Namburger

import os
import argparse
import cv2
import numpy as np
import sys
import time
import importlib.util
from tflite_runtime.interpreter import Interpreter
from tflite_runtime.interpreter import load_delegate

bookjan

import numpy as np
import cv2
import sys

cap = cv2.VideoCapture(0)

face_cascade = cv2.CascadeClassifier('<PATH_TO_CASCADES_FOLDER>/haarcascade_frontalface_default.xml')

while(True):
    # Capture frame-by-frame

zeryx

# This code example describes how to pre-load a tensorflow graph file
# into an Algorithmia container and load the graph into memory.
# This approach allows us to preserve the graph in system memory between API calls,
# Improving overall performance.

# We also document how to evict the tensorflow GPU memory context between API requests by
# moving it to a separate thread, and how to define the amount of GPU memory an algorithm uses.
# These GPU tweaks significantly improve performance on Algorithmia's infrastructure

import Algorithmia

nzw0301

*2vec papers

1. act2vec, trace2vec, log2vec, model2vec https://link.springer.com/chapter/10.1007/978-3-319-98648-7_18
2. apk2vec https://arxiv.org/abs/1809.05693
3. app2vec http://paul.rutgers.edu/~qma/research/ma_app2vec.pdf
4. ast2vec https://arxiv.org/abs/2103.11614
5. attribute2vec https://arxiv.org/abs/2004.01375
6. author2vec http://dl.acm.org/citation.cfm?id=2889382
7. baller2vec https://arxiv.org/abs/2102.03291
8. bb2vec https://arxiv.org/abs/1809.09621

kingjr

# Author: Jean-Remi King <[email protected]>
"""
    Illustrate how a hinge loss and a log loss functions 
    typically used in SVM and Logistic Regression 
    respectively focus on a variable number of samples.
    For simplification purposes, we won't consider the
    regularization or penalty (C) factors.
"""
import numpy as np
import matplotlib.animation as animation

ebenolson

# Context manager to generate batches in the background via a process pool
# Usage:
#
# def batch(seed):
#    .... # generate minibatch
#    return minibatch
#
# with BatchGenCM(batch) as bg:
#    minibatch = next(bg)
#    .... # do something with minibatch

ebenolson

"""
Functions to create network diagrams from a list of Layers.

Examples:

    Draw a minimal diagram to a pdf file:
        layers = lasagne.layers.get_all_layers(output_layer)
        draw_to_file(layers, 'network.pdf', output_shape=False)

    Draw a verbose diagram in an IPython notebook: