🔥 8 skills = 8 free certifications >>>
AI (Microsoft) -
https://learn.microsoft.com/en-us/training/paths/get-started-artificial-intelligence/
Deep learning (NVIDIA) -
https://learn.nvidia.com/en-us/training/self-paced-courses
Data science (IBM) -
https://skillsbuild.org/students/course-catalog/data-science
Data Analyst (Microsoft) -
https://learn.microsoft.com/en-us/training/paths/data-analytics-microsoft/
Python (Microsoft) -
https://learn.microsoft.com/en-us/shows/intro-to-python-development/
SQL (Infosys) -
https://www.coursejoiner.com/freeonlinecourses/infosys-free-certification-course-9/
Java (Infosys) -
https://www.coursejoiner.com/uncategorized/infosys-launched-free-java-certification-course/
Cloud computing (AWS) -
https://explore.skillbuilder.aws/learn/course/134/aws-cloud-practitioner-essentials
@Machine_learn
AI (Microsoft) -
https://learn.microsoft.com/en-us/training/paths/get-started-artificial-intelligence/
Deep learning (NVIDIA) -
https://learn.nvidia.com/en-us/training/self-paced-courses
Data science (IBM) -
https://skillsbuild.org/students/course-catalog/data-science
Data Analyst (Microsoft) -
https://learn.microsoft.com/en-us/training/paths/data-analytics-microsoft/
Python (Microsoft) -
https://learn.microsoft.com/en-us/shows/intro-to-python-development/
SQL (Infosys) -
https://www.coursejoiner.com/freeonlinecourses/infosys-free-certification-course-9/
Java (Infosys) -
https://www.coursejoiner.com/uncategorized/infosys-launched-free-java-certification-course/
Cloud computing (AWS) -
https://explore.skillbuilder.aws/learn/course/134/aws-cloud-practitioner-essentials
@Machine_learn
NVIDIA
NVIDIA Self-Paced Training and Courses
Learn how to set up an end-to-end project in eight hours or how to apply a specific technology or development technique in two hours—anytime, anywhere.
❤2👍1
"The Mathematics of Bitcoin" is a concise work that analyzes Bitcoin from a mathematical perspective. 📊
It utilizes probability theory, stochastic processes, martingales, combinatorics, and special functions to explore the mechanisms of the Bitcoin protocol. 🧮
In particular, the authors examine the probability of double-spending, the profitability of mining, block generation, miner strategies, and the resilience of the protocol. ⛏️
If you want to delve deeper, I also recommend "Bitcoin and Cryptocurrency Technologies" from Princeton University. This is a much broader introduction to cryptographic hash functions, digital signatures, consensus, Proof of Work, mining, transactions, anonymity, security, and the incentive system in cryptocurrencies. 🎓
The Mathematics of Bitcoin:
https://arxiv.org/pdf/2003.00001
Bitcoin and Cryptocurrency Technologies:
https://d28rh4a8wq0iu5.cloudfront.net/bitcointech/readings/princeton_bitcoin_book.pdf
@Machine_learn
It utilizes probability theory, stochastic processes, martingales, combinatorics, and special functions to explore the mechanisms of the Bitcoin protocol. 🧮
In particular, the authors examine the probability of double-spending, the profitability of mining, block generation, miner strategies, and the resilience of the protocol. ⛏️
If you want to delve deeper, I also recommend "Bitcoin and Cryptocurrency Technologies" from Princeton University. This is a much broader introduction to cryptographic hash functions, digital signatures, consensus, Proof of Work, mining, transactions, anonymity, security, and the incentive system in cryptocurrencies. 🎓
The Mathematics of Bitcoin:
https://arxiv.org/pdf/2003.00001
Bitcoin and Cryptocurrency Technologies:
https://d28rh4a8wq0iu5.cloudfront.net/bitcointech/readings/princeton_bitcoin_book.pdf
@Machine_learn
❤3
🔖 ML algorithms in visualizations
A useful repository that helps you understand how machine learning algorithms work – through interactive diagrams and step-by-step explanations.
You can run it in your browser or locally using Docker.
⛓ Link to GitHub
https://github.com/gavinkhung/machine-learning-visualized
@Machine_learn
A useful repository that helps you understand how machine learning algorithms work – through interactive diagrams and step-by-step explanations.
You can run it in your browser or locally using Docker.
⛓ Link to GitHub
https://github.com/gavinkhung/machine-learning-visualized
@Machine_learn
❤8
Matrix Calculus for Machine Learning and Beyond! — a free ebook from MIT.
This is the 2025 MIT textbook by Alan Edelman, Steven G. Johnson, and Paige Bright.
The book directly connects matrix calculus to modern machine learning.
Inside:
* Derivatives of matrices and vectors
* Jacobian and Hessian
* Matrix decompositions
* Optimization
* Differentiation in reverse mode
* Backpropagation of error
* Automatic differentiation
* Derivatives through ODEs
* Problems focused on machine learning
This is a comprehensive mathematical bridge between linear algebra, calculus, optimization, backpropagation, and machine learning.
