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Training machine learning models

Deepnote offers similar features for machine learning engineers as Jupyter or Google Colab. With Deepnote there are also added benefits of real-time collaboration, seamless integrations, and enhanced productivity tools. Deepnote allows multiple users to work simultaneously on the same notebook, providing an ideal environment for team-based projects and collaborative research. Additionally, it supports integration with various data sources, monitoring and experimenting tools, streamlining the workflow for data scientists and machine learning practitioners.

Deepnote supports a wide range of popular machine learning frameworks, including scikit-learn, NumPy, Keras, TensorFlow, PyTorch, and ONNX. This versatility allows data scientists to choose the most suitable tools for their specific needs, whether they are working on traditional machine learning algorithms or cutting-edge deep learning models. Moreover, Deepnote allows the creation of custom environments tailored to particular project requirements. This flexibility ensures that users can integrate specialized libraries and dependencies, optimizing the development and experimentation process.

For training larger models and experimenting with various parameters, we recommend using tools like TensorBoard, Weights and Biases or Comet.ml. These tools are invaluable for monitoring and optimizing the training process. You can visualize metrics such as loss and accuracy over time, see insights into the model's performance throughout the training epochs. All three tools also allow for real-time monitoring of model performance - this can help in identifying issues like overfitting or underfitting early in the process, enabling timely adjustments.