Personal AI assistant in 5 minutes
No code. No card. Free 😳
Works in Telegram, WhatsApp, or Discord — just send it tasks by voice or text. It gets things done, not just tells you how to do them.
✅ Create your personal AI assistant here →
getamplify.team
No code. No card. Free 😳
Works in Telegram, WhatsApp, or Discord — just send it tasks by voice or text. It gets things done, not just tells you how to do them.
• reads and sends emails
• creates and edits Google Sheets
• uploads files to Google Drive
• works in Notion
• sends reminders
• generates PDFs, images, and videos
• actually makes life and work easier
✅ Create your personal AI assistant here →
getamplify.team
❤4
Personal AI assistant in 5 minutes
No code. No card. Free 😳
Works in Telegram, WhatsApp, or Discord — just send it tasks by voice or text. It gets things done, not just tells you how to do them.
✅ Create your personal AI assistant here →
getamplify.team
No code. No card. Free 😳
Works in Telegram, WhatsApp, or Discord — just send it tasks by voice or text. It gets things done, not just tells you how to do them.
• reads and sends emails
• creates and edits Google Sheets
• uploads files to Google Drive
• works in Notion
• sends reminders
• generates PDFs, images, and videos
• actually makes life and work easier
✅ Create your personal AI assistant here →
getamplify.team
❤3🔥1
📚 "Mathematical Methods in Data Science with Python" by Sebastian Roche.
🔗 https://mmids-textbook.github.io
#Python #DataScience #MachineLearning #Mathematics #Programming #Learning
✨ Join Best TG Channels https://t.me/addlist/0f6vfFbEMdAwODBk
⭐️ Join Our WhatsApp Channel https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
🔗 https://mmids-textbook.github.io
#Python #DataScience #MachineLearning #Mathematics #Programming #Learning
✨ Join Best TG Channels https://t.me/addlist/0f6vfFbEMdAwODBk
⭐️ Join Our WhatsApp Channel https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
❤4
The best programming and artificial intelligence channels on Telegram:
https://t.me/addlist/8_rRW2scgfRhOTc0
Bot to get the latest free courses:
https://t.me/UdemySybot?start=ref_channel
For assistance and research and programming projects:
t.me/Husseinsheikho
https://t.me/addlist/8_rRW2scgfRhOTc0
Bot to get the latest free courses:
https://t.me/UdemySybot?start=ref_channel
For assistance and research and programming projects:
t.me/Husseinsheikho
Telegram
Data science
You’ve been invited to add the folder “Data science”, which includes 15 chats.
❤3
Forwarded from Machine Learning with Python
🚨 Cambridge has just released a real bombshell this time.
📚 A whole collection of classic textbooks on AI and machine learning is now available for free in PDF format.
If you want to really understand machine learning and don't want to waste money on overpriced courses, these ten books will be enough to build a very solid foundation.
From simple to complex.
1️⃣ Understanding Machine Learning
One of the best books for beginners. It covers the basic theoretical algorithms of machine learning.
🔗 https://cs.huji.ac.il/~shais/UnderstandingMachineLearning/understanding-machine-learning-theory-algorithms.pdf
2️⃣ Mathematical Foundations of Machine Learning
If you're not very confident in your math skills, I would start here.
🔗 https://mml-book.github.io/book/mml-book.pdf
3️⃣ Mathematical Analysis of Machine Learning Algorithms
A more in-depth look at the mathematical principles of machine learning algorithms.
🔗 https://tongzhang-ml.org/lt-book/lt-book.pdf
4️⃣ Theoretical Principles of Deep Learning
The theoretical foundations of deep learning and an understanding of why it all works.
🔗 https://arxiv.org/pdf/2106.10165
5️⃣ Neural Networks and Learning Machines
A systematic analysis of neural networks and the principles of their training.
🔗 https://arxiv.org/pdf/1901.05639
6️⃣ Graph Deep Learning
A good starting point for those who want to understand graph neural networks.
