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A must for deep learningWe 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 PigA must have thing for deep learningI 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 MathematicaMost advanced deep learning libraryI 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 SoftwareMy perception of the first year with TensorFlowCurrently, 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,KerasBest deep learning library which comes with lots of prebuilt features and visualisation toolsI 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 Framework
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TensorFlow
17 Ratings
Score 8.8 out of 101
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TensorFlow Reviews

TensorFlow
17 Ratings
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Score 8.8 out of 101
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Kevin Perkins profile photo
August 17, 2018

TensorFlow Review: "A must for deep learning"

Score 8 out of 10
Vetted Review
Verified User
Review Source
We 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
TensorFlow is a must for deep learning. If deep learning is not necessary then other machine learning packages such as scikit-learn are a more appropriate choice. We have found that TensorFlow can be very useful in performing anomaly detection on time series data. TensorFlow provides easy aAPI for generating LSTM and CNN neural networks.
Read Kevin Perkins's full review
Shambhavi Jha profile photo
November 08, 2018

TensorFlow Review: "A must have thing for deep learning"

Score 9 out of 10
Vetted Review
Verified User
Review Source
I 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.
There are lots of scenarios where TensorFlow can be used efficiently. One of them is image processing and video processing that include classification, recognition, etc. It can also be used for natural language processing and building chatbots. As TensorFlow has LSTM in built, it will be easy to use this for doing NLP stuff.
Read Shambhavi Jha's full review
Rounak Jangir profile photo
November 05, 2018

TensorFlow Review: "Most advanced deep learning library"

Score 9 out of 10
Vetted Review
Verified User
Review Source
I 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.
If someone wants to develop or work around deep learning (artificial neural network), then it is a good choice to use. It is also useful for natural language processing. Implementing the LSTM is easy with this. Some examples of where it can be used are image classification, video classification, creating chatbots, etc.
Read Rounak Jangir's full review
Jose Machicao, MSc profile photo
September 25, 2018

Review: "My perception of the first year with TensorFlow"

Score 7 out of 10
Vetted Review
Verified User
Review Source
Currently, 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
It is better when there is a lot of data available and the complexity of variables is high—for instance, when nobody has modeled that problem before. If there is not enough data, it does not work, or if it works, it is not going to help to model reality. It is also very good to test the performance of any other model even if the TensorFlow model itself is not going to be the solution for the client.
Read Jose Machicao, MSc's full review
Gaurav Yadav profile photo
August 16, 2018

TensorFlow Review: "Best deep learning library which comes with lots of prebuilt features and visualisation tools"

Score 9 out of 10
Vetted Review
Verified User
Review Source
I 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.
The best suited scenario is when you want to develop a deep learning model consisting of a deep neural network, like doing something around images/video, which may include convolution network. Other than this, it can also be used to develop NLP models. But if you are developing conventional machine learning, I don't think this is much required as that can be done using Python libraries like sciPy.
Read Gaurav Yadav's full review
Ajay Shewale profile photo
October 30, 2018

TensorFlow Review: "Best deep learning tool"

Score 9 out of 10
Vetted Review
Verified User
Review Source
I have used this tool for building a recommendation system. We have built a system to recommend flights to users so we used TensorFlow to build that. I personally have used this tool in a different sector like doing image processing, building an image recognition algorithm and implementing neural networks etc. This is being used by a part of our company not by the whole or across the company.
  • TensorFlow is very powerful to build the neural networks, it gives you the power to write your own implementation with full customisation.
  • It has inbuilt methods which helps a lot when it comes to writing your own implementation of neural networks
  • It has lots of inbuilt methods to do the data processing like reading data from a directory as classes using an image data generator etc.
  • First and biggest con is that it has a very tense learning curve. Understanding the concept of a tensor, a dynamic graph is difficult and takes a lot of time to learn
  • As compared to Keras, TensorFlow takes a lot of time to build and implement a neural network. You have to write everything by yourself.
There are lots of use cases to use this tool and also lot of cases where you should look to this. Like when it comes to building or playing with a deep learning algorithm like neural networks you should choose this one. But if you are implementing some other machine learning algorithm then definitely you have to check whether TensorFlow is a good choice or not. And you can also use this for NLP as well.
Read Ajay Shewale's full review

TensorFlow Scorecard Summary

About TensorFlow

TensorFlow is an open-source machine learning software library for numerical computation using data flow graphs. It was originally developed by Google.
Categories:  Machine Learning

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