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TensorFlow has proven to be a versatile tool for solving a wide range of problems across various industries. In the healthcare sector, …
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What is TensorFlow?
TensorFlow is an open-source machine learning software library for numerical computation using data flow graphs. It was originally developed by Google.
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What is TensorFlow?
TensorFlow is an open-source machine learning software library for numerical computation using data flow graphs. It was originally developed by Google.
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What is TensorFlow?
TensorFlow is an open-source machine learning software library for numerical computation using data flow graphs. It was originally developed by Google.
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The TensorFlow community is thriving. We're thrilled to see the adoption and the pace of machine learning development by people all around the world. TensorFlow is an open-source project for everyone and we're looking forward to building it into something more useful in collab...
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TrustRadius Insights are summaries of user sentiment data from TrustRadius reviews and, when necessary, 3rd-party data sources. Have feedback on this content? Let us know!
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TensorFlow has proven to be a versatile tool for solving a wide range of problems across various industries. In the healthcare sector, users have utilized TensorFlow for patient monitoring, appointment cancellation, scheduling, and registration, leading to improved efficiency and better patient care. It has also been adopted by multiple departments within organizations to address user-facing business challenges.
Another key use case of TensorFlow is in building complex neural networks, particularly when dealing with large training datasets consisting of millions of data points. This capability makes it invaluable for tasks such as predictive analysis and recommendation engines, enabling more accurate predictions and significant cost savings for businesses.
The application of TensorFlow extends beyond traditional domains as well. For instance, it has been employed for time series analysis in the equity market, allowing traders to make informed decisions based on reliable predictions. Moreover, TensorFlow's powerful deep learning algorithms have facilitated image and video classification tasks, enhancing capabilities in areas like computer vision and object recognition.
In addition to these specific use cases, TensorFlow has found practical applications in diverse scenarios such as developing chatbots that answer queries related to trained documents, predicting product categories from images in e-commerce settings, automating tasks for merchants, and building recommendation systems. Its flexibility is especially evident when traditional models fall short or generate complex solutions.
Furthermore, researchers have leveraged TensorFlow's strengths in natural language processing, image processing, and predictive modeling exercises. The tool's visualization capabilities are highly regarded by users who require efficient model training and tuning with large datasets.
Finally, TensorFlow plays a crucial role in real-time inference products by supporting state-of-the-art machine learning and deep learning models. This allows businesses to deploy cutting-edge solutions that deliver fast and accurate results.
Overall, TensorFlow's wide range of use cases demonstrates its effectiveness in various industries and problem-solving scenarios. Its ability to handle large datasets and develop complex models makes it a valuable asset for those seeking advanced machine learning solutions.
Clear Documentation: Many users have found the documentation for multi-GPU support in TensorFlow to be simple and clear. This has been helpful for users who are new to working with multiple GPUs, as it allows them to easily understand and implement this feature.
Powerful Visualization Tools: Reviewers appreciate the ability to visualize the graph using TensorBoard, as it helps them understand and navigate through complex models. The interactive nature of TensorBoard also allows users to log events and monitor output over time, providing a convenient way to perform quick sanity checks.
Active Community Support: Users highly value the active community surrounding TensorFlow, which has helped them learn faster and overcome obstacles in their development work. The availability of readily available answers and top-notch documentation from the community has been instrumental in ensuring a smooth experience while working with TensorFlow.
Lack of User-Friendliness: Users have expressed that TensorFlow has a steep learning curve and is not as simple as other popular Python libraries. Some users find it difficult to understand concepts like Tensor Graph, which takes a lot of time. Additionally, the implementation of a whole neural network can be time-consuming, leading users to suggest the provision of a wrapper library to simplify the process.
Confusing Error Messages: Error messages from TensorFlow can be difficult to understand and debug, especially for beginners. Some users have found certain error messages hard to decipher, resulting in confusion during troubleshooting.
Complexity in Implementing Models: Users feel that implementing complex architectures can be challenging in TensorFlow. Certain actions require too many lines of code and are not intuitive for non-programming engineers. Users suggest creating more high-level APIs like Keras and providing better support for Keras to address these concerns.
Users commonly recommend the following when using Tensorflow:
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Start with the provided examples: Users suggest looking at the examples provided by the developers to get started with Tensorflow. This allows users to understand how the framework works and provides a solid foundation for further exploration.