🔗 https://yaoma24.github.io/dlg_book/dlg_book.pdf
7️⃣ Machine Learning: A Probabilistic Perspective
It allows you to look at machine learning from a probabilistic and algorithmic perspective.
🔗 https://people.csail.mit.edu/moitra/docs/bookexv2.pdf
8️⃣ Probability Theory: Theory and Examples
Fundamental theory of probability. Very useful if you want to understand machine learning beyond the level of using ready-made libraries.
🔗 https://sites.math.duke.edu/~rtd/PTE/PTE5_011119.pdf
9️⃣ Fundamentals of Applied Probability
More focus on the practical application of probability theory.
🔗 https://sites.math.duke.edu/~rtd/EP4A/EP4A_April2021.pdf
🔟 Advanced Data Analysis
An advanced level for those who want to seriously improve their data analysis skills.
🔗 https://stat.cmu.edu/~cshalizi/ADAfaEPoV/ADAfaEPoV.pdf
#AI #MachineLearning #FreeBooks #DataScience #DeepLearning #Tech
✨ Join Best TG Channels https://t.me/addlist/0f6vfFbEMdAwODBk
⭐️ Join Our WhatsApp Channel https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
📚 A whole collection of classic textbooks on AI and machine learning is now available for free in PDF format.
If you want to really understand machine learning and don't want to waste money on overpriced courses, these ten books will be enough to build a very solid foundation.
From simple to complex.
1️⃣ Understanding Machine Learning
One of the best books for beginners. It covers the basic theoretical algorithms of machine learning.
🔗 https://cs.huji.ac.il/~shais/UnderstandingMachineLearning/understanding-machine-learning-theory-algorithms.pdf
2️⃣ Mathematical Foundations of Machine Learning
If you're not very confident in your math skills, I would start here.
🔗 https://mml-book.github.io/book/mml-book.pdf
3️⃣ Mathematical Analysis of Machine Learning Algorithms
A more in-depth look at the mathematical principles of machine learning algorithms.
🔗 https://tongzhang-ml.org/lt-book/lt-book.pdf
4️⃣ Theoretical Principles of Deep Learning
The theoretical foundations of deep learning and an understanding of why it all works.
🔗 https://arxiv.org/pdf/2106.10165
5️⃣ Neural Networks and Learning Machines
A systematic analysis of neural networks and the principles of their training.
🔗 https://arxiv.org/pdf/1901.05639
6️⃣ Graph Deep Learning
A good starting point for those who want to understand graph neural networks.
🔗 https://yaoma24.github.io/dlg_book/dlg_book.pdf
7️⃣ Machine Learning: A Probabilistic Perspective
It allows you to look at machine learning from a probabilistic and algorithmic perspective.
🔗 https://people.csail.mit.edu/moitra/docs/bookexv2.pdf
8️⃣ Probability Theory: Theory and Examples
Fundamental theory of probability. Very useful if you want to understand machine learning beyond the level of using ready-made libraries.
🔗 https://sites.math.duke.edu/~rtd/PTE/PTE5_011119.pdf
9️⃣ Fundamentals of Applied Probability
More focus on the practical application of probability theory.
🔗 https://sites.math.duke.edu/~rtd/EP4A/EP4A_April2021.pdf
🔟 Advanced Data Analysis
An advanced level for those who want to seriously improve their data analysis skills.
🔗 https://stat.cmu.edu/~cshalizi/ADAfaEPoV/ADAfaEPoV.pdf
#AI #MachineLearning #FreeBooks #DataScience #DeepLearning #Tech
✨ Join Best TG Channels https://t.me/addlist/0f6vfFbEMdAwODBk
⭐️ Join Our WhatsApp Channel https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
❤8
🔥 GPT INPUT FROM JUST US$0.03 / 1M TOKENS!
⚡️ Top up US$20 in a single payment to unlock the 1.5% GPT official rate:
- gpt-5.6-terra: US$0.03 input｜US$0.18 output
- gpt-5.6-sol / gpt-5.5: US$0.075 input｜US$0.45 output
All prices are per 1 million tokens.
