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Jackknife Sampling Method#

A large number of samples are obtained by removing one data point at a time from the original dataset, and the model is trained and tested on these samples. This method is used to estimate the sensitivity of a model's performance to individual observations in the dataset.

See also J, Resampling Method

Jailbreaking#

Similar to Red Teaming, but also encompass every method to bypass controls for the model's generated output.

{% youtube "https://youtu.be/5cEvNO9rZgI?si=GgcYCFBKSmHqSn1n" %}

See J, Red Teaming

JAX Python Module#

A python module that

Jensen Huang Person#

Founder and CEO of NVidia

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  • Wikipedia -

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Joblib Python Module#

A python module to save models in files

Create a model and save it in a file

import joblib
import ...

df = pd.read_csv('/home/Data/transformed_airbnb_NYC_2019.csv')

y = df['price']
X = df.drop(columns = ['price', 'host_id', 'neighbourhood'])
X_train, X_test, y_train, y_test = train_test_split(X,y, test_size=0.2, random_state=123)

lr_classifier = LinearRegression()
lr_classifier.fit(X_train, y_train)

joblib.dump(lr_classifier, 'airbnb_lr_classifier.joblib')

Load model

from flask import Flask

app = Flask(__name__)

@app.route('/predict', methods=['POST'])
def predict():
     json_ = request.json
     query_df = pd.DataFrame(json_)
     query = pd.get_dummies(query_df)

     classifier = joblib.load('airbnb_lr_classifier.joblib')
     prediction = classifier.predict(query)
     return jsonify({'prediction': list(prediction)})


if __name__ == '__main__':
     #app.run(port=8080)
     from gevent.pywsgi import WSGIServer
     web_server = WSGIServer(('', 5000), app)
     web_server.serve_forever()
     print('Success! Server available at http://127.0.0.1:5000')

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See also J, ...

Join Distribution#

Joint distribution is based on joint probability, which can be simply defined as the probability of two events (variables) happening together. These two events are usually coined event A and event B, and can formally be written as:

p(A and B)

Joint distribution, or joint probability distribution, shows the probability distribution for two or more random variables. Hence:

f(x,y) = P(X = x, Y = y)

Joint-Embedding Predictive Architecture (JEPA)#

Our goal is to build advanced machine intelligence that can learn more like humans do, forming internal models of the world around them to learn, adapt, and forge plans efficiently in the service of completing complex tasks.

  • [Image JEPA (I-JEPA)]
  • [Video JEPA (V-JEPA)]

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Jukebox Model#

Generative model for music built by OpenAI

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Jupyter Notebook#

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Jupyter Server#

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JupyterLab#

See also J, Jupyter Notebook