Juanita  Apio

Juanita Apio

1632733200

Deep Exploration in GraphQL

It's no secret that the Apollo community thinks GraphQL is the best thing to develop since sliced ​​bread. We talk to new teams every week who have received huge improvements in their workflow and development speed by using GraphQL as their new API layer. So we wanted to create a new resource that could help convey that excitement and give people the tools to get GraphQL to work in production at their organization.
#graphql 

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Deep Exploration in GraphQL
Marget D

Marget D

1618317562

Top Deep Learning Development Services | Hire Deep Learning Developer

View more: https://www.inexture.com/services/deep-learning-development/

We at Inexture, strategically work on every project we are associated with. We propose a robust set of AI, ML, and DL consulting services. Our virtuoso team of data scientists and developers meticulously work on every project and add a personalized touch to it. Because we keep our clientele aware of everything being done associated with their project so there’s a sense of transparency being maintained. Leverage our services for your next AI project for end-to-end optimum services.

#deep learning development #deep learning framework #deep learning expert #deep learning ai #deep learning services

Elm Graphql: Autogenerate Type-safe GraphQL Queries in Elm

dillonkearns/elm-graphql  

Why use this package over the other available Elm GraphQL packages? This is the only one that generates type-safe code for your entire schema. Check out this blog post, Type-Safe & Composable GraphQL in Elm, to learn more about the motivation for this library. (It's also the only type-safe library with Elm 0.18 or 0.19 support, see this discourse thread).

I built this package because I wanted to have something that:

  1. Gives you type-safe GraphQL queries (if it compiles, it's valid according to the schema),
  2. Creates decoders for you in a seamless and failsafe way, and
  3. Eliminates GraphQL features in favor of Elm language constructs where possible for a simpler UX (for example, GraphQL variables & fragments should just be Elm functions, constants, lets).

See an example in action on Ellie. See more end-to-end example code in the examples/ folder.

Overview

dillonkearns/elm-graphql is an Elm package and accompanying command-line code generator that creates type-safe Elm code for your GraphQL endpoint. You don't write any decoders for your API with dillonkearns/elm-graphql, instead you simply select which fields you would like, similar to a standard GraphQL query but in Elm. For example, this GraphQL query

query {
  human(id: "1001") {
    name
    homePlanet
  }
}

would look like this in dillonkearns/elm-graphql (the code in this example that is prefixed with StarWars is auto-generated)

import Graphql.Operation exposing (RootQuery)
import Graphql.SelectionSet as SelectionSet exposing (SelectionSet)
import StarWars.Object
import StarWars.Object.Human as Human
import StarWars.Query as Query
import StarWars.Scalar exposing (Id(..))


query : SelectionSet (Maybe HumanData) RootQuery
query =
    Query.human { id = Id "1001" } humanSelection


type alias HumanData =
    { name : String
    , homePlanet : Maybe String
    }


humanSelection : SelectionSet HumanData StarWars.Object.Human
humanSelection =
    SelectionSet.map2 HumanData
        Human.name
        Human.homePlanet

GraphQL and Elm are a perfect match because GraphQL is used to enforce the types that your API takes as inputs and outputs, much like Elm's type system does within Elm. elm-graphql simply bridges this gap by making your Elm code aware of your GraphQL server's schema. If you are new to GraphQL, graphql.org/learn/ is an excellent way to learn the basics.

After following the installation instructions to install the @dillonkearns/elm-graphql NPM package and the proper Elm packages (see the Setup section for details). Once you've installed everything, running the elm-graphql code generation tool is as simple as this:

npx elm-graphql https://elm-graphql.herokuapp.com --base StarWars --output examples/src

If headers are required, such as a Bearer Token, the --header flag can be supplied.

npx elm-graphql https://elm-graphql.herokuapp.com --base StarWars --output examples/src --header 'headerKey: header value'

Learning Resources

There is a thorough tutorial in the SelectionSet docs. SelectionSets are the core concept in this library, so I recommend reading through the whole page (it's not very long!).

The examples/ folder is another great place to start.