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Use TensorBoard for visualization: Users recommend utilizing TensorBoard, a built-in tool in Tensorflow, for visualizing and monitoring the training process. It helps users gain insights into the performance of their models and facilitates debugging.
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Consider using Keras with Tensorflow: Many users find it beneficial to use Keras, a high-level neural networks API, in conjunction with Tensorflow. They suggest using Keras for prototyping before diving into Tensorflow, as it simplifies network building and automates certain processes.
These recommendations highlight the importance of starting with examples, leveraging visualization tools like TensorBoard, and exploring the integration of Keras with Tensorflow for enhanced productivity and efficiency.
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August 27, 2022
My review on Tensorflow
We use it for state of the art machine learning / deep learning models for real time inference in our products.
March 23, 2022
Tensorflow is built for deep-divers
Tensorflow is a good intermediate level for building neural networks, or more generally, differentiable programming. Tensorflow v1 and Tensorflow v2 have very significant architectural differences: v1 is about defining a computational graph, upon which operations are performed (like "do one step of backprop" or "batch-evaluate on this data"), while v2 does more computations "live" and is built more like, essentially, a heavy-duty calculator with a differentiable history. v2 is tightly integrated with Keras, so if you intend to use industry-standard layers and architectures from Keras, then Tensorflow is probably your best bet. Both v1 and v2 allow you to define your own layers, or do other differentiable programming tasks; for instance, differentiable physics engines have been written in Tensorflow.
April 29, 2021
Get Flowing with TensorFlow
TensorFlow is used as a development platform for deep learning algorithms, in particular for:
1. Recommendations: selecting the best templates to recommend to users via email in the various countries the company has a market in, over 100 languages supported,
2. User feedback classification: when users provide feedback, natural language processing algorithms implemented in TensorFlow and Keras are used to classify issues so that stakeholders can identify the major issues with a product/product release,
3. Learning-to-rank for search: there is some development on improving search results by switching to deep learning algorithms from a gradient boosting one, and TensorFlow provides that capability, and
4. Computer vision: some experimentation performed on object detection and image classification.
1. Recommendations: selecting the best templates to recommend to users via email in the various countries the company has a market in, over 100 languages supported,
2. User feedback classification: when users provide feedback, natural language processing algorithms implemented in TensorFlow and Keras are used to classify issues so that stakeholders can identify the major issues with a product/product release,
3. Learning-to-rank for search: there is some development on improving search results by switching to deep learning algorithms from a gradient boosting one, and TensorFlow provides that capability, and
4. Computer vision: some experimentation performed on object detection and image classification.
April 09, 2021
TensorFlow, what else?
Obviously, TensorFlow is a great opportunity for everyone who is interested in ML and DL area. We wanted to use TensorFlow in our company, majorly focusing on helping the Operation and Planning domains. Also, it is used as POC for Clearance domain. The purpose is quite similar, by using the DL Technics, through injecting large amount of historical data, learning the patterns, predicting the future trend or advice the best candidate suggestions. Some examples include Commercial Invoice recognition and classification, HS Code prediction, Transportation Time Prediction, Volume Density Prediction, Dimension Prediction.
TensorFlow is the best deep learning library for visualization, training and tuning the model with a large dataset. We are using TensorFlow in the research and development department for the training of natural language, image processing and for the application of specific predictive models. It is also used by the production department to support and host the trained models at the application level.
April 12, 2019
Predict with confidence : Tensorflow
We use TensorFlow for machine learning implementations. Primarily for predictive analysis and recommendation engines. It is being used at an organization level. Our objective is to use a large amount of publicly available data and make meaningful insights from it. It has helped us make better predictions and save costs.
We also use it for time series analysis to make predictions in the equity market. TensorFlow has been a powerful and easy to deploy tool for various algorithms.
We also use it for time series analysis to make predictions in the equity market. TensorFlow has been a powerful and easy to deploy tool for various algorithms.
Tensorflow (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.
December 18, 2018
Tensor Flow Reviews
Score 8 out of 10
Vetted Review
Verified User
Our 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.
November 08, 2018
A must have thing for deep learning
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.
November 05, 2018
Most advanced deep learning library
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.
October 30, 2018
Best deep learning tool
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.
September 25, 2018
My perception of the first year with TensorFlow
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.
August 17, 2018
A must for deep learning
Score 8 out of 10
Vetted Review
Verified User
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.
August 16, 2018
Best deep learning library which comes with lots of prebuilt features and visualisation tools
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.