dharmatech

Net Liquidity

Net Liquidity formula:

Net Liquidity = Federal Reserve Balance sheet - Treasury General Account - Reverse Repo

Twitter thread by Max Anderson:

https://twitter.com/maxjanderson/status/1546472693234470912

InnovArul

import torch
import torch.nn as nn
import torch.nn.functional as F

class model(nn.Module):
    def __init__(self):
        super(model, self).__init__()
        self.conv1 = nn.Conv1d(9, 18, kernel_size=3) #9 input channels, 18 output channels
        self.conv2 = nn.Conv1d(18, 36, kernel_size=3) #18 input channels from previous Conv. layer, 36 out
        self.conv2_drop = nn.Dropout2d() #dropout

vanangamudi

import tensorflow as tf

training = tf.placeholder(dtype=tf.bool, name='is_training')

a = tf.placeholder(dtype=tf.float32, name='a')
b = tf.placeholder(dtype=tf.float32, name='b')
c = tf.cond(training, lambda : a+b, lambda : a*b)

sess = tf.Session()

greydanus

'''Solves Pong with Policy Gradients in Tensorflow.'''
# written October 2016 by Sam Greydanus
# inspired by karpathy's gist.github.com/karpathy/a4166c7fe253700972fcbc77e4ea32c5
import numpy as np
import gym
import tensorflow as tf

# hyperparameters
n_obs = 80 * 80           # dimensionality of observations
h = 200                   # number of hidden layer neurons

shanest

#!/usr/bin/env python

import gym
import numpy as np
import tensorflow as tf


class PolicyGradientAgent(object):

    def __init__(self, hparams, sess):

karpathy

""" Trains an agent with (stochastic) Policy Gradients on Pong. Uses OpenAI Gym. """
import numpy as np
import cPickle as pickle
import gym

# hyperparameters
H = 200 # number of hidden layer neurons
batch_size = 10 # every how many episodes to do a param update?
learning_rate = 1e-4
gamma = 0.99 # discount factor for reward

paulirish

What forces layout / reflow

All of the below properties or methods, when requested/called in JavaScript, will trigger the browser to synchronously calculate the style and layout*. This is also called reflow or layout thrashing, and is common performance bottleneck.

Generally, all APIs that synchronously provide layout metrics will trigger forced reflow / layout. Read on for additional cases and details.

Element APIs

Getting box metrics

• elem.offsetLeft, elem.offsetTop, elem.offsetWidth, elem.offsetHeight, elem.offsetParent

karpathy

"""
Minimal character-level Vanilla RNN model. Written by Andrej Karpathy (@karpathy)
BSD License
"""
import numpy as np

# data I/O
data = open('input.txt', 'r').read() # should be simple plain text file
chars = list(set(data))
data_size, vocab_size = len(data), len(chars)

ChadFulton

tsiege

ANNOUNCEMENT

I have moved this over to the Tech Interview Cheat Sheet Repo and has been expanded and even has code challenges you can run and practice against!






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