🔑 One API key for GPT, Claude, Grok, Gemini, DeepSeek, and more.
🎁 Sign up and get US$0.25 in free trial credit.
Claim your credit and start testing now 👇
https://modelflare.dev/
⚡️ Top up US$20 in a single payment to unlock the 1.5% GPT official rate:
- gpt-5.6-terra: US$0.03 input｜US$0.18 output
- gpt-5.6-sol / gpt-5.5: US$0.075 input｜US$0.45 output
All prices are per 1 million tokens.
🔑 One API key for GPT, Claude, Grok, Gemini, DeepSeek, and more.
🎁 Sign up and get US$0.25 in free trial credit.
Claim your credit and start testing now 👇
https://modelflare.dev/
❤3
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
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
👍4❤2
This media is not supported in your browser
VIEW IN TELEGRAM
Benchmarks disagree, model cards hide the licence, and the thread that actually explains the tradeoff is on someone's timeline from March.
Create your own AI agent inside Telegram in about a minute and hand it the reading.
▫️ drop a paper or an arXiv link and get the method, the claim and the weak spot in a few lines
▫️ ask it to compare two models on licence, context, price and latency, with the sources under the answer
▫️ send a chart from a paper and ask what the axes are hiding
▫️ writes an eval script and runs it, so the comparison stops being anecdotal
▫️ keeps your reading list and your notes in memory for months
▫️ add it to your lab chat and it answers in the thread
Setup takes a minute: open the link, start the free trial, name your agent.
30 days free, no card needed.
Please open Telegram to view this post
VIEW IN TELEGRAM
👍3🔥2❤1
Forwarded from Machine Learning with Python
"Linear Algebra with Applications" is a free and comprehensive textbook that introduces the computational, theoretical, and applied aspects of linear algebra.
The book covers topics such as systems of linear equations, matrices, determinants, vector spaces, linear transformations, eigenvalues and eigenvectors, diagonalization, inner product spaces, orthogonality, and many more. The explanations are accompanied by over 330 worked examples, exercises, and practical applications in geometry, electrical networks, dynamic systems, probability theory, and optimization.
A particularly interesting section discusses how Google's PageRank algorithm uses the dominant eigenvector to rank web pages. The links between websites are represented as a connectivity matrix, and the components of its dominant eigenvector provide an estimate of the relative importance of each page.
This is a very clear example of how an apparently abstract idea from linear algebra can underlie a real-world technology used on a massive scale.
The 2023 edition is available under a Creative Commons license. This is another excellent resource that is worth keeping as a reference.
https://collection.bccampus.ca/textbook/qTj4b4Ey
The book covers topics such as systems of linear equations, matrices, determinants, vector spaces, linear transformations, eigenvalues and eigenvectors, diagonalization, inner product spaces, orthogonality, and many more. The explanations are accompanied by over 330 worked examples, exercises, and practical applications in geometry, electrical networks, dynamic systems, probability theory, and optimization.
A particularly interesting section discusses how Google's PageRank algorithm uses the dominant eigenvector to rank web pages. The links between websites are represented as a connectivity matrix, and the components of its dominant eigenvector provide an estimate of the relative importance of each page.
This is a very clear example of how an apparently abstract idea from linear algebra can underlie a real-world technology used on a massive scale.
The 2023 edition is available under a Creative Commons license. This is another excellent resource that is worth keeping as a reference.
https://collection.bccampus.ca/textbook/qTj4b4Ey
❤3
💸 START FREE & EARN DAILY!
Earn up to $130 every day and enjoy daily withdrawals directly from your earnings. 🚀
✅ Free to start
✅ Simple & fast
✅ Earn daily
✅ Withdraw daily
Check Payout: @Dollar_Tree_Official
Start today and turn your free time into daily earnings. 💰🔥
Earn up to $130 every day and enjoy daily withdrawals directly from your earnings. 🚀
✅ Free to start
✅ Simple & fast
✅ Earn daily
✅ Withdraw daily
Check Payout: @Dollar_Tree_Official
Start today and turn your free time into daily earnings. 💰🔥