Compare MATLAB and TensorFlow 
- Users report that TensorFlow excels in Model Training with a score of 9.3, highlighting its efficiency in handling large datasets and complex models, while MATLAB's score of 8.6 indicates it may not be as robust in this area.
- Reviewers mention that MATLAB shines in Ease of Use with a score of 8.3, making it more accessible for beginners, whereas TensorFlow's score of 7.6 suggests a steeper learning curve, particularly for those new to programming.
- G2 users note that TensorFlow offers superior Pre-Built Algorithms with a score of 9.2, which allows for quicker implementation of machine learning solutions, compared to MATLAB's score of 8.8, which may require more customization.
- Users on G2 highlight that MATLAB's Drag and Drop functionality scores 8.7, making it easier for users to build models visually, while TensorFlow's score of 6.6 indicates a more code-centric approach that may not appeal to all users.
- Reviewers say that TensorFlow's Computer Vision capabilities are rated at 9.2, showcasing its strength in image processing tasks, while MATLAB's score of 8.8 suggests it may not be as powerful in this specific application.
- Users report that both platforms have similar scores in Application Deployment at 8.6, indicating that both TensorFlow and MATLAB provide solid options for deploying models, but TensorFlow's scalability score of 9.1 suggests it may handle larger applications more effectively.
Matlab is very essential scientific environment which is very useful for students and working people.Especially to write the fuzzy logic's and Neural Network logic. It intuitive way of programming which made easy to use all of its futures and variable...
The graph editing functions are ridiculously hard to use and slow. It is also very difficult to understand what the errors mean, because they don't apply to your specific project or give any useful documentation. Also, there have been many time where errors...
Easy to get started with. The TensorFlow ecosystem provides support tools to load data efficiently (TF Dataloaders) , build models (Keras), Optimize it (TF Lite), and Deploy and monitor (TFX) and it is production-ready.
The documentation is not very good. The API is too messed up - there are several functions that do the same thing with minor differences and little documentation about the differences. Boilerplate code is also usually long. The API is cumbersome to use...
Matlab is very essential scientific environment which is very useful for students and working people.Especially to write the fuzzy logic's and Neural Network logic. It intuitive way of programming which made easy to use all of its futures and variable...
Easy to get started with. The TensorFlow ecosystem provides support tools to load data efficiently (TF Dataloaders) , build models (Keras), Optimize it (TF Lite), and Deploy and monitor (TFX) and it is production-ready.
The graph editing functions are ridiculously hard to use and slow. It is also very difficult to understand what the errors mean, because they don't apply to your specific project or give any useful documentation. Also, there have been many time where errors...
The documentation is not very good. The API is too messed up - there are several functions that do the same thing with minor differences and little documentation about the differences. Boilerplate code is also usually long. The API is cumbersome to use...
