Automated Machine Learning, or AutoML, is a technology that simplifies the process of building machine learning models by automating various steps, from data preprocessing to model selection and tuning.

Using custom images with a pre-trained model like ResNet-18 is common in transfer learning to adapt the model to a specific task or dataset.

Generative Adversarial Networks (GANs) are a type of deep learning model consisting of two neural networks—a generator and a discriminator—that compete against each other to produce high-quality, realistic data.

This article will guide you through building a DQN AI agent that learns to navigate a simple 5×5 grid, avoiding obstacles and reaching a goal.

In this article, we will walk through the process of setting up and interacting with the CartPole environment using OpenAI’s Gym library.

Let’s dive into the key concepts, explore algorithms, and get a feel of how agents learn through interaction and feedback.

Recurrent Neural Networks (RNNs) are a type of artificial neural network designed to handle sequential data by maintaining a form of memory. They excel in tasks where context over time is critical, such as speech recognition and natural language processing.

In this article, we will walk through the development of a sentiment analysis system using Python, focusing on movie reviews as the core data source.

Convolutional Neural Networks (CNNs) are a class of deep learning algorithms primarily used for processing structured grid data such as images.

In this tutorial, we will walk through the steps to create a simple web application for converting text to speech (TTS) using Python’s Flask framework and a deep learning-based TTS model.

We will explore how to create a basic command-based automation system using Python, which listens for user input, processes the input with fuzzy matching to determine the closest matching command, and triggers actions based on that input.

In this project, you’ll learn how to build a simple neural network using TensorFlow to classify handwritten digits from the MNIST dataset.

Sentiment analysis has become an essential tool for businesses to understand customer opinions and feedback. In this article, we’ll explore a simple implementation of sentiment analysis using logistic regression.

In this project, we aim to build a robust machine learning model to predict house prices in California using the California Housing dataset.