If you want to learn more GraphQL basics, this is a great tutorial, and a short read: graphql.org/learn/

My Elm Conf 2018 talk goes into the philosophy behind dillonkearns/elm-graphql

Types Without Borders Elm Conf Talk

(Skip to 13:06 to go straight to the dillonkearns/elm-graphql demo).

If you're wondering why code is generated a certain way, you're likely to find an answer in the Frequently Asked Questions (FAQ).

There's a very helpful group of people in the #graphql channel in the Elm Slack. Don't hesitate to ask any questions about getting started, best practices, or just general GraphQL in there!

Setup

dillonkearns/elm-graphql generates Elm code that allows you to build up type-safe GraphQL requests. Here are the steps to setup dillonkearns/elm-graphql.

Add the dillonkearns/elm-graphql elm package as a dependency in your elm.json. You will also need to make sure that elm/json is a dependency of your project since the generated code has lots of JSON decoders in it.

elm install dillonkearns/elm-graphql
elm install elm/json

Install the @dillonkearns/elm-graphql command line tool through npm. This is what you will use to generate Elm code for your API. It is recommended that you save the @dillonkearns/elm-graphql command line tool as a dev dependency so that everyone on your project is using the same version.

npm install --save-dev @dillonkearns/elm-graphql
# you can now run it locally using `npx elm-graphql`,
# or by calling it through an npm script as in this project's package.json

Run the @dillonkearns/elm-graphql command line tool installed above to generate your code. If you used the --save-dev method above, you can simply create a script in your package.json like the following:

{
  "name": "star-wars-elm-graphql-project",
  "version": "1.0.0",
  "scripts": {
    "api": "elm-graphql https://elm-graphql.herokuapp.com/api --base StarWars"
  }

With the above in your package.json, running npm run api will generate dillonkearns/elm-graphql code for you to call in ./src/StarWars/. You can now use the generated code as in this Ellie example or in the examples folder.

Subscriptions Support

You can do real-time APIs using GraphQL Subscriptions and dillonkearns/elm-graphql. Just wire in the framework-specific JavaScript code for opening the WebSocket connection through a port. Here's a live demo and its source code. The demo server is running Elixir/Absinthe.

Contributors

Thank you Mario Martinez (martimatix) for all your feedback, the elm-format PR, and for the incredible logo design!

Thank you Mike Stock (mikeastock) for setting up Travis CI!

Thanks for the reserved words pull request @madsflensted!

A huge thanks to @xtian for doing the vast majority of the 0.19 upgrade work! :tada:

Thank you Josh Adams (@knewter) for the code example for Subscriptions with Elixir/Absinthe wired up through Elm ports!

Thank you Romario for adding OptionalArgument.map!

Thank you Aaron White for your pull request to improve the performance and stability of the elm-format step! 🎉

Roadmap

All core features are supported. That is, you can build any query or mutation with your dillonkearns/elm-graphql-generated code, and it is guaranteed to be valid according to your server's schema.

dillonkearns/elm-graphql will generate code for you to generate subscriptions and decode the responses, but it doesn't deal with the low-level details for how to send them over web sockets. To do that, you will need to use custom code or a package that knows how to communicate over websockets (or whichever protocol) to setup a subscription with your particular framework. See this discussion for why those details are not handled by this library directly.

I would love to hear feedback if you are using GraphQL Subscriptions. In particular, I'd love to see live code examples to drive any improvements to the Subscriptions design. Please ping me on Slack, drop a message in the #graphql channel, or open up a Github issue to discuss!

I would like to investigate generating helpers to make pagination simpler for Connections (based on the Relay Cursor Connections Specification). If you have ideas on this chime in on this thread.

See the full roadmap on Trello.


Author: dillonkearns
Source Code: https://github.com/dillonkearns/elm-graphql
License: View license

#graphql 

Delbert  Ferry

Delbert Ferry

1622102394

What is a GraphQL query? GraphQL query examples using Apollo Explorer

How to execute GraphQL queries

We know what queries look like and we know what to expect in response when we write them. The next question is: how do we execute them?

