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β‘οΈ Top up US$20 in a single payment to unlock the 1.5% GPT official rate:
- gpt-5.6-terra: US$0.03 inputο½US$0.18 output
- gpt-5.6-sol / gpt-5.5: US$0.075 inputο½US$0.45 output
All prices are per 1 million tokens.
π One API key for GPT, Claude, Grok, Gemini, DeepSeek, and more.
π Sign up and get US$0.25 in free trial credit.
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Forwarded from Data Analytics
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
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
β€6
Matrix Calculus for Machine Learning and Beyond! β 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
β€8π₯1
"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
β€9π₯1
Roadmap for those who want to become a robotics engineer:
* Programming β Python, C++
* Mathematics β Linear algebra, calculus, probability theory
* Electronics β Sensors, motors, power systems
* Embedded systems β Microcontrollers, real-time operating systems, hardware interaction
* Control theory β PID controllers, modeling, stability
* Mechanics β Kinematics, dynamics, CAD systems
* Linux β Terminal, networking, debugging
* Robotics software β ROS 2
* Simulation β Gazebo, Isaac Sim
* Environmental perception β Computer vision, LiDAR, sensor data fusion
* Localization β Kalman filters, SLAM
Motion planning β A, RRT, trajectory generation
* Manipulator control β Inverse kinematics, object grasping
* AI for robotics β Reinforcement learning
* Building your own robots β Drones, rovers, robotic arms
* Autonomy β Perception β Planning β Control
* Deployment on real devices β Edge computing, AI directly on board
* Industrial robotics β PLCs, production automation
* Programming β Python, C++
* Mathematics β Linear algebra, calculus, probability theory
* Electronics β Sensors, motors, power systems
* Embedded systems β Microcontrollers, real-time operating systems, hardware interaction
* Control theory β PID controllers, modeling, stability
* Mechanics β Kinematics, dynamics, CAD systems
* Linux β Terminal, networking, debugging
* Robotics software β ROS 2
* Simulation β Gazebo, Isaac Sim
* Environmental perception β Computer vision, LiDAR, sensor data fusion
* Localization β Kalman filters, SLAM
Motion planning β A, RRT, trajectory generation
* Manipulator control β Inverse kinematics, object grasping
* AI for robotics β Reinforcement learning
* Building your own robots β Drones, rovers, robotic arms
* Autonomy β Perception β Planning β Control
* Deployment on real devices β Edge computing, AI directly on board
* Industrial robotics β PLCs, production automation
β€9π2
Forwarded from Learn Python Coding
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β€3π2
πComputer Science Fundamentals from MIT
We found the textbook Mathematics for Computer Science β covering the mathematics that underlies algorithms and computer science.
Logic, graphs, combinatorics, probability, induction, recurrence relations, and discrete structures β all in one place.
βοΈ Link to the textbook
https://ocw.mit.edu/courses/6-042j-mathematics-for-computer-science-spring-2015/mit6_042js15_textbook.pdf
https://t.me/CodeProgrammerβ€οΈ π°
We found the textbook Mathematics for Computer Science β covering the mathematics that underlies algorithms and computer science.
Logic, graphs, combinatorics, probability, induction, recurrence relations, and discrete structures β all in one place.
βοΈ 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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β€7
Forwarded from Udemy Free Coupons
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Learn Python from basics to advanced concepts, including variables, data types, loops, functions, and object-oriented programming.Open Online Courses
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Learn Python from basics to advanced concepts, including variables, data types, loops, functions, and object-oriented programming.Open Online Courses
π‘ Why this is useful: Master Python programming with confidence and practical skills.
π€ Best for: Beginners and intermediate learners of Python programming.
β After this course: Understand Python fundamentals, object-oriented programming, and advanced concepts to build programs and solve real-world problems.
π· Category: Development
π Language: English
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