- Labelling Scenes
- Robot Navigation
- Self-Driving Cars
- Body Recognition (Microsoft Kinect)
- Disease and Cancer Detection
- Facial Recognition
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// Storing cryptographic password hashes in database or memory | |
// and comparing with password. | |
package main | |
import ( | |
"log" | |
"errors" | |
"strings" | |
"crypto/subtle" | |
"encoding/base64" |
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"""Easily save tf.data.Datasets as tfrecord files, and restore tfrecords as Datasets. | |
The goal of this module is to create a SIMPLE api to tfrecords that can be used without | |
learning all of the underlying mechanics. | |
Users only need to deal with 2 functions: | |
save(dataset) | |
dataset = load(tfrecord, header) | |
To make this work, we create a .header file for each tfrecord which encodes metadata |
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I always forget the exacty syntax for copying & moving files, especially when dealing with a remote Linux server. And I had wrote some documentation years ago but lost it (so much for persistent storage!). So here's some notes: | |
Change into the directory you want to download the files | |
cd /home/dir_to_dump_file_in | |
Log onto server using Safe File Transfer Protocol--just like how you would "ssh" into the Linux server | |
sftp [email protected] | |
password: ilovemarge | |
Obtain the files from the Linux server on to your local server |
Exhaustive list of SPDX (Software Package Data Exchange) licenses: https://spdx.org/licenses/
Warning: this document has to be updated by pointing to https://github.com/SpenceKonde/ATTinyCore
This note describes the configuration of an ATtiny85 based microcontroller development board named Digispark and similar to the Arduino line. It is available in many online marketplaces for roughly 1 dollar (e.g., Ebay, Amazon, AliExpress) and is shipped fully assembled, including a V-USB interface (a software-only implementation of a low-speed USB device for Atmel's AVR microcontrollers). Coding is similar to Arduino: it uses the familiar Arduino IDE and is already provided with a ready-to-use bootloader (
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# Create first network with Keras | |
from keras.models import Sequential | |
from keras.layers import Dense | |
import numpy | |
import threading as t | |
import tensorflow as tf | |
graph = tf.get_default_graph() | |
def t_thread(): |
nvidia-smi
to check for current memory usage.watch -n 1 nvidia-smi
to monitor memory usage every second.- Often, extra Python processes can stay running in the background, maintaining a hold on the GPU memory,
even if
nvidia-smi
doesn't show it.- Probably due to running Keras in a notebook, and then running the cell that starts the processes again, since this will fork the current process, which has a hold on GPU memory. In the future, restart the kernel first, and stop all process before exiting (even though they are daemons and should stop automatically when the parent process ends).
Use Python to:
- send a plain text email
- send an email with attachment
- receive and filter emails according to some criteria
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