Free ebook:
https://geni.us/Matrix-Calculus-Book
@Machine_learn
This is the 2025 MIT textbook by Alan Edelman, Steven G. Johnson, and Paige Bright.
The book directly connects matrix calculus to modern machine learning.
Inside:
* Derivatives of matrices and vectors
* Jacobian and Hessian
* Matrix decompositions
* Optimization
* Differentiation in reverse mode
* Backpropagation of error
* Automatic differentiation
* Derivatives through ODEs
* Problems focused on machine learning
This is a comprehensive mathematical bridge between linear algebra, calculus, optimization, backpropagation, and machine learning.
Free ebook:
https://geni.us/Matrix-Calculus-Book
@Machine_learn
❤6👍1
🔖Computer Science Fundamentals from MIT
We found the textbook Mathematics for Computer Science – covering the mathematics that underlies algorithms and computer science.
Logic, graphs, combinatorics, probability, induction, recurrence relations, and discrete structures – all in one place.
⛓️ Link to the textbook
https://ocw.mit.edu/courses/6-042j-mathematics-for-computer-science-spring-2015/mit6_042js15_textbook.pdf
@Machine_learn
We found the textbook Mathematics for Computer Science – covering the mathematics that underlies algorithms and computer science.
Logic, graphs, combinatorics, probability, induction, recurrence relations, and discrete structures – all in one place.
⛓️ Link to the textbook
https://ocw.mit.edu/courses/6-042j-mathematics-for-computer-science-spring-2015/mit6_042js15_textbook.pdf
@Machine_learn
❤5
با عرض سلام برای یکی از مقالاتمون تحت عنون زیر نیازمند نفر دوم و سوم هستیم.
Price: 2 --> 200$
Price 3--> 150$
Title:Skin cancer diagnosis (scd) using efficientnet-wavelet and Optimization algortithms
@Raminmousa1
Price: 2 --> 200$
Price 3--> 150$
Title:Skin cancer diagnosis (scd) using efficientnet-wavelet and Optimization algortithms
@Raminmousa1
❤1
Machine learning books and papers pinned «با عرض سلام برای یکی از مقالاتمون تحت عنون زیر نیازمند نفر دوم و سوم هستیم. Price: 2 --> 200$ Price 3--> 150$ Title:Skin cancer diagnosis (scd) using efficientnet-wavelet and Optimization algortithms @Raminmousa1»
This repository contains Jupyter notebooks for the O'Reilly book "Transformers: The Definitive Guide."
It includes code for computer vision tasks, time series analysis, audio processing, and reinforcement learning.
https://github.com/Nicolepcx/transformers-the-definitive-guide
@Machine_learn
It includes code for computer vision tasks, time series analysis, audio processing, and reinforcement learning.
https://github.com/Nicolepcx/transformers-the-definitive-guide
@Machine_learn
❤7
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ReCamMaster: Camera-Controlled Generative Rendering from A Single Video
Source code: https://github.com/KlingAIResearch/ReCamMaster
@Machine_learn
Source code: https://github.com/KlingAIResearch/ReCamMaster
@Machine_learn
❤1
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❇️ MindDrive: A Vision-Language-Action Model for Autonomous Driving via Online Reinforcement Learning 🔥
Source code: https://github.com/xiaomi-mlab/Minddrive
@Machine_learn
Source code: https://github.com/xiaomi-mlab/Minddrive
@Machine_learn
❤3
Machine learning books and papers
#for_sell @Raminmousa1
High-Impact Research Paper – MedicalRec / GROKRec
Title: MedicalRec
Price: $1,000 USD
Looking for a ready-to-publish, novel research paper in medical AI and sustainable deep learning?
This paper introduces GROKRec, an innovative recommender framework that uses embedding vectors from the Grok language model combined with numerical features to recommend the best deep learning model for any medical image classification task — without the need to train dozens of models on the target dataset.
Key Highlights:
Addresses major real-world problems: high computational cost, energy consumption, carbon emissions, and e-waste caused by training large DL models.
Built on a newly curated public dataset MedicalRec-Bench II containing 3,500 research papers and over 6,000 model evaluation records across diverse medical imaging tasks.
Evaluated under four feature configurations (MedicalRec I) using 13 different models.
Achieves strong performance with HitRate@100 ranging from 72.43% to 77.08% — the highest among compared approaches.
Uses composite loss functions and regularization techniques for accurate recommendations.
Fully eliminates the trial-and-error process of training multiple models, significantly reducing carbon footprint.
This is a complete, self-contained research contribution with a novel dataset and a practical, environmentally conscious solution for the medical AI community.
Ideal for: Researchers, academic publishers, journals, or institutions looking for high-quality, ready-to-use work in medical image analysis, recommender systems, and green AI.
Price: $1,000 USD (one-time transfer of ownership/rights as agreed).
Interested? Contact me for the full manuscript, dataset details, or to discuss terms.
