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Big data for training models in the cloud

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Big data for training models in the cloud When you have a lot of data like you can't run training on your local machine, or the size of that data is bigger than your hard drive, it's time to look at other options. To the cloud One concrete option is to transfer machine learning training to another computer to access additional storage, thus freeing up your hard drive space and allowing you to work on other things while the pieces of training are taking place. Let's break down some of the parts that need to be moved to the cloud. It is useful to think of our training as a need for two primary resources: calculation and storage. The interesting thing here is that we don't have to tie them so tightly as you expected before. We can decode them, which means we can take advantage of special systems for both of us. This can affect data efficiency when dealing with big data. Compute loads are easily moved together, but moving large datasets can be a bit more engaging. However,

Visualizing your model using TensorBoard

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Visualizing your model using TensorBoard Debugging problems are much easier when you can see what the problem is. But in a complex model with pieces of fidget data ted, that can get ... complex. Thanks, Tensorboard makes it a lot easier. Unlike traditional programming, machine learning is often very unpredictable. The quality of your data, including the nuances of our model, will have many parameters to choose from, all of which have detailed implications for the success or failure of the training process. If only there was some way to track some of these metrics through the training process, and also look at the structure of the model we created, which enables us to tune and debug the model. Now, this abstract process may be hard to imagine, but fortunately, TensorFlow has a built-in solution! Visit Tensorboard, TensorFlow's built-in visualizer, which enables you to perform detailed tasks to see training progress from looking at your model structure. Tensorflow uses the idea of ​​