13 docs tagged with "Deep Learning"

In this post, we will explore Autoencoders, a type of artificial neural network used for unsupervised learning that focuses on efficiently encoding input data and reconstructing it.

Convolutional Neural Networks (CNNs) are specialized deep learning architectures that are particularly effective for image processing tasks due to their ability to capture spatial hierarchies in data.

Computer Vision (CV) enables machines to interpret and understand visual data from the world. It is widely applied in tasks such as object detection, image classification, and facial recognition.

This post explores Convolutional Neural Networks (CNN), a specialized neural network architecture widely used for tasks involving image processing and computer vision.

An introduction to Deep Q-Networks, a reinforcement learning technique that combines Q-Learning with deep neural networks to handle complex, high-dimensional state spaces.

In this post, we'll explore Generative Adversarial Networks (GAN), a powerful class of neural networks used for generating new data based on learned distributions.

Generative Adversarial Networks (GANs) are a deep learning model that generate new data samples by training two neural networks in opposition. They are widely used in tasks such as image generation, style transfer, and data augmentation.

This post delves into Long Short-Term Memory (LSTM), a type of recurrent neural network designed to overcome the vanishing gradient problem, enabling better learning of long-term dependencies in sequential data.

Build and visualize neural networks with support for feedforward, convolutional, and recurrent architectures. Explore how these models learn from data using backpropagation and gradient descent.

Recurrent Neural Networks (RNNs) are a type of neural network designed to recognize patterns in sequences of data, including time-series data, language processing, and other sequence-related tasks.

This post explores Recurrent Neural Networks (RNN), a class of neural networks designed to handle sequential data and time-series information, commonly used for tasks involving natural language processing, speech recognition, and more.

Stochastic Gradient Descent (SGD) is an optimization algorithm used to minimize the loss function in machine learning and deep learning models.

YOLO (You Only Look Once) is a popular deep learning algorithm for real-time object detection that divides images into grid cells and predicts bounding boxes and class probabilities for each cell.