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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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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.
- Data pipeline implementation is quite good, loading large amounts of data and pre-process it in an efficient way is no more issue for us
- It supports all major DL algorithms and network layouts such as ConvNets, RNN, LSTMs, Word2Vec, and even the latest transformer architecture
- The abstraction for the device is perfectly done and its support seamlessly for multiple GPU and even TPU will bring a lot of performance gain for enterprise scoped solution while still keep the flexibility
- The TensorBoard is amazing. I haven't seen a similar thing in other frameworks on the market. It allows us to quickly understand and debug the model with the info visualization which makes understanding much better
- A very supportive community, which is the key for sharing the ideas and find the quick and best solutions
- TensorFlow has its own model and terminology, which is not quite the same as the normal Python styled other frameworks, so in order to master it, the learning curve is a little bit steep, and as a by-product of the fast iteration and release, sometimes the documentation is not quite catching up
- TensoFlow is based on Design Model Then Run Model concept, which means the model itself is static. Maybe it could also borrow some ideas from PyTorch, which is more intuitive and supports dynamic model building
- I only can nominate the positive impact-- it is open source so there's no financial cost, with full functions and features. What it brings to us is more objective, reliable patterns learned from the data, without having to spend a long time and rely on a lot of domain specialists' limited knowledge, and its output is even better than human (sometimes too subjective) decision
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
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.
- 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.
- 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
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.
- 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.
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.
- 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.
- 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.
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
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.
- 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.
- 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.
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.