This notebook takes you through the implementation of binary image classification with CNNs using the hot-dog/not-dog dataset on PyTorch. — Import Libraries import numpy as np import pandas as pd import seaborn as sns from tqdm.notebook import tqdm import matplotlib.pyplot as plt import torch import torchvision import torch.nn as nn import torch.optim as optim import torch.nn.functional as F from torchvision import transforms, utils, datasets from torch.utils.data import Dataset, DataLoader, SubsetRandomSampler

This notebook takes you through an implementation of random_split, SubsetRandomSampler, and WeightedRandomSampler on Natural Images data using PyTorch. Import Libraries import numpy as np import pandas as pd import seaborn as sns from tqdm.notebook import tqdm import matplotlib.pyplot as plt import torch import torchvision import torch.nn as nn import torch.optim as optim import torch.nn.functional as F from…

This blog post takes you through an implementation of regression on tabular data using PyTorch. — We will use the red wine quality dataset available on Kaggle. This dataset has 12 columns where the first 11 are the features and the last column is the target column. The data set has 1599 rows. Import Libraries We’re using tqdm to enable progress bars for training and testing loops. import…

This blog post takes you through an implementation of multi-class classification on tabular data using PyTorch. — We will use the wine dataset available on Kaggle. This dataset has 12 columns where the first 11 are the features and the last column is the target column. The data set has 1599 rows. Import Libraries We’re using tqdm to enable progress bars for training and testing loops. import numpy as…

This blog post takes you through an implementation of binary classification on tabular data using PyTorch. — We will use the lower back pain symptoms dataset available on Kaggle. This dataset has 13 columns where the first 12 are the features and the last column is the target column. The data set has 300 rows.

This blog post takes you through the different types of RNN operations in PyTorch. — This blog post takes you through the implementation of Vanilla RNNs, Stacked RNNs, Bidirectional RNNs, and Stacked Bidirectional RNNs in PyTorch by predicting a sequence of numbers.

This blog post takes you through Dataloaders and different types of Loss Functions in PyTorch. — In this blog post, we will see a short implementation of custom dataset and dataloader as well as see some of the common loss functions in action. Datasets and Dataloaders

This blog post takes you through the different types of CNN operations in PyTorch. — In this blog post, we will implement 1D and 2D convolutions using torch.nn. What is a CNN? A Convolutional Neural Network is type of neural network that is used mainly in image processing applications. Other applications of CNNs are in sequential data such as audio, time series, and NLP. Convolution is one of the…

This blog post takes you through a few of the most commonly used tensor operations and demonstrates the Autograd functionality in PyTorch. — With all the hype around PyTorch, I decided to take the plunge and learn it towards the end of 2019. Getting started was kind of tough because there are not a lot of tutorials which (in my opinion) are good for beginners. …