ttungl's Stars
AUTOMATIC1111/stable-diffusion-webui
Stable Diffusion web UI
rust-unofficial/awesome-rust
A curated list of Rust code and resources.
oobabooga/text-generation-webui
A Gradio web UI for Large Language Models.
karpathy/LLM101n
LLM101n: Let's build a Storyteller
karpathy/minGPT
A minimal PyTorch re-implementation of the OpenAI GPT (Generative Pretrained Transformer) training
microsoft/unilm
Large-scale Self-supervised Pre-training Across Tasks, Languages, and Modalities
stanfordnlp/dspy
DSPy: The framework for programming—not prompting—foundation models
karpathy/minbpe
Minimal, clean code for the Byte Pair Encoding (BPE) algorithm commonly used in LLM tokenization.
alexeygrigorev/data-science-interviews
Data science interview questions and answers
interstellard/chatgpt-advanced
WebChatGPT: A browser extension that augments your ChatGPT prompts with web results.
andrewekhalel/MLQuestions
Machine Learning and Computer Vision Engineer - Technical Interview Questions
rmcelreath/stat_rethinking_2023
Statistical Rethinking Course for Jan-Mar 2023
pymc-devs/pymc-resources
PyMC educational resources
JShollaj/awesome-llm-interpretability
A curated list of Large Language Model (LLM) Interpretability resources.
WillianFuks/tfcausalimpact
Python Causal Impact Implementation Based on Google's R Package. Built using TensorFlow Probability.
amit-sharma/causal-inference-tutorial
Repository with code and slides for a tutorial on causal inference.
aswintechguy/Deep-Learning-Projects
This repository contains all the deep learning projects done as tutorial
kaz-yos/tableone
R package to create "Table 1", description of baseline characteristics with or without propensity score weighting
Lisprez/so_stupid_search
It's my honor to drive you fucking fire faster, to have more time with your Family and Sunshine.This tool is for those who often want to search for a string Deeply into a directory in Recursive mode, but not with the great tools: grep, ack, ripgrep .........every thing should be Small, Thin, Fast, Lazy....without Think and Remember too much ...一个工具最大的价值不是它有多少功能,而是它能够让你以多快的速度达成所愿......
IgnoranceAI/hugh
A voice-powered AI built with Whisper, ChatGPT, and ElevenLabs
bootandy/window_funcs
A Rust web app to teach SQL window functions
amazonqa/amazonqa
Evidence-based QA system for community question answering.
rchavezj/Cracking_The_Machine_Learning_Interview
(Under Construction) I am currently writing a solution from the Medium article "Cracking the Machine Learning Interview," written by Subhrajit Roy. In the past year since the article went public, Subhrajit has only written down the questions with no update on the solutions. I plan on finishing the war. I may add more questions outside of the articles domain. No one else on the internet has written down a solution for machine learning interview, an opportunity I want to take advantage of.
vkoul/awesome-Marketing-Analytics
:rotating_light: Resources :briefcase: to learn/practice :dart: Marketing analytics :chart: :rotating_light:
crackingthemachinelearninginterview/Cracking-The-Machine-Learning-Interview
Code snippets for our Book solutions
arpan65/Scanned-document-classification-deep-learning
BFSI sectors deal with lots of unstructured scanned documents which are archived in document management systems for further use.For example in Insurance sector, when a policy goes for underwriting, underwriters attached several raw notes with the policy, Insureds also attach various kind of scanned documents like identity card, bank statement, letters etc. In later parts of the policy life cycle if claims are made on a policy, releted scanned documents also archeived.Now it becomes a tedious job to identify a particular document from this vast repository. The goal of this case study is to develop a deep learning based solution which can automatically classify scanned documents.
bdanalytics/Berkeley-Spark
edX:Berkeley:Spark
sambalshikhar/Document-Image-Classification-with-Intra-Domain-Transfer-Learning-and-Stacked-Generalization-of-Deep
RVL-CDIP could be looked at as the equivalent of ImageNet for the document image community. It’s certainly the largest we’ve seen in the literature. There are 400,000 total document images in the dataset. The dataset contains much noise and variance in composition of each document class. Uncompressed, the dataset size is ~100GB, and comprises 16 classes of document types, with 25,000 samples per classes. Example classes include email, resume, and invoice. Achieved an Accuracy of over 93% which beat the benchmark score of 92% based on https://paperswithcode.com/sota/document-image-classification-on-rvl-cdip
ttungl/Deep-Learning
Implemented the deep learning techniques using Google Tensorflow that cover deep neural networks with a fully connected network using SGD and ReLUs; Regularization with a multi-layer neural network using ReLUs, L2-regularization, and dropout, to prevent overfitting; Convolutional Neural Networks (CNNs) with learning rate decay and dropout; and Recurrent Neural Networks (RNNs) for text and sequences with Long Short-Term Memory (LSTM) networks.
ttungl/HeteroArchGen4M2S
HeteroArchGen4M2S: An automatic software for configuring and running heterogeneous CPU-GPU architectures on Multi2Sim simulator. This tool is built on top of M2S simulator, it allows us to configure various heterogeneous CPU-GPU architectures (e.g., number of CPU cores, GPU cores, L1$, L2$, memory (size and latency (via CACTI 6.5)), network topologies (currently support 2D-Mesh, customized 2D-Mesh, and Torus networks)...). The output files include the results of network throughput and latency, caches/memory access time, and dynamic power of the cores (can be collected after running McPAT).