Reproduce GPT2 (124M) by Andrej Karpathy 1 Micrograd

Follow the github page by Dr. Karparthy: https://github.com/karpathy/build-nanogpt and youtube: https://www.youtube.com/watch?v=l8pRSuU81PU&list=PLAqhIrjkxbuWI23v9cThsA9GvCAUhRvKZ&t=1175s, the whole series is worth deep diving hence here it is, right from the very beginning. First, basics by Andrej, he illustrated how manually applied micro level backward propagation in the first great video: macrograd! with these two simple py file, engine.py and nn.py, … Continue reading Reproduce GPT2 (124M) by Andrej Karpathy 1 Micrograd

Decompose Scipy Linprog.py

""" A top-level linear programming interface. .. versionadded:: 0.15.0 Functions --------- .. autosummary:: :toctree: generated/ linprog linprog_verbose_callback linprog_terse_callback """ import numpy as np from ._optimize import OptimizeResult, OptimizeWarning from warnings import warn from ._linprog_highs import _linprog_highs from ._linprog_ip import _linprog_ip from ._linprog_simplex import _linprog_simplex from ._linprog_rs import _linprog_rs from ._linprog_doc import (_linprog_highs_doc, _linprog_ip_doc, # noqa: … Continue reading Decompose Scipy Linprog.py

Genesis and Isaac Platforms

According to Perplexity, Genesis and Isaac are both platforms designed for robotics and AI development, but they have distinct features and focus areas: Genesis Platform Simulation Speed: Genesis can run simulations up to 43 million frames per second, which is 430,000 times faster than real-time7. Physics Engine: Integrates various state-of-the-art physics solvers into a unified … Continue reading Genesis and Isaac Platforms

Practical Problems in Quant Workflow in AI Era: QuantLib

With the rise of AI, traditional Quant Workflow is simply primitive. The traditional research workflow is increasingly inadequate in today’s data-rich environment. It often begins with a dataset containing hundreds of dimensions, where manually designing features is not only time-consuming but also inefficient. In contrast, AI-driven approaches leverage machine learning algorithms to automatically generate these … Continue reading Practical Problems in Quant Workflow in AI Era: QuantLib

Practical Problems in Quant Workflow in AI Era: Qlib

With the rise of AI, traditional Quant Workflow is simply primitive. The traditional research workflow is increasingly inadequate in today’s data-rich environment. It often begins with a dataset containing hundreds of dimensions, where manually designing features is not only time-consuming but also inefficient. In contrast, AI-driven approaches leverage machine learning algorithms to automatically generate these … Continue reading Practical Problems in Quant Workflow in AI Era: Qlib

Investing in Quantum Computing Stocks

Quantum computing is on the brink of transforming global industries, much like the internet did in the late 20th century. Major corporations and governments are pouring billions into its development, with quantum technology expected to drive innovations in healthcare, finance, logistics, and cybersecurity. For example, quantum computers can dramatically speed up drug discovery by simulating … Continue reading Investing in Quantum Computing Stocks

How Does Carbon Connect Any Data Source to LLM?

What Carbon has been offering is essential: It Enhances LLM Applications Automation and Workflows: Facilitates task automation, such as summarizing reports or analyzing trends across datasets. Enterprise AI Search: Enables organizations to query their internal documents, emails, and reports through LLMs. Knowledge Management: Helps LLMs access company-specific data to answer questions accurately. How does Carbon … Continue reading How Does Carbon Connect Any Data Source to LLM?