TrustRadius
Open SourceTensorflow - a feature rich & easy to use distributed open source ML framework2019-02-15T19:50:02.003ZTensorflow (TF) is one of the Machine Learning (ML) libraries at LinkedIn. The necessary plumbing needed to deploy, maintain and monitor a TF project is under active development. It is currently used for building Wide and Deep Neural Networks, where training data is in the order of millions. However, in production, tree-based models or logistic regression are still popular.,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.,Profiling the TensorFlow (TF) graph for performance optimizations is still a challenge due to lack of proper documentation.
In our experiments with using TF-GPU on Kubernetes, we see constant memory issues causing nodes to crash.
There is still a significant learning curve and it's not as simple as other popular Python libraries. Having said that, the TF team and community are actively working on this problem.,10,Tensorflow (TF) has really simplified building complex models in a few lines of manageable code.
TF Serving makes deployment very easy too.
TensorBoard makes monitoring a pleasing task for features like charts, embeddings, histograms, what-if tools, etc.
The minimal learning curve is absolutely worth the effort for all the benefits.,Nitin PasumarthyTensor Flow Reviews2018-12-18T15:31:34.348ZOur organization was using it when it was 6 months old. It's a open source software by Google pretty robust. We use this AI to solve our healthcare problems when it comes to patient monitoring, appointment cancellation, scheduling, and registration.,Multi-GPU support. It works; the documentation is simple and clear. You’ll still need to figure out how to divide and conquer your problem, but isn’t that part of the fun?
Training across distributed resources (i.e., cloud). As of v0.8, distributed training is supported.
Queues for putting operations like data loading and preprocessing on the graph.
Visualize the graph itself using TensorBoard. When building and debugging new models, it is easy to get lost in the weeds. For me, holding mental context for a new framework and model I’m building to solve a hard problem is already pretty taxing, so it can be really helpful to inspect a totally different representation of a model; the TensorBoard graph visualization is great for this.
Logging events interactively with TensorBoard. In UNIX/Linux, I like to use tail -f to monitor the output of tasks at the command line and do quick sanity checks. Logging events in TensorFlow allows me to do the same thing, by emitting events and summaries from the graph and then monitoring output over time via TensorBoard (e.g., learning rate, loss values, train/test accuracy).
Model checkpointing. Train a model for a while. Stop to evaluate it. Reload from checkpoint, keep training.
Performance and GPU memory usage are similar to Theano and everything else that uses CUDNN. Most of the performance complaints in the earlier releases appear to have been due to using CUDNNv2, so TensorFlow v0.8 (using CUDNNv4) is much improved in this regard.,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.,8,Positive Impact- As I mentioned before its open source. Very easy to learn for average programmer/ developer. We were able to design a POC model for understanding the patient appointment cancellation snd reasons behind it in 3 week time frame.
Negative Impact- If you are using tensor flow for small project it works fine. If you are trying to build a model for face recognition it will be hard to program and train the system. It needs data to be processed before hand cannot learn on the go.,,TheanoNisha murthyA 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, MSc
Unspecified
TensorFlow
23 Ratings
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