xdralex

Deep Learning

concepts

• forward and backward propagation
• vanishing gradient
• image convolution operation
• feature map, filter/kernel
• receptive field
• embedding
• translation invariance

okld

import random
import streamlit as st
from streamlit.ReportThread import get_report_ctx
from streamlit.server.Server import Server
from threading import Thread
from time import sleep


def main():
    st.line_chart(random.sample(range(100), 20))

qzm

version: '2'

services:
  watchtower:
    image: containrrr/watchtower
    volumes:
      - /var/run/docker.sock:/var/run/docker.sock
      - /root/.docker/config.json:/config.json
    command: nginx-proxy nginx-proxy-le

ndronen

#!/usr/bin/env python
"""
A quick, partial implementation of ENet (https://arxiv.org/abs/1606.02147) using PyTorch.
The original Torch ENet implementation can process a 480x360 image in ~12 ms (on a P2 AWS
instance).  TensorFlow takes ~35 ms.  The PyTorch implementation takes ~25 ms, an improvement
over TensorFlow, but worse than the original Torch.
"""

from __future__ import absolute_import