It’s important to know that since GraphQL queries are just plain strings, we can use a variety of approaches to fetch data. Among the many options, some that come to mind are:

  • curl
  • fetch
  • GraphQL client libraries (like Apollo Client)

#graphql #graphql query #apollo explorer

Mikel  Okuneva

Mikel Okuneva

1603735200

Top 10 Deep Learning Sessions To Look Forward To At DVDC 2020

The Deep Learning DevCon 2020, DLDC 2020, has exciting talks and sessions around the latest developments in the field of deep learning, that will not only be interesting for professionals of this field but also for the enthusiasts who are willing to make a career in the field of deep learning. The two-day conference scheduled for 29th and 30th October will host paper presentations, tech talks, workshops that will uncover some interesting developments as well as the latest research and advancement of this area. Further to this, with deep learning gaining massive traction, this conference will highlight some fascinating use cases across the world.

Here are ten interesting talks and sessions of DLDC 2020 that one should definitely attend:

Also Read: Why Deep Learning DevCon Comes At The Right Time


Adversarial Robustness in Deep Learning

By Dipanjan Sarkar

**About: **Adversarial Robustness in Deep Learning is a session presented by Dipanjan Sarkar, a Data Science Lead at Applied Materials, as well as a Google Developer Expert in Machine Learning. In this session, he will focus on the adversarial robustness in the field of deep learning, where he talks about its importance, different types of adversarial attacks, and will showcase some ways to train the neural networks with adversarial realisation. Considering abstract deep learning has brought us tremendous achievements in the fields of computer vision and natural language processing, this talk will be really interesting for people working in this area. With this session, the attendees will have a comprehensive understanding of adversarial perturbations in the field of deep learning and ways to deal with them with common recipes.

Read an interview with Dipanjan Sarkar.

Imbalance Handling with Combination of Deep Variational Autoencoder and NEATER

By Divye Singh

**About: **Imbalance Handling with Combination of Deep Variational Autoencoder and NEATER is a paper presentation by Divye Singh, who has a masters in technology degree in Mathematical Modeling and Simulation and has the interest to research in the field of artificial intelligence, learning-based systems, machine learning, etc. In this paper presentation, he will talk about the common problem of class imbalance in medical diagnosis and anomaly detection, and how the problem can be solved with a deep learning framework. The talk focuses on the paper, where he has proposed a synergistic over-sampling method generating informative synthetic minority class data by filtering the noise from the over-sampled examples. Further, he will also showcase the experimental results on several real-life imbalanced datasets to prove the effectiveness of the proposed method for binary classification problems.

Default Rate Prediction Models for Self-Employment in Korea using Ridge, Random Forest & Deep Neural Network

By Dongsuk Hong

About: This is a paper presentation given by Dongsuk Hong, who is a PhD in Computer Science, and works in the big data centre of Korea Credit Information Services. This talk will introduce the attendees with machine learning and deep learning models for predicting self-employment default rates using credit information. He will talk about the study, where the DNN model is implemented for two purposes — a sub-model for the selection of credit information variables; and works for cascading to the final model that predicts default rates. Hong’s main research area is data analysis of credit information, where she is particularly interested in evaluating the performance of prediction models based on machine learning and deep learning. This talk will be interesting for the deep learning practitioners who are willing to make a career in this field.


#opinions #attend dldc 2020 #deep learning #deep learning sessions #deep learning talks #dldc 2020 #top deep learning sessions at dldc 2020 #top deep learning talks at dldc 2020

Delbert  Ferry

Delbert Ferry

1622105190

How to use GraphQL with Javascript – GraphQL.js tutorial

One of the fastest ways to get up and running with GraphQL is to install Apollo Server as middleware on your new or existing HTTP server.

In this short post, we demonstrate how to use Apollo Server to create a GraphQL server with Express.js using the [apollo-server-express] package. At the end, we’ll discuss the tradeoffs of this approach.

#graphql #javascript #graphql.js #graphql.js tutorial