A software project management system used to plan, track and release great software with this lightweight and customizable system that integrates into any project management workflow. FogBugz is designed for software development teams and includes all the project management tools developers need straight out of the box. Users can: Track projects from start to finish - With tasks and subtasks for each case with required details and track them to ensure…
$62
per month
TensorFlow
Score7.6 out of 10
N/A
TensorFlow is an open-source machine learning software library for numerical computation using data flow graphs. It was originally developed by Google.
FogBugz has been a very useful tool to our organization, and much preferred over other options we reviewed, mainly JIRA. There are still some improvements needed, but with the fairly recent acquisition by DevFactory, we have a great deal of hope for what is in store given DevFactory's focus and transparency. It seems like both DevFactory and FogBugz customers are eager for substantial improvements on the front-end, but there is/was a great deal of backend housecleaning that definitely needed to take place first.
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
TensorFlow is great for most deep learning purposes. This is especially true in two domains: 1. Computer vision: image classification, object detection and image generation via generative adversarial networks 2. Natural language processing: text classification and generation. The good community support often means that a lot of off-the-shelf models can be used to prove a concept or test an idea quickly. That, and Google's promotion of Colab means that ideas can be shared quite freely. Training, visualizing and debugging models is very easy in TensorFlow, compared to other platforms (especially the good old Caffe days). In terms of productionizing, it's a bit of a mixed bag. In our case, most of our feature building is performed via Apache Spark. This means having to convert Parquet (columnar optimized) files to a TensorFlow friendly format i.e., protobufs. The lack of good JVM bindings mean that our projects end up being a mix of Python and Scala. This makes it hard to reuse some of the tooling and support we wrote in Scala. This is where MXNet shines better (though its Scala API could do with more work).
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
Tasks, Subtasks, and notes. All three of these areas were critical for our team. Tasks in Fogbugz were a bit easier to see than in more bug based software like Trello or JIRA
The entire screen is used to view a task or case. Clicking on a task or case will open up and take up the entire screen, aside from the sidebar nav columns. I like to see details and I think Fogbugz does this very well, using up as much digital real estate as possible.
Flowcharting in Fogbugz with Creately is nice - instead of getting an exterior flowchart software like Lucidchart, Creately works right in Fogbugz.
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
The simplicity of a single admin type user is not great because anyone who can create a job or client in the system, can also add and delete users. Content and User administrative rights should be separated.
There are ways to change the terminology/lexicon within the tool, but we are not able to get it to work even after reaching out to tech support. So we are forced to use the system terminology that doesn't match up to our company making training a bit difficult.
There is a subscribe function that you can opt into, there should be a way to add subscribers as you create a new task.
Theano is perhaps a bit faster and eats up less memory than TensorFlow on a given GPU, perhaps due to element-wise ops. Tensorflow wins for multi-GPU and “compilation” time.
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
Community support for TensorFlow is great. There's a huge community that truly loves the platform and there are many examples of development in TensorFlow. Often, when a new good technique is published, there will be a TensorFlow implementation not long after. This makes it quick to ally the latest techniques from academia straight to production-grade systems. Tooling around TensorFlow is also good. TensorBoard has been such a useful tool, I can't imagine how hard it would be to debug a deep neural network gone wrong without TensorBoard.
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
Keras is built on top of TensorFlow, but it is much simpler to use and more Python style friendly, so if you don't want to focus on too many details or control and not focus on some advanced features, Keras is one of the best options, but as far as if you want to dig into more, for sure TensorFlow is the right choice
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
Saves time by quickly allowing Developers to make the necessary notes without getting bogged down in bloated UIs
Has allowed us to look back easily and see the exact code changes made for the exact Case to aid in decisions for current changes, increasing the certainty of the decided path, without regression
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
