Machine Learning with Python
🙌 If I only had one weekend to master Claude, I would start with these resources.
👩🏻💻 Stop saving dozens of different Claude guides that you'll never actually read! This list contains only the resources that are truly useful for real-world projects.
💗 Level 1 — Basic Fundamentals (17 minutes)
🟡 Claude Explained Simply (For Beginners)
🟡 Getting Started with Claude
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💗 Level 2 — Real-World Workflows (1 hour)
🟠 Working with Claude Daily
🟠 Claude for Work Teams
🟠 Brainstorming and Design with Claude
🟠 Combining Teamwork and Project Management
🟠 Creating Presentations with Claude
🟠 Claude Skills
➖ ➖ ➖
💗 Level 3 — Professional Level (3.5 hours)
🔵 How to Avoid Generic and Machine-Like Responses from Claude?
🔵 Coding with Claude
🔵 The Basics of Claude
🔵 How to Avoid Reaching Claude's Limit?
🔵 Saying Goodbye to Traditional Prompt Engineering
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💗 Level 6 — Expert Level (8 hours)
🟢 Understanding Claude's Computational Capabilities
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💡 Remember, you don't need dozens of different guides; you just need the right resources, in the right order.
🤖 Claude 101
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Tensor Algebra: A Small Concept That Has a Big Impact in AI 🧠
One thing I realized while learning deep learning is that tensors are everywhere. Whether you're working with TensorFlow, PyTorch, or building transformer models, almost everything revolves around tensor operations.
Although we often think of tensors as multi-dimensional arrays in machine learning, they're the structures that allow neural networks to efficiently represent and process complex data.
Here's a quick summary:
- Scalar (Rank 0): A single value
- Vector (Rank 1): A one-dimensional collection of values
- Matrix (Rank 2): A two-dimensional arrangement of values
- Tensor (Rank 3 or higher): A higher-dimensional representation used to model complex data
A few places where tensors show up every day:
- Images are represented as 3D tensors (Height × Width × Channels).
- Mini-batches become 4D tensors during model training.
- Transformer models process embeddings, attention scores, and hidden states as tensors throughout the network.
- Operations like matrix multiplication, broadcasting, reshaping, tensor contraction, and automatic differentiation power modern deep learning.
I created the infographic below as a simple visual reference while revisiting tensor algebra. I hope it's helpful for anyone learning deep learning or refreshing the fundamentals.
I'm curious. How did you first learn about tensors?
- Through mathematics?
- While using TensorFlow or PyTorch?
- During your first deep learning project?
- Or was there another resource that made the concept finally click?
I'd love to hear your experience and any resources you'd recommend for beginners. Looking forward to learning from your experiences and recommendations.
#DeepLearning #TensorFlow #PyTorch #AI #MachineLearning #Tensors
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23 · 2.4K · ReplyTensor Algebra: A Small Concept That Has a Big Impact in AI 🧠
One thing I realized while learning deep learning is that tensors are everywhere. Whether you're working with TensorFlow, PyTorch, or building transformer models, almost everything revolves around tensor operations.
Although we often th
text not yet in the index
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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.
• reads and sends emails
• creates and edits Google Sheets
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• actually makes life and work easier
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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.
• 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
5 · 4.4K · Machine Learning with Python
ReplyPersonal 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.
• reads and sends emails
• creates and edits Google Sheets
• uploads files to Google Drive
• works in NA unique experience, I recommend you try it.
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🔖 A comprehensive resource on LLMs in one repository
We found an open-source course covering Transformers, LoRA, RAG, prompts, model editing, and other key topics.
After each chapter, you can immediately access the original sources – the authors have compiled papers and collections from arXiv.
⛓Link to GitHub
https://github.com/ZJU-LLMs/Foundations-of-LLMs
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Whether you're a university student, graduate researcher, or working on your final-year project, ResearchHub AI can help you move forward with expert guidance and practical technical support.
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🚨 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 theo
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🧲 Your agent writes the tool. You keep the terminal closed.
You know the shape of the script before you open the editor. The hour goes to argparse, a retry wrapper, a rate limiter you have written eleven times already.
Create your own AI agent inside Telegram in about a minute, and create small tools with it right in the chat.
▫️ describe a tool in a sentence and it writes, runs and returns the working script
▫️ ships a mini-app inside Telegram — a form, a converter, a dashboard, no deploy and no hosting
▫️ drop in a traceback or a repo link and get the fix, not a lecture
▫️ swap the model per task with one command, so cheap work runs cheap
▫️ remembers your stack, your conventions and your project for months
Setup takes a minute: open the link, start the free trial, name your agent.
30 days free, no card needed.
🧲 Create your agent
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🔥 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
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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.
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https://modelflare.dev/
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From machine learning and data visualization to time series and financial data.
This repository contains 920 open-source Python projects, categorized into 34 groups.
It's a great collection if you want to quickly find reliable libraries and tools for machine learning, data analysis, and related tasks, rather than searching everything manually on GitHub.
https://github.com/lukasmasuch/best-of-ml-python
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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
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Machine Learning with Python
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"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
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Roadmap for those who want to become a robotics engineer:
* Programming → Python, C++
* Mathematics → Linear algebra, calculus, probability theory
* Electronics → Sensors, motors, power systems
* Embedded systems → Microcontrollers, real-time operating systems, hardware interaction
* Control theory → PID controllers, modeling, stability
* Mechanics → Kinematics, dynamics, CAD systems
* Linux → Terminal, networking, debugging
* Robotics software → ROS 2
* Simulation → Gazebo, Isaac Sim
* Environmental perception → Computer vision, LiDAR, sensor data fusion
* Localization → Kalman filters, SLAM
Motion planning → A, RRT, trajectory generation
* Manipulator control → Inverse kinematics, object grasping
* AI for robotics → Reinforcement learning
* Building your own robots → Drones, rovers, robotic arms
* Autonomy → Perception → Planning → Control
* Deployment on real devices → Edge computing, AI directly on board
* Industrial robotics → PLCs, production automation
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🔖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
https://t.me/CodeProgrammer ❤️🔰
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Reply🧲 Your agent writes the tool. You keep the terminal closed.
You know the shape of the script before you open the editor. The hour goes to argparse, a retry wrapper, a rate limiter you have written eleven times already.
Create your own AI agent inside Telegram in about a minute, and create small toTry it, it's free, your AI assistant
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📚 This is probably one of the best technical books on how large language models are trained at scale:
> GPU memory and profiling
> Breaking down computations into blocks, kernel fusion, and FlashAttention
> Data parallelism, tensor parallelism, pipeline parallelism, and context parallelism
I've already read the free online version, but I still had to buy a physical copy for my library. 📖
You can also read it for free on Hugging Face:
https://huggingface.co/spaces/nanotron/ultrascale-playbook
#LLM #AI #MachineLearning #TechBooks #DataScience #Coding
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