Large language models (LLMs) are recent advances in deep learning models to work on human languages. Some great use case of LLMs has been demonstrated. A large language model is a trained deep-learning model that understands and generates text in a human-like fashion. Behind the scene, it is a large transformer model that does all […]

## Activation Functions in PyTorch

As neural networks become increasingly popular in the field of machine learning, it is important to understand the role that activation functions play in their implementation. In this article, you’ll explore the concept of activation functions that are applied to the output of each neuron in a neural network to introduce non-linearity into the model. […]

## PyTorch Tutorial: How to Develop Deep Learning Models with Python

Predictive modeling with deep learning is a skill that modern developers need to know. PyTorch is the premier open-source deep learning framework developed and maintained by Facebook. At its core, PyTorch is a mathematical library that allows you to perform efficient computation and automatic differentiation on graph-based models. Achieving this directly is challenging, although thankfully, […]

## Deep Learning with PyTorch (9-Day Mini-Course)

Deep learning is a fascinating field of study and the techniques are achieving world class results in a range of challenging machine learning problems. It can be hard to get started in deep learning. Which library should you use and which techniques should you focus on? In this 9-part crash course you will discover applied […]

## Building a Logistic Regression Classifier in PyTorch

Logistic regression is a type of regression that predicts the probability of an event. It is used for classification problems and has many applications in the fields of machine learning, artificial intelligence, and data mining. The formula of logistic regression is to apply a sigmoid function to the output of a linear function. This article […]

## Training Logistic Regression with Cross-Entropy Loss in PyTorch

In the previous session of our PyTorch series, we demonstrated how badly initialized weights can impact the accuracy of a classification model when mean square error (MSE) loss is used. We noticed that the model didn’t converge during training and its accuracy was also significantly reduced. In the following, you will see what happens if […]

## Building an Image Classifier with a Single-Layer Neural Network in PyTorch

A single-layer neural network, also known as a single-layer perceptron, is the simplest type of neural network. It consists of only one layer of neurons, which are connected to the input layer and the output layer. In case of an image classifier, the input layer would be an image and the output layer would be […]

## Text Generation with LSTM in PyTorch

Recurrent neural network can be used for time series prediction. In which, a regression neural network is created. It can also be used as generative model, which usually is a classification neural network model. A generative model is to learn certain pattern from data, such that when it is presented with some prompt, it can […]

## LSTM for Time Series Prediction in PyTorch

Long Short-Term Memory (LSTM) is a structure that can be used in neural network. It is a type of recurrent neural network (RNN) that expects the input in the form of a sequence of features. It is useful for data such as time series or string of text. In this post, you will learn about […]

## Handwritten Digit Recognition with LeNet5 Model in PyTorch

A popular demonstration of the capability of deep learning techniques is object recognition in image data. The “hello world” of object recognition for machine learning and deep learning is the MNIST dataset for handwritten digit recognition. In this post, you will discover how to develop a deep learning model to achieve near state-of-the-art performance on […]