Compare Keras and Knet 
- Users report that Keras has a more user-friendly interface, making it easier for beginners to get started with deep learning projects compared to Knet, which some users find less intuitive.
- Reviewers mention that Knet excels in model optimization, with features that allow for fine-tuning of hyperparameters, while Keras users have noted that its automated model tuning capabilities are not as robust.
- G2 users highlight Keras's strong documentation and support, which is particularly beneficial for small businesses, whereas Knet users have expressed that they sometimes struggle with the quality of support available.
- Users on G2 indicate that Keras offers better scalability options, making it a preferred choice for small businesses looking to grow, while Knet is favored by enterprises for its performance in handling large datasets.
- Reviewers say that Keras provides superior visualization tools, which help in understanding model performance, while Knet's visualization features are considered basic and less effective.
- Users report that Knet has a strong focus on advanced features like transfer learning, which is highly valued in enterprise settings, while Keras is often seen as more accessible for general deep learning tasks.
Getting results with neural networks very wuickly
There is minimum configurability and things that are essential and easily accomplished in other frameworks takes a while. For example, building a layer that is not in one of the predefined templates or checking the weights on each layers.
The tracking of the course is easy and great learning opportunities from a wide variety of courses. Also there are various ways through which we can learn about anything/
One module is not working even after switching browsers. CC is too small to read on a laptop or when casting to large screen TV. Resize CC
Getting results with neural networks very wuickly
The tracking of the course is easy and great learning opportunities from a wide variety of courses. Also there are various ways through which we can learn about anything/
There is minimum configurability and things that are essential and easily accomplished in other frameworks takes a while. For example, building a layer that is not in one of the predefined templates or checking the weights on each layers.
One module is not working even after switching browsers. CC is too small to read on a laptop or when casting to large screen TV. Resize CC
