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

Try it, it's free, your AI assistant

📚 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

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Professor Steve Branton from the Mechanical Engineering Department at the University of Washington has uploaded a complete course on control theory for master's and doctoral students to YouTube. It's free.

The course is called Control Bootcamp.

It covers topics such as linear systems, stability and eigenvalues, controllability and observability, pole placement, the Kalman filter, LQR/LQG, robust control, and MPC – all explained sequentially with examples in Matlab.

Branton is the Boeing Professor of AI & Data-Driven Engineering at the University of Washington. He holds a bachelor's degree in mathematics from Caltech, with a specialization in control and dynamical systems, and a Ph.D. in mechanical and aerospace engineering from Princeton.

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Natural Language Processing has undergone massive advancements, and this advanced PyTorch course takes learners through the evolution of text modeling. Moving from standard tokenization and RNNs to modern Transformer architectures and attention mechanisms, it delivers a comprehensive blueprint for deep NLP application design.
Who It's For
Advanced machine learning engineers and NLP specialists who want to master PyTorch text preprocessing, recurrent networks, Transformers, and transfer learning.
Key Takeaways
• Text Preprocessing & Encodings: Master tokenization, stemming, lemmatization, One-Hot, Bag-of-Words, and TF-IDF encodings for neural networks.
…

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