Likelihood to Recommend As a Data Analyst, it is my job to analyze large datasets using complex mathematical models. Anaconda provides a one-stop destination with tools like PyCharm, Jupyter, Spyder, and RStudio. One case where it is well suited is for someone who has just started his/her career in this field. The ability to install Anaconda requires zero to little skills and its UI is a lot easier for a beginner to try. On the other hand, for a professional, its ability to handle large data sets could be improved. From my experience, it has happened a lot that the system would crash with big files.
Read full review 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).
Read full review Pros It provides easy access to software like Jupyter, Spyder, R and QT Console etc. Easy installation of Anaconda even without much technical knowledge. Easy to navigate through files in Jupyter and also to install new libraries. R Studio in Anaconda is easy to use for complex machine learning algorithms. Read full review A vast library of functions for all kinds of tasks - Text, Images, Tabular, Video etc. Amazing community helps developers obtain knowledge faster and get unblocked in this active development space. Integration of high-level libraries like Keras and Estimators make it really simple for a beginner to get started with neural network based models. Read full review Cons Although I have generally had positive experiences with Anaconda, I have had trouble installing specific python libraries. I tried to remedy the solution by updating other packages, but in the end, things got really messed up, and I ended up having to uninstall and reinstall a total of about 4 times over the past 2 years. If you have the free version of Anaconda, there is not much support. Googling questions and error messages are helpful, but there were times when I wished I would have been able to ask technical support to help me troubleshoot issues. There were a few times when I tried to install tensorflow and tensorboard via Anaconda on a PC, but I could not get them to install properly. Anaconda allows you to create 'environments' , which allow you to install specific versions of python and associated libraries. You can keep your environments separate so they do not conflict with one another. Anyway, I ended up having to create several 'conda envrionments' just so I could use tensforflow/tensorboard and a few other utilities to avoid errors. This was somewhat annoying, because every time I wanted to run a specific model, I'd have to open up the specific conda environment with the appropriate python libraries. Read full review RNNs are still a bit lacking, compared to Theano. Cannot handle sequence inputs 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. Read full review Likelihood to Renew It's really good at data processing, but needs to grow more in publishing in a way that a non-programmer can interact with. It also introduces confusion for programmers that are familiar with normal Python processes which are slightly different in Anaconda such as virtualenvs.
Read full review Usability The interface is an easy to use command-line interface, or a GUI for launching and/or discovering different parts of the system.
Read full review Support of multiple components and ease of development.
Read full review Support Rating Anaconda provides fast support, and a large number of users moderate its online community. This enables any questions you may have to be answered in a timely fashion, regardless of the topic. The fact that it is based in a Python environment only adds to the size of the online community.
Read full review 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.
Read full review Implementation Rating Use of cloud for better execution power is recommended.
Read full review Alternatives Considered ANACONDA VS
Alteryx Analytics : Even though I find Alteryx to be an excellent tool for managing extremely massive data, Anaconda is much better and easy for analytics. Anaconda VS.
MicroStrategy Analytics : Compared with Anaconda,
MicroStrategy Analytics is very difficult to use and counter-intuitive Anaconda VS.
Power BI For Office 365 : One of the main advantages of BI for Office 364 is its capacity to data connectivity. However, it's very hard to edit data connections, once BI for Office is deployed in other platforms
Read full review 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
Read full review Return on Investment Positive: Lower maintenance cost compared to other tools on the market Positive: Ease in hiring professionals already accustomed to the tool in the job market Positive: Projects are portable, allowing you to share projects with others and execute projects on different platforms, reducing deployment costs Read full review Learning is s bit difficult takes lot of time. Developing or implementing the whole neural network is time consuming with this, as you have to write everything. Once you have learned this, it make your job very easy of getting the good result. Read full review ScreenShots