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1. General Background and Overview

• Probabilistic Data Structures for Web Analytics and Data Mining : A great overview of the space of probabilistic data structures and how they are used in approximation algorithm implementation.
• Models and Issues in Data Stream Systems
• Philippe Flajolet’s contribution to streaming algorithms : A presentation by Jérémie Lumbroso that visits some of the hostorical perspectives and how it all began with Flajolet
• Approximate Frequency Counts over Data Streams by Gurmeet Singh Manku & Rajeev Motwani : One of the early papers on the subject.
• [Methods for Finding Frequent Items in Data Streams](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.187.9800&rep

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package net.debasishg.fin

import org.joda.time.DateTime

trait Account {
  def no: String
  def name: String
  def openedOn: DateTime
  def closedOn: Option[DateTime]
}

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1. Feature Learning

• Learning Feature Representations with K-means by Adam Coates and Andrew Y. Ng
• The devil is in the details: an evaluation of recent feature encoding methods by Chatfield et. al.
• Emergence of Object-Selective Features in Unsupervised Feature Learning by Coates, Ng
• Scaling Learning Algorithms towards AI Benjio & LeCun
• A Theory of Feature Learning by Brendan van Rooyen, Robert C. Williamson

2. Deep Learning

• Dropout: A Simple Way to Prevent Neural Networks from Overfitting by Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever and Ruslan Salakhutdinov
• [Understanding

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"""
Given a dictionary, transform it to a string. Then byte encode that string. Then base64 encode it and since this will go
on a url, use the urlsafe version. Then decode the byte string so that it can be else where.
"""
data = base64.urlsafe_b64encode(json.dumps({'a': 123}).encode()).decode()

# And the decode is just as simple...
data = json.loads(base64.urlsafe_b64decode(query_param.encode()).decode())
# Byte encode the string, base64 decode that, then byte decode, finally transform it to a dictionary

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Join based queries with skunk decoders - folding with a Semigroup

Domain Model

Let's take a simple example that models an employee along with the salaries accrued in a year. Here's the domain model in Scala:

case class Salary(empId: String, month: Int, amount: BigDecimal)
case class Employee(id: String, name: String, salaries: List[Salary])

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import java.net.URI
import cats.effect._

object Main extends IOApp {
  override def run(args: List[String]): IO[ExitCode] =
    RedisClient.makeWithURI[IO](new URI("http://localhost:6379")).use { cmd =>
      import cmd._

      val result = for {

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import cats.data.ValidatedNec
import cats.implicits._

import eu.timepit.refined._
import eu.timepit.refined.api.Refined
import eu.timepit.refined.auto._
import eu.timepit.refined.numeric._
import eu.timepit.refined.collection._

object person {

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Thread Pools

Thread pools on the JVM should usually be divided into the following three categories:

1. CPU-bound
2. Blocking IO
3. Non-blocking IO polling

Each of these categories has a different optimal configuration and usage pattern.

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1. Graph Convolution Network - a blog post and the associated tweet. Check for the video.
2. Deep Graph Library
3. Machine Learning on Graphs: A Model and Comprehensive Taxonomy by Ines Chami, Sami Abu-El-Haija, Bryan Perozzi, Christopher Ré, Kevin Murphy
4. Relational inductive biases, deep learning, and graph networks by Peter W. Battaglia, Jessica B. Hamrick, Victor Bapst, Alvaro Sanchez-Gonzalez, Vinicius Zambaldi, Mateusz Malinowski, Andrea Tacchetti, David Raposo, Adam Santoro, Ryan Faulkner, Caglar Gulcehre, Francis Song, Andrew Ballard, Justin Gilmer, George Dahl, Ashish Vaswani, Kelsey Allen, Charles Nash, Victoria Langston, Chris Dyer, Nicolas Heess, Daan Wierstra, Pushmeet Kohli, Matt Botvinick, Oriol Vinyals, Yujia Li, Razvan Pascanu - For the intuition of what GNNs can achieve, what the pro

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Keeping a remote fork in sync
-----------------------------
git status
git checkout master
git remote -v
git remote add upstream https://github.com/lightbend/cloudflow.git
git remote -v
git fetch upstream
git pull upstream master
git push origin master