Greenhouse vs. Keras

Well suited for Big organizations where recruitment is a daily task. Understanding of Greenhouse workings is simple and it is not difficult to train people who use it for first time. Greenhouse is less appropriate if you are looking for sharing offer letters or doing employee onboarding. Did not had a great experience in doing that

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Keras is quite perfect, if the aim is to build the standard Deep Learning model, and materialize it to serve the real business use case, while it is not suitable if the purpose is for research and a lot of non-standard try out and customization are required, in that case either directly goes to low level TensorFlow API or Pytorch

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• Customization of interview plans, questions, interviewer roles for multiple positions
• Manages efficient and timely candidate, hiring manager, and interviewer communication
• Integrates with other tools needed in the hiring process such as sourcing tools and onboarding systems
• Provides analytics and reporting to allow for data insights and process improvements
• Provides an Onboarding feature that makes the hiring cycle seamless from first interview to hire date

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• One of the reason to use Keras is that it is easy to use. Implementing neural network is very easy in this, with just one line of code we can add one layer in the neural network with all it's configurations.
• It provides lot of inbuilt thing like cov2d, conv2D, maxPooling layers. So it makes fast development as you don't need to write everything on your own. It comes with lot of data processing libraries in it like one hot encoder which also makes your development easy and fast.
• It also provides functionality to develop models on mobile device.

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• Greenhouse Security is simple, but not that granular. For example, everytime new recruiter joins, we have to give them access manually one by one. There is no one click or an easy way to auto assign security unlike other systems that we use such as Workday.
• Greenhouse reporting is very lacking. Even though we are on the highest tier, we cannot extract data through this delivered feature. For example, scorecard custom questions. We have to do this through SQL.
• Greenhouse Audit Log is also lacking. It is not useful at all. We cannot even see what job info changed and who changed it even though we are on the highest tier now.

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• As it is a kind of wrapper library it won't allow you to modify everything of its backend
• Unlike other deep learning libraries, it lacks a pre-defined trained model to use
• Errors thrown are not always very useful for debugging. Sometimes it is difficult to know the root cause just with the logs

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we are gaining more by in from our hiring managers and candidates seem to like the ease of getting us interview times etc

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Greenhouse's user-friendly interface, efficient workflows, and integration capabilities with other HR tools make it a powerful tool for our company, helping us streamline our hiring and onboarding processes. With its customizable workflow and intuitive design, Greenhouse allows us to create a hiring process that suits their specific needs and requirements, without the need for extensive coding or technical expertise. Moreover, Greenhouse's integration capabilities with other HR tools enable us to centralise our hiring and onboarding data, making it easier to access and analyse applicant information, and reducing the risk of data loss or errors.

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I am giving this rating depending on my experience so far with Keras, I didn't face any issue far. I would like to recommend it to the new developers.

I have never needed to call Greenhouse for support. I am not the primary account holder, nor do I use Greenhouse enough to merit calling support. I delegate that responsibility to the Recruiting team. They have never mentioned any experience with Greenhouse technical support, either bad or good. I would assume that they would tell me if the support was poor.

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Keras have really good support along with the strong community over the internet. So in case you stuck, It won't so hard to get out from it.

I think we could have used more delivered functionality and less customized which now results in more maintenance

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Tools appears very light to use. It does not take forever to load. the interface is very simple but capable, unlike many ATS's. The tools is Simple yet loaded with very useful functionalities that save a lot of time and expedites the processes. For different stakeholders, the tools works accordingly.

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Keras is good to develop deep learning models. As compared to TensorFlow, it's easy to write code in Keras. You have more power with TensorFlow but also have a high error rate because you have to configure everything by your own. And as compared to MATLAB, I will always prefer Keras as it is easy and powerful as well.

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• Streamlined the hiring process and new hire time to value
• Increased satisfaction for hiring managers and sales account managers
• Allowed us to gain access to a bigger and more targeted talent pool
• Saved us time and helped us avoid things falling by the wayside by keeping things in one place and sending automated alerts

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• Easy and faster way to develop neural network.
• It would be much better if it is available in Java.
• It doesn't allow you to modify the internal things.

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