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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

TrustRadius Insights

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

(1-1 of 1)
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Anupam Mittal | TrustRadius Reviewer
Score 8 out of 10
Vetted Review
Verified User
Incentivized
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.
  • Support for many libraries and programming languages.
  • Ability to use GPU and TPU - hence faster execution.
  • Low effort in getting started in development, hence ease of learning.
  • Graphic interface to create layers can help beginners.
  • Detailed tutorials on what goes behind the scenes in each layer. Currently, the tutorials don't focus on that.
  • Better support to integrate with files on the cloud.
Best suited for deployment on the cloud with the subscription-based model for execution infrastructure. For startups or for companies that do not have a strong data science staff, learning Tensorflow is easy because of the libraries and online tutorials availability.

It can be avoided when your development stack is Microsoft, as using Azure may provide better integration. Also, if the work requires detailed customization of the algorithm, it may be easier to work directly with Python code and TensorFlow may not help.
  • Ability to make better predictions.
  • Increase in profit from equity investments on a consistent basis.
  • Move towards digital transformation in the company and a better brand name.
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.
3
Currently, we use Tensorflow to develop algorithm-based trading models. These are time series based predictive models using NSE data publicly available. It is being used by the investment modeling department of our business.
3
Analytical thinking is a must. A good understanding of statistics, probability, and matrices. Logical thinking, project experience in at least one of the machine learning platforms/languages like Python, R, Azure, will help the use of TensorFlow.
  • Predictive Analytics - algorithm based trading
Yes
We were using R machine learning with Shinyapps. TensorFlow was easier to implement with better support for online/cloud hosting.
  • Product Features
  • Product Usability
  • Analyst Reports
  • Third-party Reviews
Product features: Tensor flow comes with the support of built-in algorithms that are easy to implement.
We would now consider a lot more tools that have been released.
  • Implemented in-house
Yes
We started with smaller problem statements and took them to completion. Then added more features.
Change management was minimal
Use of cloud for better execution power is recommended.
No
not needed so far.
No
At times when we got stuck with some code, use of open source forums was the way to go for problem resolution. We found support from the community forum members.
No support is taken from Google as such.
  • Adding of new neural network layers in the code.
  • Running the model. Especially in the newer versions where a number of epochs and other execution parameters are easy to use.
  • support for Keras, Numpy, Pandas and other libraries.
  • Graphical front-end to develop code.
Support of multiple components and ease of development.
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