Robust, stable, reliable but not the simplest/most flexible option
Continue reading
Overview
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.
Recent Reviews
Reviewer Pros & Cons
Pricing
N/A
Unavailable
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.
Entry-level set up fee?
- No setup fee
Offerings
- Free Trial
- Free/Freemium Version
- Premium Consulting/Integration Services
Would you like us to let the vendor know that you want pricing?
25 people also want pricing
Alternatives Pricing
What is Qlik Sense?
Qlik Sense® is a self-service BI platform for data discovery and visualization. It supports a full range of analytics use cases—data governance, pixel-perfect reporting, and collaboration. Its Associative Engine indexes and connects relationships between data points for creating actionable insights.
N/A
Unavailable
What is Dataiku?
Dataiku is a French startup and its product, DSS, is a challenger to market incumbents and features some visual tools to assist in building workflows.
Product Details
- About
- Tech Details
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.
TensorFlow Video
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...
Show More TensorFlow Technical Details
| Operating Systems | Unspecified |
|---|---|
| Mobile Application | No |
Comparisons
Compare with
Reviews and Ratings
(52)
Community Insights
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!
- Business Problems Solved
- Pros
- Cons
- Recommendations
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:
-
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.
-
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.
-
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.
Attribute Ratings
Reviews
(1-14 of 14)Companies can't remove reviews or game the system. Here's why
I prefer Pytorch overall, recent models are often only available with Pytorch
Pytorch is also easier to use and it is often easier to find support for Pytorch code nowadays than TensorFlow
Also it seems like lots of Google internal resource uses JAX. I mostly uses TensorFlow to maintain code already in production.
Pytorch is also easier to use and it is often easier to find support for Pytorch code nowadays than TensorFlow
Also it seems like lots of Google internal resource uses JAX. I mostly uses TensorFlow to maintain code already in production.
August 27, 2022
My review on Tensorflow
April 29, 2021
Get Flowing with TensorFlow
Can't seem to choose any deep learning platform in the above, so I'll list it here:
1. Apache MXNet: this has been used for one of our main algorithms for search as an end-to-end pipeline. We chose this because of the Scala bindings, which makes it easier to integrate with out JVM backend. MXNet seems comparable to TensorFlow, although community support is not as good as TensorFlow, and there are issues with memory leaks that are being worked on. TensorFlow in general is easier to use, but MXNet isn't too far behind.
2. Keras: still a favorite. Often I use this when paired with TensorFlow. TensorFlow 2.0 will make it even easier.
3. PyTorch: only used it a little, so it's hard to provide a good opinion.
4. DL4J: used it initially in an early days project because it has good JVM support. Harder to used not because of poor API design, but because community support is lacking and features don't come out as fast as TensorFlow.
1. Apache MXNet: this has been used for one of our main algorithms for search as an end-to-end pipeline. We chose this because of the Scala bindings, which makes it easier to integrate with out JVM backend. MXNet seems comparable to TensorFlow, although community support is not as good as TensorFlow, and there are issues with memory leaks that are being worked on. TensorFlow in general is easier to use, but MXNet isn't too far behind.
2. Keras: still a favorite. Often I use this when paired with TensorFlow. TensorFlow 2.0 will make it even easier.
3. PyTorch: only used it a little, so it's hard to provide a good opinion.
4. DL4J: used it initially in an early days project because it has good JVM support. Harder to used not because of poor API design, but because community support is lacking and features don't come out as fast as TensorFlow.
April 09, 2021
TensorFlow, what else?
Keras is built on top of TensorFlow, but it is much simpler to use and more Python style friendly, so if you don't want to focus on too many details or control and not focus on some advanced features, Keras is one of the best options, but as far as if you want to dig into more, for sure TensorFlow is the right choice
TensorFlow provides a wide range of algorithms with more detail and customization options compared to others. Also, the library is advanced and updates regularly for optimization and new functions.
April 12, 2019
Predict with confidence : Tensorflow
Most of the machine learning platforms these days support integration with R and Python libraries. So, the use of reusable libraries is not an issue. TensorFlow performs well in cloud hosting and support for GPU/TPU. However, where it lacks compared to Azure is a graphical front-end to drag and drop layers.
Thought about alternatives like scikit-learn, xgboost, pytorch, caffe2, fastai exist, but they don't offer as many tools and functionality as TensorFlow does. It is better to inanest in a eco-system which is very active and well maintained by giants. Being open source, one can contribute and modify the code if anything is missing or has issues.
December 18, 2018
Tensor Flow Reviews
Score 8 out of 10
Vetted Review
Verified User
Theano is a Python library and is good for making algorithms from scratch. It is an alternative to Tensor flow. We used tensor flow because it is open source Java source and easy to learn and use.
TensorFlow is developed and maintained by Google. It's the engine behind a lot of features found in Google applications, such as: * recognizing spoken words * translating from one language to another * improving Internet search results Making it a crucial component in a lot of Google applications. As such, continued support and development is ensured in the long-term, considering how important it is to the current maintainers.
November 08, 2018
A must have thing for deep learning
There are lots of competitors with this library, but I think TensorFlow is the best thing for deep learning. Although it has a sharp learning curve, it's worth learning. It easy to deploy its model on Android. Keras is very good option too it, easy. In Keras, writing the neural network is very easy: with just a few lines of code you can write a whole neural network.
November 05, 2018
Most advanced deep learning library
I have used Keras and MATLAB along with this. Also used Caffe and pyTorch sometimes, but all of them are not as powerful as TensorFlow. Keras is in good competition with TensorFlow but Keras won't allow you a lot of customization in your algorithms. And TensorFlow gives you the power to write and configure each and every parameter of your implementation.
October 30, 2018
Best deep learning tool
September 25, 2018
My perception of the first year with TensorFlow
TensorFlow is much more complete to model
August 17, 2018
A must for deep learning
Score 8 out of 10
Vetted Review
Verified User
Tensorflow has a more broad community of support.
August 16, 2018
Best deep learning library which comes with lots of prebuilt features and visualisation tools
I have used Theano to develop machine learning models, like writing the neural network. TensorFlow has reinforcement learning support and lot more algorithms while Theano does come with lots of prebuilt tools. TensorFlow provides data visualisation tools and it is possible to implement parallelism in tensorFlow. Also, using TensorFlow, we can deploy models on multiple CPUs or GPUs.