## Pandas plot Group By Histogram using df.hist() and Matplotlib

There are many ways of showing data with graphs, but a histogram is special. It is similar to bar graphs and a super-fast and easy way to summarize

There are many ways of showing data with graphs, but a histogram is special. It is similar to bar graphs and a super-fast and easy way to summarize

PyTorch gather function is very difficult to understand but it is pretty useful. So what is the use of the gather function? Let’s understand using

Tensors are the building blocks for PyTorch Neural networks. It takes tensors as input and produces tensors as outputs. In fact, all operations

Keras model cannot directly process raw data. The data has to be converted into a suitable format to enable the model to interpret. For example, the

Once you train the deep learning model in PyTorch, you can use it to make predictions on new data instances. In this tutorial, you will discover

How many samples do you want to load at once that is what batch size is. Let’s just look at a closer look at the batch size. It’s not just how

In the real world, often our data has imbalanced classes e.g., 99.9% of observations are of class 1, and only 0.1% are class 2. In the presence of

Once you have trained a model, you don’t want to just “hope” it generalizes to new cases. You want to evaluate it and fine-tune it if

The tensor data structure is a fundamental building block of PyTorch. Tensors are pretty much like NumPy arrays, except that, a tensor is designed to

PyTorch tensors can remember where they come from, in terms of the operations and parent tensors that originated them. They can automatically provide

The process of running a trained model on new data is called evaluation or inference in deep learning. In order to do an evaluation, we need to put

Data loading is one of the first steps in building a Deep Learning pipeline, or training a model. In this post, we will learn how to iterate the

In the previous post, we discuss the difference between ReLU, Sigmoid, and Tanh activation functions. ReLU is mainly used in hidden layers of an NN

At each step in the training loop, we evaluate our model on the samples we got from the data loader. We then compare the outputs of our model to the

Rounding error is problematic when it compounds across many operations and can cause models that work in theory but fail in practice if they are not