Reinforcement Learning and Deep RL Python Theory and Projects - DNN Implementation in PyTorch

The video tutorial covers the use of PyTorch for implementing deep neural networks. It begins with setting up the necessary resources and imports, followed by data preparation using tensors and data loaders. The tutorial then defines a sequential model with multiple layers and activation functions. It explains the setup of an optimizer and loss function, and demonstrates a training loop for batch processing. Finally, it shows how to make predictions and concludes with tips for working with large datasets.

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