Lecture 1: Introduction to Research — [📝Lecture Notebooks] [
Lecture 2: Introduction to Python — [📝Lecture Notebooks] [
Lecture 3: Introduction to NumPy — [📝Lecture Notebooks] [
Lecture 4: Introduction to pandas — [📝Lecture Notebooks] [
Lecture 5: Plotting Data — [📝Lecture Notebooks] [[
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import os | |
import autogen | |
import memgpt.autogen.memgpt_agent as memgpt_autogen | |
import memgpt.autogen.interface as autogen_interface | |
import memgpt.agent as agent | |
import memgpt.system as system | |
import memgpt.utils as utils | |
import memgpt.presets as presets | |
import memgpt.constants as constants | |
import memgpt.personas.personas as personas |
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import os | |
import autogen | |
import memgpt.autogen.memgpt_agent as memgpt_autogen | |
import memgpt.autogen.interface as autogen_interface | |
import memgpt.agent as agent | |
import memgpt.system as system | |
import memgpt.utils as utils | |
import memgpt.presets as presets | |
import memgpt.constants as constants | |
import memgpt.personas.personas as personas |
IPv4 Addr | IPv6 Addr | ASn | Political Region | Loc | Svc | Org |
---|---|---|---|---|---|---|
8.8.8.8 | 2001:4860:4860::8888 | AS15169 | US | Worldwide (Anycast) | Google Public DNS | |
8.8.4.4 | 2001:4860:4860::8844 | AS15169 | US | Worldwide (Anycast) | Google Public DNS | |
1.1.1.1 | 2606:4700:4700::1111 | AS13335 | US | Worldwide (Anycast) | Cloudflare-DNS | Cloudflare/APNIC |
1.0.0.1 | 2606:4700:4700::1001 | AS13335 | US | Worldwide (Anycast) | Cloudflare-DNS | Cloudflare/APNIC |
208.67.222.222 | 2620:119:35::35 | AS36692 | US | *W |
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# Copyright 2018 SwiftComply.com | |
commands: | |
01_node_install: | |
test: "[ `node --version` != 'v8.10.0' ]" | |
command: "curl --silent --location https://rpm.nodesource.com/setup_8.x | sudo bash -" | |
02_yarn_repo: | |
test: "[ ! -f /etc/yum.repos.d/yarn.repo ]" | |
command: "curl --silent --location https://dl.yarnpkg.com/rpm/yarn.repo | sudo tee /etc/yum.repos.d/yarn.repo" | |
03_yarn_install: | |
test: "[ ! -x /usr/bin/yarn ]" |
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import json | |
import numpy as np | |
import keras | |
import keras.preprocessing.text as kpt | |
from keras.preprocessing.text import Tokenizer | |
from keras.models import model_from_json | |
# we're still going to use a Tokenizer here, but we don't need to fit it | |
tokenizer = Tokenizer(num_words=3000) | |
# for human-friendly printing |
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model = Sequential() | |
model.add(Embedding(num_words, 32, input_length=max_log_length)) | |
# Prevent overfitting using dropout method of regularization | |
model.add(Dropout(0.5)) | |
model.add(LSTM(64, recurrent_dropout=0.5)) | |
model.add(Dropout(0.5)) | |
# Condense to single binary output value | |
model.add(Dense(1, activation='sigmoid')) | |
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy']) | |
# Training set automatically split 75/25 to check validation loss/accuracy at each epoch |
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