Jupyter Notebook is an open-source web application that allows users to create and share documents containing live code, equations, visualizations and narrative text. Uses include: data cleaning and transformation, numerical simulation, statistical modeling, data visualization, and machine learning. It supports over 40 programming languages, and notebooks can be shared with others using email, Dropbox, GitHub and the Jupyter Notebook Viewer. It is used with JupyterLab, a web-based IDE for…
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Keras
Score 7.0 out of 10
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Keras is a Python deep learning library
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Jupyter Notebook
Keras
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Jupyter Notebook
Keras
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Jupyter Notebook
Keras
Considered Both Products
Jupyter Notebook
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Anonymous
Chose Jupyter Notebook
As a beginner I tried all of them but finally due to simple and user friendly interface I opted it. I also tried visual basic which is also good platform with versatility, however for basic need it is the best.
Jupyter is very easy to understand and easy to use. And can also be used by a student, freelancer, small industries, big industries. Jupyter also provides you a tool to work with machine learning and artificial intelligence.
Jupyter Notebook is very attractive platform for new developers to code and to learn programming and perform tasks as compared to other IDE. It has very well and easy visualization, interactive programming and sharing the live code and slideshow is very easy as compare to …
Jupyter is still the most well known and widely used platform I've seen. Using it over other competition like Zeppelin simply because of its availability, and my familiarity with its functionality.
Jupyter Notebook is unique in that it offers a flexible, lightweight, easy-to-replicate way of organizing your code in a visually intuitive fashion that can be exported in a number of formats. I've found that the broad functionalities available within the notebooks suit a lot …
Well, so far Jupyter Notebook has been the better tool for me. It gives us more freedom & has more ability to train ML models & do the data visualization more efficiently. It's easier to operate & has a very simple-to-understand UI & with the support for taking data from …
I have used PyCharm as well as Jupyter Notebook and for me, Jupyter wins almost every time. I really like its user-friend interface for someone who is new to python programming. The ability to run a big chunk of code part by part is a big game-changer for me. One thing I would …
It should have cleaner support for multi-environment setup and should also increase the amount of features. Moreover, more support should be present for other programming languages. It should also have the option to set a specific location that opens up whenever I run command …
Jupyter Notebook has a nicer interface than RStudio in our opinion and since most of our group is familiar with Jupyter Notebook it has made it a default choice. Overall the interactive programming as well as the easy visualizations, model deployment, and markdown made Jupyter …
Jupyter Notebook is the core feature extended on by many commercial alternatives. The commercial alternatives have more feature integration with the rest of their portfolio. RStudio is another competitor for interactive and literate programming.
An interesting thing is that Jupyter Notebook is run on browser environments which may or may not be a positive feature according to cases. VS Code on [the] other hand doesn't use any interface and can run Jupyter Notebooks too. Sometimes my browser consumes too much RAM due to …
With Jupyter Notebook besides doing data analysis and performing complex visualizations you can also write machine learning algorithms with a long list of libraries that it supports. You can make better predictions, observations etc. with it which can help you achieve better …
I like Jupyter Notebook over the other two because it keeps my work more organized. It helps me to structure my workflow and the ability to run commands in chunks keeps me from being confused when coming back to the work after some time.
I selected Jupyter Notebook because this is better integrated with the existing production systems than optional tools (for example, R). It is also commonly used tool within the scientist community.
When I tried Zeppelin in 2017, it was still in initial versions, Jupyter was way ahead as of then. Zeppelin had limitations and I wasn't confident of it making progress as much as Jupyter.
As Keras is the high level API, so using Keras, we don't have to be bothered by the low level TensorFlow complexity, and we can reduce a lot coding and testing efforts.
For beginners, I always recommend starting with Keras, because it's really easy to use and learn at first. There is not much pre-requisite for this to start with.
Keras is a good point where you can learn lots of things and also have hands-on experience. There is not much comparison of Keras with Tensorlow, as Keras is a wrapper library which supports TensorFlow and Theano as backends for computation. But once you have enough knowledge …
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 …
TensorFlow and Caffe are bit hard to learn but they give you power to implement everything by you own. But most of the time it is not required to implement our own algorithm, we can solve the problem with just using the already provided algorithms. As compared to TensorFlow and …
I would rate it 9/10 while recommending Jupyter Notebook as it offers me a wide range of functionality to operate. It is very well suited for someone who is new to python programming as the user interface helps you build code line by line. I personally have written multiple programs in Python using Jupyter Notebook as it helps me organize long code by breaking it in a structure. Also the ability to write comments using '#' helps a lot to a reader understand the code.
I would recommend it for use when anyone wants to quickly develop a neural network. Or if a user is solving any machine learning problem that includes deep learning. And this kind of problem will be like image recognition, face recognition, doing some text analysis using deep learning which includes LSTM or some other algorithm.
Need more Hotkeys for creating a beautiful notebook. Sometimes we need to download other plugins which messes [with] its default settings.
Not as powerful as IDE, which sometimes makes [the] job difficult and allows duplicate code as it get confusing when the number of lines increases. Need a feature where [an] error comes if duplicate code is found or [if a] developer tries the same function name.
Jupyter is highly simplistic. It took me about 5 mins to install and create my first "hello world" without having to look for help. The UI has minimalist options and is quite intuitive for anyone to become a pro in no time. The lightweight nature makes it even more likeable.
Jupyter Notebook is unique in that it offers a flexible, lightweight, easy-to-replicate way of organizing your code in a visually intuitive fashion that can be exported in a number of formats. I've found that the broad functionalities available within the notebooks suit a lot of needs I have for EDA, modeling, and data export that makes other software products fairly redundant.
As Keras is the high level API, so using Keras, we don't have to be bothered by the low level TensorFlow complexity, and we can reduce a lot coding and testing efforts.