For Machine Learning

In neural network terminology, an epoch is one pass through all source data. The DataLoader class is designed to be iterated using the enumerate() function, which returns a tuple with the current batch zero-based index value.

In this tutorial, we will look even further into what is derivative, how to calculate the gradient and explore how a neural network’s actual forward and backward passes are done.

Keras EarlyStopping callback interrupts training once a target metric has stopped improving for a fixed number of epochs. For instance, this callback allows you to interrupt training as soon as you start overfitting, thus avoiding having to retrain your model for a smaller number of epochs. 

Reason to register the tensor as a buffer is to be able to serialize the model and restore all internal states and buffers and parameters will be pushed to the device if called on the parent model.

We need a way to get access to the activations inside the mode. In PyTorch, this can be done with a hook. Hooks are PyTorch callbacks.

A Sequential model is useful for quickly defining a linear stack of layers (i.e., where one layer follows directly from the previous layer without any branching). We can define our MLP model using the Sequential class as shown:

tqdm can help you create progress bars for training machine learning models. This tutorial will teach you how to implement Progress Bars during training in keras.

If you’re using Intel Core then download x64 DMG Installer. If you’re using Apple Silicon M1/M2 Chip then you have to Download ARM64 DMG Installer. I have an Apple M2 Chip so I am going to download Arm 64 DMG Installer.

The shape mismatch is raised in the first linear layer of self.out as it’s expecting an input activation with 1050 features while the actual activation has 1568 features. Change the in_features of this linear layer to 1568 and it should work.

The convolution layer uses filters that perform convolution operations as it is scanning the input with respect to its dimensions. The resulting output is called a feature map or activation map.