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.
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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:
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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:
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.
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.
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.
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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.
There are a lot of talks being given at conferences around the world introducing people to GraphQL and getting them excited. Once someone is interested in the technology though, there isn’t a clear place to link them to so they can take the next steps to explore GraphQL’s benefits and try it for themselves. That’s why you’ll see slides at the end of every GraphQL talk with 5–7 links pointing to a variety of resources.