Compare Anaconda and TensorFlow 
- Users report that TensorFlow excels in model training with a score of 9.3, highlighting its robust capabilities for deep learning applications, while Anaconda, with a score of 8.9, is noted for its ease of use in managing environments but falls slightly behind in training performance.
- Reviewers mention that TensorFlow's pre-built algorithms receive high praise with a score of 9.2, making it a preferred choice for users looking for ready-to-use solutions, whereas Anaconda's pre-built algorithms score lower at 7.9, indicating a need for more customization.
- G2 users indicate that TensorFlow's support for computer vision is particularly strong, scoring 9.2, which is beneficial for projects requiring image processing, while Anaconda's score of 8.8 suggests it is competent but not as specialized in this area.
- Users on G2 highlight that Anaconda shines in ease of setup with a score of 8.6, making it a favorable option for beginners, while TensorFlow's score of 7.6 indicates a steeper learning curve for new users.
- Reviewers say that TensorFlow's scalability is impressive, scoring 9.1, which is crucial for large-scale deployments, while Anaconda's scalability score of 8.4 suggests it may not handle extensive projects as effectively.
- Users report that both products have similar managed service scores of 8.4 and 8.6, respectively, but Anaconda's slightly higher score indicates a more user-friendly experience in managing services.
- Basic workspace for the student or practitioner
- 5GB for shareable, cloud-hosted notebooks
- Thousands of curated packages
During my final year project "Identifying and marking player during live matches" I used most of the features like Spider, Notebook, Jupiter Notebook. It's easy to implement solutions in anaconda. The latest feature that I checked recently is Anaconda is...
The thing which i dislike is anaconda platform speed, i found it sometimes very slow.
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...
During my final year project "Identifying and marking player during live matches" I used most of the features like Spider, Notebook, Jupiter Notebook. It's easy to implement solutions in anaconda. The latest feature that I checked recently is Anaconda is...
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 thing which i dislike is anaconda platform speed, i found it sometimes very slow.
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...
