TensorFlow Reviews in January 2025: User Ratings, Pros & Cons

Hard to get into but worth it

PROS: One thing I appreciate about this tool is how constructing a machine learning model can be accomplished by getting rid of capabilities for low-level implementation. I can make use of simple Python scripts to integrate several model subblocks and models that already come prebuilt with the framework. I can take a TPU, CPU, or GPU and easily abstract it away, thanks to how this tool effectively handles implementation. I also appreciate how I don't have to think of convoluted algorithms ...

Ideal when you want to get into AI

PROS: What I like about this tool is that it is so easy to get into but there's always a new thing to master. You end up wanting to get more into artificial intelligence thanks to the volume of code samples that you get. CONS: The thing that I like about this tool is also the thing that may put off some people. There is a lot of documentation to go through and I can understand if somebody who isn't tech-savvy finds this overwhelming.

Makes the construction of a neural network so easy

PROS: I love how this tool can handle terabyte-sized records that probably number in the millions. If you're looking for a framework to perform machine learning and deep learning for datasets of a large volume, this is the tool you're looking for. CONS: You might need to bring in a GPU to help with processing since working with large files can make this tool run slower.

Robust tool

PROS: I appreciate how this tool works so well that it even has three different types of users that benefit from it: developers, data scientists, and researchers. Thanks to this tool, these three types can work together and work efficiently. There are similar platforms, but I think this one is the one that is the most user-friendly, especially when it comes to deploying on different platforms. As a tool, it can effectively manage images and graphs, as well as handle events. I like that it ...

This is an impressive tool

PROS: I like the Google integrations that come with this tool for it results in easy deployments. As a model backend, it works very quickly and efficiently. It doesn't take up a lot of space on your machine, and installing it and running it is a breeze. It isn't so complicated, especially when compared to other similar tools. Best of all, it is free, so you don't even have to worry much about the cost. CONS: It really requires quite a huge amount of data when training a network. That said, ...

This has got everything you need

PROS: I like that this has made performing large scale calculations easy to do, thanks to the building blocks it provides that pretty much cover everything. I can't think of any other tool you would use if you want to work with machine learning problems. CONS: My biggest gripe is that setting up this system can be a bit difficult. Other than that, there isn't anything to complain about.

The resources provided are robust and useful

PROS: I like how versatile this tool is. You can use it as a backend for Keras and similar libraries. On its own, it is pretty robust and can be used for the regression and classification of multiple neural network models like CNNs and GANs. CONS: Compared to similar tools, this one tends to slow down when handling a large volume of applications. The API can also get messy and complicated as you write more code, and that isn't something you look for in a tool like this. They could handle ...

Comes with a great resource

PROS: I like how this tool makes model prototyping quick and easy. The methods all interact with each other intuitively, which is also something that I appreciate. If you plan to use it for deep learning research and projects, you'll like its user-friendliness. CONS: While updates are frequent, it can be a little overwhelming since some users might need to relearn some areas that they're already comfortable with. I've also noticed some irritating deprecation warnings with each new update.

High level of compatibility

PROS: I appreciate how production levels can be optimized by this tool, thanks to how compatible it is with other frameworks. This tool also integrates deep learning models that are already optimized, and you can build machine learning models on top of that. CONS: If you're working with mobile applications and only have limited space, you might find it harder to deploy models and therefore get slower executions. It's also device-dependent when it comes to module division.

Exceptional tool to use for machine learning solutions

PROS: I like how this is great to use for Kaggle competitions, thanks to its deep learning framework. The Google cloud platform integration is also something I like since it allows me to take advantage of the machine learning solutions that Google has. This tool is also great to use for convolutional neural networks that you can use for computer vision applications and image recognition. CONS: The program flow isn't as dynamic as other similar tools, and that can overwhelm someone who isn't ...