CapCut | All-in-one video editor & graphic design tool driven by AI
free video editor
AI/ML Tech Tools
Freenom - A Name for Everyone and FREE DNS
free ppublic dns resolver
Welcome – My First Project – Google Cloud console
Hacking and developing with the google console
Free your wrist - AsteroidOS
building an os for a wearable device
10 ChatGPT Vision Use Cases You Must See!
From Calorie Tracking to Debugging Code: How ChatGPT Vision Is Changing the Data Science and AI Landscape.
recsim/recsim/colab/RecSim_Developing_an_Agent.ipynb at master · google-research/recsim · GitHub
recommender system could be one way we can automatically push AI insights to users in the app
Gentle Introduction to Reinforcement Learning (self-driving cars example)
Weights & Biases, developer tools for machine learning
Weights & Biases tracking artifacts by reference
Weights & Biases, developer tools for machine learning
How to evaluate a completion(QA) model? - Prompting - OpenAI Developer Forum
Hello, I have fine tuned a “davinci” base model using hundreds of QA pairs, to build a a customized chatbot. After the model is built, the model can return a response by calling openai.Completion.create( model="davinci-fine-tuned", prompt=my_prompt) Now, I want to evaluate the model performance, should I compare the model performance and true value (from QA pairs) by calculating text similarity? If so, how can I calculate the accuracy/F1 of the fine tuned model? Thank you.
Weights and biases ai awesome tools
Launch a job into any target environment with W&B Launch, and dramatically scale machine learning training workloads from local machine to distributed compute.
An Experiment in Crowdscraping
OSINT scraping pages using Node.js
scrape
request-promise cheerio json2csv
merge csv2json - reads input csv s json2csv - exports master.csv
location table from master csv, create a list of wiki location page urls mine each page for coordinates and join to master.csv