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Atlas Orion
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Atlas Orion
@tec_Orion0
Creating impactful content & digital value. Helping brands grow with strategy-driven promotion. DM for paid collaboration.💼📈
Katılım Aralık 2025
31 Takip Edilen512 Takipçiler
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So let me get this straight:
Built by ex-McKinsey, Uses actual consulting frameworks (MECE, top-down) Generates real research with sources, Outputs editable PPTX in your template
And it costs less than one hour of a junior consultant's time?
The big firms should be worried.
Perceptis AI@perceptis_ai
Real business presentations demand more than good design. Meet Perceptis AI - built for people who win on their thinking, not their templates. A prompt becomes a boardroom-ready, editable PPT deck. Grounded in real research and structured like top‑tier consulting deliverable.
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Atlas Orion retweetledi
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Faceless YouTube channels are one of the best ways to make money.
Here's how to make faceless YT videos with AI,start earning online.
I just made a FULL GUIDE on how you can make $5000
Usually, charges $699 for this,but today I'm giving it away for FREE.
Follow @siddhantgarg33

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𝗖𝗮𝗺𝗯𝗿𝗶𝗱𝗴𝗲 𝗨𝗻𝗶𝘃𝗲𝗿𝘀𝗶𝘁𝘆 𝗶𝘀 𝗼𝗳𝗳𝗲𝗿𝗶𝗻𝗴 𝗙𝗥𝗘𝗘 𝗯𝗼𝗼𝗸𝘀 𝗼𝗻 𝗔𝗜 & 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴
Learn from world-class academic resources. Start with foundations, move toward advanced ML & Deep Learning.
𝗙𝗼𝘂𝗻𝗱𝗮𝘁𝗶𝗼𝗻𝘀 𝗶𝗻 𝗠𝗮𝘁𝗵 & 𝗣𝗿𝗼𝗯𝗮𝗯𝗶𝗹𝗶𝘁𝘆
1. Mathematics for Machine Learning mml-book.github.io/book/mml-book.…
2. Probability: Theory and Examples sites.math.duke.edu/~rtd/PTE/PTE5_…
3. Elementary Probability for Applications sites.math.duke.edu/~rtd/EP4A/EP4A…
𝗖𝗼𝗿𝗲 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴
4. Understanding Machine Learning cs.huji.ac.il/~shais/Underst…
5. Algorithmic Aspects of Machine Learning people.csail.mit.edu/moitra/docs/bo…
6. Mathematical Analysis of ML Algorithms tongzhang-ml.org/lt-book/lt-boo…
𝗗𝗲𝗲𝗽 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 & 𝗡𝗲𝘂𝗿𝗮𝗹 𝗡𝗲𝘁𝘄𝗼𝗿𝗸𝘀
7. Machine Learning with Neural Networks arxiv.org/pdf/1901.05639
8. The Principles of Deep Learning Theory arxiv.org/pdf/2106.10165
9. Deep Learning on Graphs yaoma24.github.io/dlg_book/dlg_b…
𝗔𝗱𝘃𝗮𝗻𝗰𝗲𝗱 𝗗𝗮𝘁𝗮 & 𝗔𝗻𝗮𝗹𝘆𝘀𝗶𝘀
10. Advanced Data Analysis stat.cmu.edu/~cshalizi/ADAf…
Tip for learners: Start with math & probability → move to core ML → then explore deep learning theory → finish with advanced analysis.
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