Sebastian Raschka rasbt

## transformer-outline.py
import torch.nn as nn


class Transformer(nn.Module):
    def __init__(self, ...):
        super().__init__()
        ...
        self.encoder_layers = nn.ModuleList(...)
        self.decoder_layers = nn.ModuleList(...)
        ...

## chatgpt-summarize.py
# pip install openai

import openai
import os

# export API key environment variable before
# running this script (see https://platform.openai.com/account/api-keys)
# E.g.,
#
# export OPENAI_API_KEY=copy_paste_key_from_your_account

## whisper-audio-to-text.py
# Setup:
# conda create -n whisper python=3.9
# conda activate whisper
# https://github.com/openai/whisper
# pip install git+https://github.com/openai/whisper.git

# Usage:
# python whisper-audio-to-text.py --audio_dir my_files --out_dir texts

import argparse

## video-subtitles-via-whisper.py
# Sebastian Raschka 09/24/2022
# Create a new conda environment and packages
#   conda create -n whisper python=3.9
#   conda activate whisper
#   conda install mlxtend -c conda-forge

# Install ffmpeg
# macOS & homebrew
#   brew install ffmpeg
# Ubuntu

## onehot-numeric-mixed-data.csv

          
            categorical
            measurement1
            measurement2
            label
            measurement3

            
              F
              1.428571429
              2.721312902
              0
              2.089

            
              R
              0.685939167
              0.982976329
              0
              0.637

            
              P
              1.055817143
              0.624210021
              0
              0.226

            
              S
              0.995956364
              0.321101265
              0
              0.138

            
              R
              1.376773333
              1.578308527
              0
              0.478

            
              R
              0.837229167
              1.549647481
              0
              0.169

            
              Q
              1.552222941
              2.641907404
              0
              1.599

            
              E
              1.492519333
              2.634558656
              0
              1.052

            
              W
              0.816596667
              1.168353374
              0
              0.432

## classifier-calibration.py
import numpy as np
from sklearn.datasets import make_classification
from sklearn.model_selection import cross_val_score
from sklearn.tree import DecisionTreeClassifier
from sklearn.calibration import CalibratedClassifierCV


X, y = make_classification(
    n_samples=1000, n_features=5, n_redundant=2,
    n_clusters_per_class=1, random_state=123)

## .zshrc
### EYECANDY ###
autoload -U colors && colors
PS1="%{$fg[red]%}%n%{$reset_color%}@%{$fg[blue]%}%m %{$fg[yellow]%}%~ %{$reset_color%}%% "
export CLICOLOR=1
#export LSCOLORS=ExFxBxDxCxegedabagacad
#alias ls='ls -GFh'
### END

## miniforge-m1-pytorch-env.yml
name: base
channels:
  - conda-forge
dependencies:
  - absl-py=1.0.0=pyhd8ed1ab_0
  - aiohttp=3.8.1=py39h5161555_0
  - aiosignal=1.2.0=pyhd8ed1ab_0
  - anyio=3.5.0=py39h2804cbe_0
  - appnope=0.1.2=py39h2804cbe_2
  - argh=0.26.2=pyh9f0ad1d_1002

## vgg16.py
import time
import torch
import torch.nn as nn
import torch.nn.functional as F
from torchvision import datasets
from torchvision import transforms
from torch.utils.data import DataLoader


if torch.cuda.is_available():

## ipnbdoctest.py
#!/usr/bin/env python
"""
simple example script for running and testing notebooks.

Usage: `ipnbdoctest.py foo.ipynb [bar.ipynb [...]]`

Each cell is submitted to the kernel, and the outputs are compared with those stored in the notebook.
"""
# License: Public Domain, but credit is nice (Min RK).
	import torch.nn as nn


	class Transformer(nn.Module):
	def __init__(self, ...):
	super().__init__()
	...
	self.encoder_layers = nn.ModuleList(...)
	self.decoder_layers = nn.ModuleList(...)
	...
	# pip install openai

	import openai
	import os

	# export API key environment variable before
	# running this script (see https://platform.openai.com/account/api-keys)
	# E.g.,
	#
	# export OPENAI_API_KEY=copy_paste_key_from_your_account
	# Setup:
	# conda create -n whisper python=3.9
	# conda activate whisper
	# https://github.com/openai/whisper
	# pip install git+https://github.com/openai/whisper.git

	# Usage:
	# python whisper-audio-to-text.py --audio_dir my_files --out_dir texts

	import argparse
	# Sebastian Raschka 09/24/2022
	# Create a new conda environment and packages
	# conda create -n whisper python=3.9
	# conda activate whisper
	# conda install mlxtend -c conda-forge

	# Install ffmpeg
	# macOS & homebrew
	# brew install ffmpeg
	# Ubuntu
categorical	measurement1	measurement2	label	measurement3
F	1.428571429	2.721312902	0	2.089
R	0.685939167	0.982976329	0	0.637
P	1.055817143	0.624210021	0	0.226
S	0.995956364	0.321101265	0	0.138
R	1.376773333	1.578308527	0	0.478
R	0.837229167	1.549647481	0	0.169
Q	1.552222941	2.641907404	0	1.599
E	1.492519333	2.634558656	0	1.052
W	0.816596667	1.168353374	0	0.432
	import numpy as np
	from sklearn.datasets import make_classification
	from sklearn.model_selection import cross_val_score
	from sklearn.tree import DecisionTreeClassifier
	from sklearn.calibration import CalibratedClassifierCV


	X, y = make_classification(
	n_samples=1000, n_features=5, n_redundant=2,
	n_clusters_per_class=1, random_state=123)
	### EYECANDY ###
	autoload -U colors && colors
	PS1="%{$fg[red]%}%n%{$reset_color%}@%{$fg[blue]%}%m %{$fg[yellow]%}%~ %{$reset_color%}%% "
	export CLICOLOR=1
	#export LSCOLORS=ExFxBxDxCxegedabagacad
	#alias ls='ls -GFh'
	### END
	name: base
	channels:
	- conda-forge
	dependencies:
	- absl-py=1.0.0=pyhd8ed1ab_0
	- aiohttp=3.8.1=py39h5161555_0
	- aiosignal=1.2.0=pyhd8ed1ab_0
	- anyio=3.5.0=py39h2804cbe_0
	- appnope=0.1.2=py39h2804cbe_2
	- argh=0.26.2=pyh9f0ad1d_1002
	import time
	import torch
	import torch.nn as nn
	import torch.nn.functional as F
	from torchvision import datasets
	from torchvision import transforms
	from torch.utils.data import DataLoader


	if torch.cuda.is_available():
	#!/usr/bin/env python
	"""
	simple example script for running and testing notebooks.

	Usage: `ipnbdoctest.py foo.ipynb [bar.ipynb [...]]`

	Each cell is submitted to the kernel, and the outputs are compared with those stored in the notebook.
	"""
	# License: Public Domain, but credit is nice (Min RK).