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TensorFlow

TensorFlow

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.

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Recent Reviews

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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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TensorFlow, what else?

7 out of 10
April 09, 2021
Incentivized
Obviously, TensorFlow is a great opportunity for everyone who is interested in ML and DL area. We wanted to use TensorFlow in our company, …
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Tensor Flow Reviews

8 out of 10
December 18, 2018
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 …
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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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Product 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...
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TensorFlow Technical Details

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Reviews and Ratings

(53)

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!

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

(1-2 of 2)
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Score 9 out of 10
Vetted Review
Verified User
Incentivized
We use it for state of the art machine learning / deep learning models for real time inference in our products.
  • Model saving and prediction.
  • Training is faster.
  • It's well documented.
  • Create more high level APIs like keras.
  • Provide better support for keras.
  • Try to decrease model size.
In some cases, Java users can use it but it needs more support from Java developers because many companies are using Java in their existing products.
  • Easy to train.
  • Integration in Java.
  • It's reasonable size.
  • It allows us to do machine/deep learning in Java.
  • Well documented so easy to use.
  • Good APIs like keras are available for it.
TensorFlow has better support for Java compared to Pytorch and is also very well documented.
Score 7 out of 10
Vetted Review
Verified User
Incentivized
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.
  • Integrating with Keras.
  • Working on CPU/GPU/TPU neutrally.
  • Exporting to TFLite for browsers or edge computing.
  • The massive changes between v1 and v2 can be confusing when looking at examples online.
  • TensorFlow is losing market ground to PyTorch and JAX.
If you're doing NN training, in particular, or if you have reasons why you might need customs layers or unusual architectures, then TF is probably your best bet. TF is also basically your only bet if you're planning on using any TPU edge devices, such as the Coral.
  • Flexible differentiation setup that permits backprop through backprop.
  • Portability and platform agnosticism.
  • It's been an efficient tool for our research objectives.
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