Hamid Jamshidnia
@tech_hamid
Scientist and researcher passionate for science and technology at the intersection of Fluid Dynamics, Ultrasound & Optical-based Measurement Techniques & AI
I'm deleting this soon because it's a legit cash-printing formula. 𝗣𝗮𝗶𝗱 𝗖𝗼𝘂𝗿𝘀𝗲 𝗙𝗥𝗘𝗘 (PART - 2) 1. Artificial Intelligence + Data Analyst 2. Machine Learning + Data Science 3. Cloud Computing + Web Development 4. Ethical Hacking + Hacking 5. Data Analytics + DSA 6.…
Love this image!
Hypernetworks are neural networks that generate the weights of another network, allowing models to adapt quickly across tasks. Instead of learning parameters directly, a hypernetwork learns a mapping from task inputs or conditions to weight space. In deep learning, they’re used…
🚀 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗡𝗼𝘁𝗲𝘀 (𝗛𝗮𝗻𝗱𝘄𝗿𝗶𝘁𝘁𝗲𝗻 𝗣𝗗𝗙) Master the core of 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 —algorithms, models, training, evaluation & real-world examples. Perfect for interviews & AI enthusiasts! 🤖📊 📘FREE for the first 500 people! 1.…
Graphons are mathematical objects that model the structure of massive networks. In machine learning, they provide a powerful framework for analyzing and generating large graphs. They are used to estimate the underlying structure of a network, predict missing links, and understand…
The math of LLMs is a fusion of three key areas: 1) Probability: They are massive statistical models that predict the next word based on the probability of what's come before. 2) Linear Algebra: All words and concepts are encoded as high-dimensional vectors (embeddings). The…
Fourier series and Laplace Transform : ✍️
Monte Carlo Simulation (MCS) is one of the most important statistical developments of the 20th century. 1. MCS uses random sampling to solve problems that would otherwise be intractable, allowing us to model complex systems with uncertainty. 2. MCS is also powerful for…
"Spectral Methods in MATLAB" by Lloyd N. Trefethen.
"Numerical Recipes in Fortran 90"
Statistical physics provides a powerful lens for machine learning. It explains how complex systems, like neural networks, behave collectively. Concepts like entropy, energy landscapes, and phase transitions are used to analyze learning dynamics, understand theory of deep…
One of the coolest books in probability I’ve seen in a while:
Stanford's "Machine Learning Theory" by Tengyu Ma Lecture Notes: tselilschramm.org/mltheory/ma.pdf Lecture Videos: youtube.com/playlist?list=…
A professor at the CMI decided to teach this course, only to realise that he could not handle the depth. Later he received SSB award. He switched to a book by Steve Smale. In 2019, I covered the first 60 pp of this for Dr. Manikandan (PhD HRI) & now a faculty at UofAllahabad.
"Ordinary Differential Equations" by V. I. Arnold.
Aircraft Design with Physics AI! 🚀 Rune Aero is transforming aerospace engineering with an AI-powered virtual wind tunnel, leveraging Luminary Cloud and NVIDIA technology for real-time aerodynamic feedback. #PhysicsAI #Aerospace #DigitalTwin #Engineering
1. Schrödinger Equation 2. Dirac Equation 3. Einstein Field Equations 4. Navier--Stokes Equations 5. Planck's Law of Blackbody Radiation
Differentiable Simulators and Physics-Informed Neural Networks (PINNs) integrate physical laws—expressed as differential equations—directly into deep learning models. Unlike traditional simulations, they learn from data while respecting underlying physics, enabling accurate…
An Introduction to Dynamical Systems and Chaos:
The Neural Tangent Kernel (NTK) provides a way to understand the behavior of wide neural networks during training. It represents how the network's output changes with respect to its parameters, essentially linearizing the network's dynamics. NTK has applications in machine…
The integral form of Maxwell's equations, etched in concrete on the Warsaw University Library, is a must-see for science lovers! [bityl.co/RZV6]
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