@Raminmousa1
Title: MedicalRec
Price: $1,000 USD
Looking for a ready-to-publish, novel research paper in medical AI and sustainable deep learning?
This paper introduces GROKRec, an innovative recommender framework that uses embedding vectors from the Grok language model combined with numerical features to recommend the best deep learning model for any medical image classification task — without the need to train dozens of models on the target dataset.
Key Highlights:
Addresses major real-world problems: high computational cost, energy consumption, carbon emissions, and e-waste caused by training large DL models.
Built on a newly curated public dataset MedicalRec-Bench II containing 3,500 research papers and over 6,000 model evaluation records across diverse medical imaging tasks.
Evaluated under four feature configurations (MedicalRec I) using 13 different models.
Achieves strong performance with HitRate@100 ranging from 72.43% to 77.08% — the highest among compared approaches.
Uses composite loss functions and regularization techniques for accurate recommendations.
Fully eliminates the trial-and-error process of training multiple models, significantly reducing carbon footprint.
This is a complete, self-contained research contribution with a novel dataset and a practical, environmentally conscious solution for the medical AI community.
Ideal for: Researchers, academic publishers, journals, or institutions looking for high-quality, ready-to-use work in medical image analysis, recommender systems, and green AI.
Price: $1,000 USD (one-time transfer of ownership/rights as agreed).
Interested? Contact me for the full manuscript, dataset details, or to discuss terms.
@Raminmousa1
❤2
Machine learning books and papers pinned «High-Impact Research Paper – MedicalRec / GROKRec Title: MedicalRec Price: $1,000 USD Looking for a ready-to-publish, novel research paper in medical AI and sustainable deep learning? This paper introduces GROKRec, an innovative recommender framework that…»
Uniface
Automate face detection, recognition, and analysis of key facial landmarks with the Uniface Python library.
https://github.com/yakhyo/uniface
@Machine_learn
Automate face detection, recognition, and analysis of key facial landmarks with the Uniface Python library.
https://github.com/yakhyo/uniface
@Machine_learn
🔥2
Understanding Attention
From Q, K, V to Modern Transformer Attention
https://drive.google.com/file/d/1fCHQ5xCQJ6jZszAYf-qP3VIySbzFIEDv/view
@Machine_learn
From Q, K, V to Modern Transformer Attention
https://drive.google.com/file/d/1fCHQ5xCQJ6jZszAYf-qP3VIySbzFIEDv/view
@Machine_learn
❤3
با عرض سلام این مقاله به صورت کامل واگذار میشه به همراه پیاده سازی, مجموعه داده و قالب latex. هزینه کار ۱۰۰۰ دلار
@Raminmousa1
📖 "A Little Book on the Fundamentals of Generative AI" - an intuitive introduction to the mathematics:
arxiv.org/pdf/2605.29713
#GenerativeAI #Mathematics #DeepLearning #AIResearch #MachineLearning #arXiv
✨ Join Best TG Channels https://t.iss.one/addlist/0f6vfFbEMdAwODBk
@Machine_learn
arxiv.org/pdf/2605.29713
#GenerativeAI #Mathematics #DeepLearning #AIResearch #MachineLearning #arXiv
✨ Join Best TG Channels https://t.iss.one/addlist/0f6vfFbEMdAwODBk
@Machine_learn
❤2
Forwarded from ابر ویراک
☁️ ابرک ویراک را 24 ساعت رایگان تست کنید!
قبل از خرید، سرویس ابری VirakCloud را با شرایط واقعی امتحان کنید:
🎁 24 ساعت تست رایگان
🚀 پهنای باند اختصاصی با ترافیک نامحدود
⏱️ پرداخت ساعتی؛ فقط به اندازه مصرفتان هزینه کنید
بدون نیاز به تعهد بلندمدت، ابرک خودتان را بسازید، عملکرد سرویس را بررسی کنید و بعد تصمیم بگیرید.
برای دریافت کد تست رایگان، کلمه «تست» را به آیدی زیر ارسال کنید:
https://t.iss.one/cloud_virak
👇 سپس وارد پنل VirakCloud شوید، کد را وارد کنید و ابرک خود را بسازید:
🔗 https://B2n.ir/qy4432
☎️ 02191555530
🌐 virakcloud.com
قبل از خرید، سرویس ابری VirakCloud را با شرایط واقعی امتحان کنید:
🎁 24 ساعت تست رایگان
🚀 پهنای باند اختصاصی با ترافیک نامحدود
⏱️ پرداخت ساعتی؛ فقط به اندازه مصرفتان هزینه کنید
بدون نیاز به تعهد بلندمدت، ابرک خودتان را بسازید، عملکرد سرویس را بررسی کنید و بعد تصمیم بگیرید.
برای دریافت کد تست رایگان، کلمه «تست» را به آیدی زیر ارسال کنید:
https://t.iss.one/cloud_virak
👇 سپس وارد پنل VirakCloud شوید، کد را وارد کنید و ابرک خود را بسازید:
🔗 https://B2n.ir/qy4432
☎️ 02191555530
🌐 virakcloud.com
❤1