TensorFlow Reviews & Product Details

What do you like best about TensorFlow?

I Like There Are wide range of features, and good community support and on stackoverflow support by dev also compatibility with both research and production environments make TensorFlow Extra Ordinary In My Opinions , Its is for both beginners and advanced users is a huge plus. most of CS student are used in their daily projects and easy to use by student and professional and easy to integration using python rich support and easy to implement in python files. Review collected by and hosted on G2.com.

What do you dislike about TensorFlow?

It's hard for new users to learn at beginer stage and the instructions sets , even though there are a lot of thing to learn as like probability and statistic concepts to use efficient ,it can feel like too much. Fixing problems and debug can also be tough to devs because the error messages are hard to understand and interpret but chat gpt can solve lot of thing for dev Review collected by and hosted on G2.com.

What do you like best about TensorFlow?

I love how powerful and flexible TensorFlow is for building and training deep learning models. Keras makes it a bit easier and pre-trained models save a lot of time. Plus the community is great when I get stuck. Review collected by and hosted on G2.com.

What do you dislike about TensorFlow?

The learning curve is steep. Especially for beginners. Sometimes the error messages are too complicated to understand and debugging is frustrating. Also it requires a lot of computing power which can be a problem if you don’t have high end hardware. Review collected by and hosted on G2.com.

What do you like best about TensorFlow?

My favorite thing about TensorFlow is its scalability and adaptability. Developers can use it to develop and train machine learning models in a very efficient way, either for small applications or big ones. The presence of pre-trained models and an enormous community also enable easy starting point and solution of problems. Further, the capability of TensorFlow to support several programming languages such as Python also brings it closer to a broader array of users. Review collected by and hosted on G2.com.

What do you dislike about TensorFlow?

The steep learning curve is one of the main issues I have with TensorFlow. It can be very intimidating for newcomers to understand its structure and features, especially when contrasted with simpler machine learning libraries. Because some of the error messages aren't very clear, debugging can also be a bit of a pain. A lighter library might be more effective for smaller projects, even though TensorFlow has a lot of power. Review collected by and hosted on G2.com.

What do you like best about TensorFlow?

What I like best about Tensorflow is its flexibility and power. It's like a swiss army knife for machine learning and deep learning. You can build anything from simple models to complex neural networds for computer vision, NLP and more. The pre built models and tools for transfer learning make it easier to get started, and the support for deployment across platforms, mobile, web and cloud is super convenient.

Additionaly the community is massive. So many tutorials, open source project and helpful forums, you will never feel stuck. Once you get the hang of it the possibilites are endless. Review collected by and hosted on G2.com.

What do you dislike about TensorFlow?

The learning curve, it can feel pretty overwhelming at first, especially for beginners. The syntax can get comples, and debugging isnt always straightforward.

Another thing is it can be heave and a bit slow compared to some other frameworks, especially when you are just experimenting or working on smaller projects. Setting up the enviornment is also a hassle, plus you need to carful with versions also. Review collected by and hosted on G2.com.

What do you like best about TensorFlow?

Graphs of tensorflow:TensorFlow has better computational graph visualizations. Which are better when compared to other libraries like PyTorch and Theano.

Scalability : It can run on CPUs, GPUs for high-performance computations.

Community :Tesnorflow has a very good community support ,so when we are stuck in a problem we can always ask our questions to the community for solutions which we get most of the time.

Easy to start : Also the extensive documentation of tensorflow helps to start up with it ease for an active development.Also because of python it is easy to implement. Review collected by and hosted on G2.com.

What do you dislike about TensorFlow?

Slow : TensorFlow lacks in both speed and usage when it is compared to its competitors.

Limited GPU Support:Only NVIDIA and Python are supported by TensorFlow for GPU programming. It has no additional support of ther GPUs Review collected by and hosted on G2.com.

What do you like best about TensorFlow?

It is highly scalable, making it a great choice for both small-scale projects and large distributed systems.It offers TensorFlow Lite (for mobile & edge devices), TensorFlow.js (for web applications), and TensorFlow Extended (TFX) (for production-level deployment) that means a complete suite of requirement can be fulfilled by it.A rich ecosystem of developers and customer support is great. Review collected by and hosted on G2.com.

What do you dislike about TensorFlow?

Debugging issues, understanding execution flow, and handling tensors can be quite problematic for beginners. and Compatibility Issues & Frequent API Changes is the worst. Review collected by and hosted on G2.com.

What do you like best about TensorFlow?

What I like most about TensorFlow is its versatility—whether for deep learning, computer vision, or natural language processing, it offers a comprehensive set of tools that make model development scalable and efficient. Review collected by and hosted on G2.com.

What do you dislike about TensorFlow?

One downside is its steep learning curve. While powerful, TensorFlow can be complex for beginners, and some functions require extensive documentation review or prior experience with machine learning frameworks. Review collected by and hosted on G2.com.

What do you like best about TensorFlow?

I love TensorFlow as a data scientist I use it on daily basis. I have used it to train and tweak models. I create Production grade ML pipelines. I can get low level control support, I can integrate custom loss functions. There are thousands of examples with which we can use TensorFlow easily with new models. Review collected by and hosted on G2.com.

What do you dislike about TensorFlow?

For small datasets , it is kind of a heavy library, so should not be used here. For learning and quick deployment work as well I would prefer keras. Review collected by and hosted on G2.com.

What do you like best about TensorFlow?

I liked using TensorFlow due to its end-to-end interface. The data model building using Keras to powerful visualization supported the machine learning pipeline throughout my projects. TensorFlow heads built in tools for optimisation which was a huge plus and saves a lot of time. Review collected by and hosted on G2.com.

What do you dislike about TensorFlow?

I have felt issues with it sometimes because for embedded applications it can be quite heavy and complicated especially while converting some models to lite version with unsupported operations. Resolving or debugging such issues often need deep research or asking forums and trial and error methods. Review collected by and hosted on G2.com.

What do you like best about TensorFlow?

Best thing about TensorFlow is that it supports both deep learning and traditional ML models, with deployment on multiple platforms like CPUs, GPUs etc. We have used this package in our python framework to work on phone call sentiment analysis project by leveraging deep learning models like LSTMs and CNNs . It helps us in text preprocessing, word embeddings, and training neural networks for sentiment classification. It is easy to integrate with Google cloud AI. Review collected by and hosted on G2.com.

What do you dislike about TensorFlow?

It has very steep learning curve because of complex APIs available and its computational graphs make debugging less intuitive. Hence, we are leveraging pytorch as well for our sentiment analysis project because of pyhtonic syntax and less programming involved. Review collected by and hosted on G2.com.