Question: I'm looking for a platform that simplifies the process of building and experimenting with deep neural networks.

If you need a platform to streamline the creation and testing of deep neural networks, Neuralhub is a great option. It's an integrated environment for AI research, learning and development, designed to make AI more accessible. With Neuralhub, you can create networks from scratch or compose them from prebuilt modules, visually edit network elements, train models securely and check model performance metrics. It's a collaborative environment for AI enthusiasts, researchers and engineers to help you get past the challenges of keeping up with AI research and development.

Another option is MLflow, an open-source MLOps platform that makes it easier to develop and deploy machine learning and generative AI projects. It's a single environment for managing all aspects of ML projects, including experiment tracking, logging and model management. MLflow supports popular deep learning frameworks like PyTorch and TensorFlow and offers a wealth of learning resources. It's a good option for machine learning practitioners and teams that want to improve collaboration, reproducibility and productivity in their ML workflows.

Last, Dataloop is an integrated environment for data curation, model management, pipeline orchestration and human feedback to accelerate AI application development. It includes data management for unstructured data, automated preprocessing and embeddings for similarity detection. Dataloop also includes model management, pipeline orchestration and a function-as-a-service option for custom functionality. It's designed to improve collaboration and speed up development, so it's a good option for a variety of roles within an organization.