Written by ChatGPT and me.
I also had to enable temperhum as a non-root user and set up a cron job to call this script every once in a while.
*.json | |
*checkpoint.ipynb |
Written by ChatGPT and me.
I also had to enable temperhum as a non-root user and set up a cron job to call this script every once in a while.
Instructions:
To use it:
""" | |
Break up a long recording to fit within the Whisper API's limits, with some | |
overlap, so no words are missed, and then feed to OpenAI Whisper API to | |
transcribe it to .txt file. Written by endolith and ChatGPT-4. | |
""" | |
import openai | |
import math | |
import os | |
import subprocess |
import numpy as np | |
import matplotlib.pyplot as plt | |
from scipy.ndimage import gaussian_filter | |
rng = np.random.default_rng() | |
# shape = (1024, ) | |
# shape = (512, 512) | |
shape = (128, 128, 128) |
""" | |
Written by GPT3 from this English prompt (with one tweak to the prompt, to get | |
the timestamp parameter to not error out, and PEP8 fixes): | |
---- | |
1. Open a sqlite database with a name like `2022-07-17 17.48.19.db` or | |
`2021-04-11 08.44.16.db` | |
2. Inside is a table called `locations`, with headers [LOCTYPE INTEGER, | |
TIMESTAMP BIGINT, LATITUDE REAL, LONGITUDE REAL,ALTITUDE REAL, ACCURACY |
""" | |
Confirm the formula for estimating the variance of an estimated probability. | |
Created on Fri May 27 11:06:43 2022 | |
""" | |
import numpy as np | |
def func(x): | |
""" |
My first keras experiments:
simplest.py
— A single neuron with no activation function and no bias (so just input→weight→output, can learn y=mx functions only). Show the function for a few random weights. (Doesn't learn anything.)linear with bias.py
— A single neuron with no activation function but with bias (so it can learn y=mx+b functions, such as relationship between Celsius and Fahrenheit).single_neuron_buffer.py
— A single neuron with sigmoid activation and no bias, trained to learn a digital logic buffer.single_neuron_inverter.py
— A single neuron with sigmoid activation and no bias, trained to learn an inverting analog amplifier.""" | |
Train a neural network to learn an FIR filter. | |
Created on Fri Aug 3 15:00:40 2018 | |
""" | |
from tensorflow.keras.models import Sequential | |
from tensorflow.keras.layers import Dense | |
from tensorflow.keras.callbacks import Callback | |
import numpy as np | |
from scipy import signal |
python 3.8.* | |
scikit-learn ==0.24.1 |