I've had issues with stability when I use a lot of data and try out different combinations of modeling techniques. I would also like to see some improvement in parallel processing. We can take advantage of the GPU and compute it.
On the production side of things, having more production tooling frameworks would be helpful. TensorFlow has a lot of elaborate frameworks e.g. for serving models, and that's one area where PyTorch could improve.
We've built this course as an introduction to deep learning. Deep learning is a field of machine learning utilizing massive neural networks, massive datasets, and accelerated computing on GPUs. Many of the advancements we've seen in AI recently are due to the power of deep learning. This revolution is impacting a wide range of industries already with applications such as personal voice assistants, medical imaging, automated vehicles, video game AI, and more.
In this course, we'll be...
I've had issues with stability when I use a lot of data and try out different combinations of modeling techniques. I would also like to see some improvement in parallel processing. We can take advantage of the GPU and compute it.
On the production side of things, having more production tooling frameworks would be helpful. TensorFlow has a lot of elaborate frameworks e.g. for serving models, and that's one area where PyTorch could improve.
The training of the models could be faster. However, with PyTorch, modern training becomes a bit slower because it is within the models at Python.
None come to mind.
There is not enough documentation about some methods and parameters. It is sometimes difficult to find information.