Open Source libraries
Jon Krohn at the New-York R confenrence (libraries for Data Science)
- igraph (R, python, Mathematica & C++)
- targets (R, successor of drake [for python snakemake])
- janitor (R, cleaner)
- dplyr (R, intuitive data analysis)
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Pennylane
https://github.com/PennyLaneAI/pennylane
Machine learning on quantum hardware. Connect to quantum hardware using PyTorch, TensorFlow, JAX, Keras, or NumPy. Build rich and flexible hybrid quantum-classical models.
Good to try on AWS Bracket
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LSTM & Attention Mechanism
The attention mechanism, initially used in natural language processing fields, has found its way into finance and other domains. It operates on a simple concept: some parts of the input sequence are more important then others. The attention mechanism improve models and understand's capabilities, by allowing the model to focus on specific parts of the input sequence while ignoring others.
Incorporating attention into LSTM networks give a context to the model for predictions, certain historical data points may be more relevant than others. The attention mechanism give the LSTM ability to weigh these points more heavily, leading to more accurate and nuanced predictions.
Prepare de Data ...
from keras.models import Sequential from keras.layers import LSTM, Dense, Dropout, AdditiveAttention, Permute, Reshape, Multiply model = Sequential() model.add(LSTM(units=50, return_sequences=True, input_shape=(X_train.shape[1], 1))) model.add(LSTM(units=50, return_sequences=True, name="lstm_layer"))
attention = AdditiveAttention() attention_result = attention([model.outputs[0], model.outputs[0]]) attention_result = Multiply()([model.outputs[0], attention_result]) model.add(tf.keras.layers.Flatten()) model.add(Dense(1))
Add the Dropout and BatchNormalization before Compiling ...
Train, Predict and Visualize
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DAPHNE service
Create the service file:
sudo vim /etc/systemd/system/daphne.service
[Unit] Description=daphne daemon Requires=daphne.socket After=network.target [Service] Type=simple User=user_name WorkingDirectory=/var/www/app_name ExecStart=/home/user_name/python_env/bin/daphne -u /tmp/daphne.sock app_name.asgi:application [Install] WantedBy=multi-user.target
Command lines:
sudo systemctl start daphne.service sudo systemctl stop daphne.service sudo systemctl restart daphne.service sudo systemctl status daphne.service
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Change ssh port
Change the specified port:
vim /etc/ssh/sshd_config
Restart the deamon:
systemctl restart ssh service ssh restart
Change the port and verify:
ss -tlpn| grep ssh netstat -tlpn| grep ssh
Ask access to firewall:
ufw allow 30/tcp
If you're on a VPS with a firewall, give access to the port
