Orbifold

Orbifold

Orbifold

#========================================
# create public and private keys
#========================================

from Crypto.PublicKey import RSA

key = RSA.generate(2048)
private_key = key.exportKey()
with open("./private.pem", "wb") as f:

Orbifold

Orbifold

Orbifold

(* Content-type: application/vnd.wolfram.mathematica *)

(*** Wolfram Notebook File ***)
(* http://www.wolfram.com/nb *)

(* CreatedBy='Mathematica 12.3' *)

(*CacheID: 234*)
(* Internal cache information:
NotebookFileLineBreakTest

Orbifold

# if needed, install plotly via shell like so
%sh pip install plotly
#---
%pyspark

import plotly
import numpy as np
from plotly.graph_objs import Scatter, Layout

Orbifold

Using Keras (now part of TensorFlow) is really easy. The complexity comes when you deal with large amounts of data figuring out the topology of a neural network. With the topology comes hyperparameter tuning and all that. It's a bit like painting: it's easy to hold a brush but it takes years to paint something worth looking at.

	import tensorflow as tf
	from tensorflow.keras.models import Sequential
	from tensorflow.keras.layers import Dense

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require('dotenv').config()
const { Configuration, OpenAIApi } = require("openai");
const neo4j = require('neo4j-driver')
const driver = neo4j.driver(process.env.NEO4JURI, neo4j.auth.basic(process.env.NEO4JUSER, process.env.NEO4JPASSWORD))
const session = driver.session({ database: "biodb" })
const configuration = new Configuration({
    apiKey: process.env.OPENAI_API_KEY
});
const openai = new OpenAIApi(configuration);
let isConfidential = false;

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