Best Online AI Tools

DALL-E (Text to image converter)https://labs.openai.com
Text completionhttps://beta.openai.com/playground
Image enlargerhttps://bigjpg.com
Auto Regexhttps://www.autoregex.xyz
Chat with AIhttps://beta.character.ai
AI-powered Grammar Checkerhttps://app.gramara.com

➡️ OpenAI Tips

OpenAI Tips

GPT-3 Engines

  • davinci (most expensive)
  • curie
  • babbage
  • ada (cheapest and fastest)

Pricing: https://openai.com/api/pricing/

Ada is extremely fast and capable when it comes to tasks where creativity is more important than precision.

Temperature

Temperature = 0 (precise answer)

Temperature = 1 (random, creativity)

max_tokens

1 token ~= 4 chars in English

100 tokens ~= 75 words

Keras Project Example

Download pima indian diabetes data file first.

Python code:

from keras.models import Sequential
from keras.layers import Dense
import numpy
# fix random seed for reproducibility
numpy.random.seed(7)
# load pima indians dataset
dataset = numpy.loadtxt("pima-indians-diabetes.csv", delimiter=",")
# split into input (X) and output (Y) variables
X = dataset[:,0:8] #input data - except last column
Y = dataset[:,8] # last column is real results
# create model
model = Sequential()
model.add(Dense(12, input_dim=8, activation='relu'))
model.add(Dense(8, activation='relu'))
model.add(Dense(1, activation='sigmoid'))
# Compile model
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
# Fit the model - train
model.fit(X, Y, epochs=150, batch_size=10, verbose=2)

# evaluate the model
#scores = model.evaluate(X, Y)
#print("\n%s: %.2f%%" % (model.metrics_names[1], scores[1]*100))

# calculate predictions
predictions = model.predict(X)
rounded = [round(x[0],2) for x in predictions]
print(rounded)

Tested on: python-3.6.3-amd64, Keras API 2