disler

This is not working complete code.

This is strictly a v0, scrapy, proof of concept for the first version of a personal AI Assistant working end to end in just ~322 LOC.

It's only a frame of reference for you to consume the core ideas of how to build a POC of a personal AI Assistant.

To see the high level of how this works check out the explanation video. To follow our agentic journey check out the @IndyDevDan channel.

Stay focused, keep building.

azlen

/*
 * credit to Dhrumil Shah (@wandcrafting) and Robert Haisfield (@RobertHaisfield)
 * for the original concept which was part of their RoamGames submission
 * and can be found at: https://www.figma.com/file/5shwLdUCHxSaPNEO7pazbe/
 *
 */

/* ======= OPTIONS ======== */
/* note: if you change these, reload the page to see the effect */

mareks77

Tradingview Watchlist Import Files for Binance

The files below can be imported into a Tradingview watchlist.

List is sorted by volume

artificialsoph

Log into your server with ssh, something like

ssh -i "my_secret.pem" ubuntu@12.123.12.123

If it's a new server, you'll need to install a few things.

Install conda with

jwuphysics

from fastai import *
from fastai.tabular import *
from fastai.vision import *

PATH = os.path.abspath('..')

# distinguish categorical and continuous variables, and dependent variable
cat_names = ['cat1', 'cat2', 'cat3']
cont_names =['cont1', 'cont2']
dep_var = 'target'

korakot

# install chromium, its driver, and selenium
!apt update
!apt install libu2f-udev libvulkan1
!wget https://dl.google.com/linux/direct/google-chrome-stable_current_amd64.deb
!dpkg -i google-chrome-stable_current_amd64.deb
!wget https://edgedl.me.gvt1.com/edgedl/chrome/chrome-for-testing/118.0.5993.70/linux64/chromedriver-linux64.zip
!unzip -j chromedriver-linux64.zip chromedriver-linux64/chromedriver -d /usr/local/bin/
!pip install selenium chromedriver_autoinstaller

# set options to be headless, ..

jeremyjordan

from keras.callbacks import Callback
import keras.backend as K
import numpy as np

class SGDRScheduler(Callback):
    '''Cosine annealing learning rate scheduler with periodic restarts.

    # Usage
        ```python
            schedule = SGDRScheduler(min_lr=1e-5,

max-mapper

ajbrock

import math
from torch.optim.optimizer import Optimizer

# This version of Adam keeps an fp32 copy of the parameters and 
# does all of the parameter updates in fp32, while still doing the
# forwards and backwards passes using fp16 (i.e. fp16 copies of the 
# parameters and fp16 activations).
#
# Note that this calls .float().cuda() on the params such that it 
# moves them to gpu 0--if you're using a different GPU or want to 

internaut

"""
Shows how to do a cross join (i.e. cartesian product) between two pandas DataFrames using an example on
calculating the distances between origin and destination cities.

Tested with pandas 0.17.1 and 0.18 on Python 3.4 and Python 3.5

Best run this with Spyder (see https://github.com/spyder-ide/spyder)
Author: Markus Konrad <post@mkonrad.net>

April 2016