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Discover how the Adam optimization algorithm combines momentum and RMSprop to accelerate neural network training. Learn about its implementation, hyperparameters, and widespread adoption in deep learning.
Explore how Boltzmann machines revolutionized AI by introducing stochasticity and hidden units, enabling creative data generation beyond simple pattern recall.
Explore the fundamental concepts of probability distributions and their crucial role in AI and machine learning. Learn how entropy, cross-entropy, and KL divergence shape modern algorithms.
Explore the fundamental algorithm behind machine learning systems like GPT and AlphaFold. Learn how backpropagation enables artificial neural networks to learn and why it differs from biological brains.
Mode collapse is a common issue in GAN training where the generator produces limited variety. This article explores the causes, effects and solutions for mode collapse in generative adversarial networks.
An in-depth exploration of convolutional neural networks (CNNs), covering their structure, operations, and implementation in PyTorch. Learn about convolution layers, pooling, and the feature extraction process.
An in-depth exploration of back propagation in neural networks, covering forward and backward passes, loss functions, gradient descent, and weight updates.
An in-depth exploration of variational autoencoders (VAEs), focusing on the reparameterization trick used to enable gradient computation.
An in-depth exploration of variational autoencoders (VAEs) and latent variable models, covering key concepts, mathematical foundations, and applications in generative modeling.