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Open Source A must for deep learning2018-08-17T20:17:18.424ZWe use TensorfFow to solve challenging machine learning problems at scale. TensorfFow fills in the gaps where other machine learning paradigms such as scikit learn fail. Tensorflow is used by several departments in our organization on many user facing business problems. Tensorflow provides an intuitive way to generate and train neural networks. There are also nice visualizations with TensorBoard.,Visualizing learning
Ease of use
Good documentation,Simplify distributed learning examples in the Github repo
Provide more tutorials on distributed training,8,TensorFlow LSTMs decreased timeseries forecasting error by 50% when compared to a simple baseline.
Timeseries anomaly detection reports 20% fewer false positives when compared to a baseline.,Caffe Deep Learning Framework,Apache Hive, Apache Spark, Apache PigKevin PerkinsA must have thing for deep learning2018-11-08T20:12:25.641ZI personally use TensorFlow for my work only. I used this software for about a year in my college during a research project on deep learning. Most of the time, I used this tool to develop a deep learning algorithm which operates around image and videos. Some of the examples where I have used this tool is image classification, video classification, etc.,TensorFlow is the best when you are doing some work around deep learning
You can also use this for natural language processing as it has lot of inbuilt functionality for this.
It also can be used to clean up the data and for data processing, as it provides lots of functionality for that too.,It would be much better if they could provide good documentation and easy ways to understand concepts.
It is difficult to understand the concept behind for example, Tensor Graph, which takes a lot of time.
As you have to write everything, it is time consuming to write the implementation of whole neural network. It would be better if they can provide some wrapper library to make things easier.,9,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.,Keras and MATLAB,Keras, MATLAB, Wolfram MathematicaShambhavi JhaMost advanced deep learning library2018-11-05T22:27:34.784ZI have used TensorFlow during my college time and for some time in my professional career. Most of the time, I have used this to implement deep learning algorithms. More specifically, to build the classification algorithm and some NLP algorithms. In my company role I have used it to build a simple chatbot which can answer some question which is related to the trained document. And it is not used across the whole organisation but just by a few of us.,Fast to implement deep learning algorithm
Fast to train the big model, and easy to deploy on GPU as well
Provides a lot of inbuilt functionality which helps your development move faster
You can see dynamic graph, tensor graph, etc. which is helpful,Long learning curve—it takes a lot of time to learn its basics
Everything is not easy in this product. It takes a lot of time to develop algorithms, and it's difficult too.,9,Helped me to develop building the chatbot.
It takes time to learn and understand its concept of tensor and graph.,Keras and MATLAB,Splunk Enterprise, IntelliJ IDEA, JIRA SoftwareRounak JangirMy perception of the first year with TensorFlow2018-09-25T15:43:30.562ZCurrently, we use machine-learning models to develop solutions for our clients. But sometimes the usual models (decision tree, naive Bayes, random forest) are not helping us to find a suitable model, or it generates too many levels of modeling. Sometimes we use the pre-build neural networks included in some libraries. We are not yet experts in TensorFlow, but using Keras, it helped us to arrive to predictive models in a shorter time and with more accuracy.,Modeling for complex problems with large amounts of data
Modeling when the client is not interested in building the model patiently in levels
Guiding what we are doing wrong with other models,Too many lines of code for some actions
Not very intuitive for non-programming engineers,7,Less modeling time
More certainty about a model, and therefore fewer levels of modeling,KerasJose Machicao, MScBest deep learning library which comes with lots of prebuilt features and visualisation tools2018-08-16T19:37:13.096ZI have used TensorFlow to develop deep learning models. Recently, I have used TensorFlow to write deep neural network implementation to predict the product category(E-commerce product category) from a product image. Other than that, I have used TensorFlow many times, mostly to develop machine learning models. This is being used by one department of my organisation. In my current organisation, we have used TensorFlow to automate some tasks for an e-commerce merchant. In our case, merchants have to upload the product image and all the categories (like category, then sub-category, and then sub-sub-category), so we have developed a machine learning model using Tensorflow which will predict the product category using the product image.,First of all, it is fast. This machine library is faster as compared to other machine learning libraries like theano.
It has lots of prebuilt tools in it for data processing, neural network layers like convolution layer, pooling layer etc. It also hase great prebuilt tools for data visualization.
Easy to deploy its model on GPU. We can train the model created by tensor flow on GPU.
It can be easily used with wrapper library like Keras which makes it easier to write a machine learning model.,Initially understanding this library is bit difficult. It has a steep learning curve.
Sometime the error messages are difficult to understand and debug. So that should be made clear such that even a beginner can solve the issue quickly.
Writing models with TensorFlow only is a bit difficult. So, it's easier to use this with a wrapper library like Keras.,9,It had only positive impact on our objectives as we used it. We easily achieved or goal.
One thing is that, it require lots of processing power while learning.
Along with the processing power it take lots of time to learn.
It produces big model output and that takes a bit of time while loading that model again.,Theano,Keras, Theano, Caffe Deep Learning FrameworkGaurav Yadav

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TensorFlow

17 Ratings

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