://arxiv.org/abs/2606.24937
This is not just a textbook, but a comprehensive overview of modern LLMs, model training, RL, inference, quality assessment, and building AI agents.
It's an excellent option to get a holistic picture and understand which topics deserve deeper study.
⛓️ Link to the book
https://arxiv.org/abs/2606.24937
❤6
The diagram shows how a modern AI agent processes requests: from preparing data and indexing knowledge to searching for relevant context, working with memory, and using external tools.
This is a good way to quickly understand how RAG systems are structured, search by embeddings, memory, knowledge base, and agentic workflow.
Save it to not lose it!
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❤5
Towards Data Science
How Far Can Classical NLP Go? From Bag-of-Words to Stacking on Spooky Author Identification | Towards Data Science
An end-to-end classical NLP experiment on Kaggle’s Spooky Author Identification task: from Vowpal Wabbit and TF-IDF/NB-SVM baselines to a tuned stacked ensemble, with a compact representation survey of Bag-of-Words, BM25, Word2Vec, and FastText for context.
🚀 Looking for a portfolio-ready NLP project?
I recently published an end-to-end walkthrough on Towards Data Science using Kaggle’s Spooky Author Identification dataset.
You’ll see how far classical NLP can go with:
📝 Bag-of-Words and TF-IDF
🔤 Character n-grams
📊 Model comparison
🧩 Ensemble stacking
It’s a practical project for anyone preparing for an ML/DS role, with no deep learning required. I walk through the entire workflow step by step:
🔗 https://towardsdatascience.com/how-far-can-classical-nlp-go-from-bag-of-words-to-stacking-on-spooky-author-identification/
I recently published an end-to-end walkthrough on Towards Data Science using Kaggle’s Spooky Author Identification dataset.
You’ll see how far classical NLP can go with:
📝 Bag-of-Words and TF-IDF
🔤 Character n-grams
📊 Model comparison
🧩 Ensemble stacking
It’s a practical project for anyone preparing for an ML/DS role, with no deep learning required. I walk through the entire workflow step by step:
🔗 https://towardsdatascience.com/how-far-can-classical-nlp-go-from-bag-of-words-to-stacking-on-spooky-author-identification/
❤1
🔖 How to Reduce the Cost of LLM Inference by up to 90%
If your AI agents are constantly sending the same context, consider using LMCache. 🚀
This open-source system manages a KV cache, allowing you to reuse already computed representations instead of recalculating them for each request. 🧠
As a result:
⚡️ Up to 14x faster Time To First Token;
⚡️ Up to 4x faster decoding;
⚡️ Significant savings in GPU resources and inference costs. 💰
⛓️ Link to GitHub
https://github.com/LMCache/LMCache
#LLM #AIOptimization #LMCache #GPU #CostReduction #AIEngineering
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If your AI agents are constantly sending the same context, consider using LMCache. 🚀
This open-source system manages a KV cache, allowing you to reuse already computed representations instead of recalculating them for each request. 🧠
As a result:
⚡️ Up to 14x faster Time To First Token;
⚡️ Up to 4x faster decoding;
⚡️ Significant savings in GPU resources and inference costs. 💰
⛓️ Link to GitHub
https://github.com/LMCache/LMCache
#LLM #AIOptimization #LMCache #GPU #CostReduction #AIEngineering
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🗣You can communicate with your assistant anywhere: Telegram, WhatsApp, Slack, WeChat, or Discord.
It can help with email, draft replies to text or voice messages, send emails, set reminders, create and manage spreadsheets, generate images, create videos, edit short videos, work with PDFs, Notion, Obsidian, and much more.
Dozens of skills are already available, and the list is constantly growing. If you need a custom skill for your workflow, business, or team, the Amplify team will quickly develop and implement it.
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Promo code:
It's a familiar problem: everyone wants a personal AI assistant, but building one from scratch usually means servers, API keys, integrations, maintenance, and a ton of technical overhead.
Amplify takes care of all of this for you. In about 5 minutes, you'll have a personal AI agent connected to your Google account—Gmail, Drive, Calendar, Docs, Slides, Sheets, and more. Google integration is officially verified.
🗣You can communicate with your assistant anywhere: Telegram, WhatsApp, Slack, WeChat, or Discord.
It can help with email, draft replies to text or voice messages, send emails, set reminders, create and manage spreadsheets, generate images, create videos, edit short videos, work with PDFs, Notion, Obsidian, and much more.
Dozens of skills are already available, and the list is constantly growing. If you need a custom skill for your workflow, business, or team, the Amplify team will quickly develop and implement it.
The pricing is simple: $10 per month plus pay only for the features you actually use. No confusing token system—the cost of each action is clearly displayed in your dashboard.
And if you already have a ChatGPT subscription, you can sign up and essentially avoid paying separately for the AI model.
😎For subscribers: use the promo code and get two months free + $10 credit to your balance.
After registering, you'll receive your own promo code. If someone else signs up with it, you'll get an extra month free.
Try Amplify here: https://getamplify.team/
Promo code:
CODEPROGRAMMER❤6
Forwarded from Machine Learning with Python
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❤5
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Forwarded from Machine Learning
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