TensorFlow Reviews & Product Details

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.

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.

That it’s versatile and helps in various ml related task Review collected by and hosted on G2.com.

It’s not always compatible with the hardware or other libraries used. Sometimes it isn’t compatibility with lambda versions too Review collected by and hosted on G2.com.

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 requirements can be fulfilled by it. A rich ecosystem of developers and customer support is great. Review collected by and hosted on G2.com.

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

I have used this to develop face detection applications. Review collected by and hosted on G2.com.

At first, I found it quite difficult because the information was not readily accessible to me. Review collected by and hosted on G2.com.

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.

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.

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.

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.

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.

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.

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.

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.

I have used the library for my machine learning applications and it helped to speed up my programming tasks Review collected by and hosted on G2.com.

it doesn't have a proper reinforcement learning library Review collected by and hosted on G2.com.

extensive ecosystem of integrations specially with technologies like Keras and Numpy that help immensely in model development. Easy deployment and model training is also a good to have feature for various platforms. Review collected by and hosted on G2.com.

steeper learning curve for a beginner like me, not well documented (you will find the majority of stuff needed in docs but not those intricacies that some integrations required), for that I had to refer to stack overflow. Review collected by and hosted on G2.com.