Anton Larionov Shenziger

## .vimrc
set nocompatible              " be iMproved, required
filetype off                  " required

" set the runtime path to include Vundle and initialize
set rtp+=~/.vim/bundle/Vundle.vim
call vundle#begin()
" alternatively, pass a path where Vundle should install plugins
"call vundle#begin('~/some/path/here')

" let Vundle manage Vundle, required

## tensorflow2_customloops.py
# Author: Ygor Rebouças
#


### The Training Loop
#
# 0) Imports
import tensorflow as tf
import numpy as np

## vimrc
set shell=/bin/bash
if $TERM == "xterm"
    set t_Co=256               " 256 colors
endif
set nocompatible               " be iMproved
let mapleader=","              " change the leader to be a comma vs slash
filetype off                   " required!

set rtp+=~/.vim/bundle/vundle/
call vundle#rc()

## readme.md

      
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                Shenziger
                / readme.md
            
            
              Created
              December 8, 2019 19:04
                — forked from baraldilorenzo/readme.md
            
              
                VGG-16 pre-trained model for Keras
              
          
    ##VGG16 model for Keras
This is the Keras model of the 16-layer network used by the VGG team in the ILSVRC-2014 competition.
It has been obtained by directly converting the Caffe model provived by the authors.
Details about the network architecture can be found in the following arXiv paper:
Very Deep Convolutional Networks for Large-Scale Image Recognition

K. Simonyan, A. Zisserman

  
## classifier_from_little_data_script_1.py
'''This script goes along the blog post
"Building powerful image classification models using very little data"
from blog.keras.io.
It uses data that can be downloaded at:
https://www.kaggle.com/c/dogs-vs-cats/data
In our setup, we:
- created a data/ folder
- created train/ and validation/ subfolders inside data/
- created cats/ and dogs/ subfolders inside train/ and validation/
- put the cat pictures index 0-999 in data/train/cats

## ffmpeg.md

      
              1 file
            
          
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                Shenziger
                / ffmpeg.md
            
            
              Last active
              January 18, 2020 12:31
                — forked from protrolium/ffmpeg.md
            
              
                using ffmpeg to extract audio from video files
              
          
    ffmpeg

Converting Audio into Different Formats / Sample Rates

Minimal example: transcode from MP3 to WMA:

ffmpeg -i input.mp3 output.wma
You can get the list of supported formats with:

ffmpeg -formats
Convert WAV to MP3, mix down to mono (use 1 audio channel), set bit rate to 64 kbps and  sample rate to 22050 Hz:


## files_ops.py
# 1) как можно сделать проверку существует ли файл?

import os.path
os.path.isfile("имя_файла.расширение")

### или так:
os.path.exists('my_file')

### Функция os.getcwd возвращает текущий каталог:
import os

## assistant_library_with_button_demo.py
import logging
import platform
import sys
import threading

import aiy.assistant.auth_helpers
from aiy.assistant.library import Assistant
import aiy.voicehat
from google.assistant.library.event import EventType

## regression.ipynb

      
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                Shenziger
                / regression.ipynb
            
            
              Created
              March 17, 2018 12:41
            
              
                Линейные модели классификации и регрессии
              
          
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## tensorflow_self_check.py
# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
	set nocompatible " be iMproved, required
	filetype off " required

	" set the runtime path to include Vundle and initialize
	set rtp+=~/.vim/bundle/Vundle.vim
	call vundle#begin()
	" alternatively, pass a path where Vundle should install plugins
	"call vundle#begin('~/some/path/here')

	" let Vundle manage Vundle, required
	# Author: Ygor Rebouças
	#


	### The Training Loop
	#
	# 0) Imports
	import tensorflow as tf
	import numpy as np
	set shell=/bin/bash
	if $TERM == "xterm"
	set t_Co=256 " 256 colors
	endif
	set nocompatible " be iMproved
	let mapleader="," " change the leader to be a comma vs slash
	filetype off " required!

	set rtp+=~/.vim/bundle/vundle/
	call vundle#rc()
	'''This script goes along the blog post
	"Building powerful image classification models using very little data"
	from blog.keras.io.
	It uses data that can be downloaded at:
	https://www.kaggle.com/c/dogs-vs-cats/data
	In our setup, we:
	- created a data/ folder
	- created train/ and validation/ subfolders inside data/
	- created cats/ and dogs/ subfolders inside train/ and validation/
	- put the cat pictures index 0-999 in data/train/cats
	# 1) как можно сделать проверку существует ли файл?

	import os.path
	os.path.isfile("имя_файла.расширение")

	### или так:
	os.path.exists('my_file')

	### Функция os.getcwd возвращает текущий каталог:
	import os
	import logging
	import platform
	import sys
	import threading

	import aiy.assistant.auth_helpers
	from aiy.assistant.library import Assistant
	import aiy.voicehat
	from google.assistant.library.event import EventType
	# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
	#
	# Licensed under the Apache License, Version 2.0 (the "License");
	# you may not use this file except in compliance with the License.
	# You may obtain a copy of the License at
	#
	# http://www.apache.org/licenses/LICENSE-2.0
	#
	# Unless required by applicable law or agreed to in writing, software
	# distributed under the License is distributed on an "AS IS" BASIS,