Artificial intelligence deep learning and neural networks part 3 (4k)

In this video, we explore two powerful neural network paradigms used in modern machine learning: Restricted Boltzmann Machines (RBMs) and Recurrent Neural Networks (RNNs). We begin by explaining how RBMs originated from Hopfield networks and how they differ from traditional feedforward models through their undirected, probabilistic structure. You’ll learn how RBMs model joint probability distributions, generate latent representations, and are commonly used for unsupervised pretraining, classification, and collaborative filtering.

We then move to Recurrent Neural Networks, which are specifically designed to handle sequential and temporal data. The video covers key concepts such as hidden states, truncated backpropagation through time, and practical challenges like vanishing and exploding gradients. Advanced architectures including LSTM, bidirectional RNNs, and echo-state networks are discussed, along with their real-world applications in text processing, speech recognition, machine translation, image captioning, and computational biology.

This video is ideal for students, researchers, and practitioners looking to strengthen their understanding of probabilistic models, sequence learning, and deep neural network architectures
#MachineLearning #DeepLearning #NeuralNetworks #RBM #RestrictedBoltzmannMachine #HopfieldNetwork #ProbabilisticModels #UnsupervisedLearning #RNN #RecurrentNeuralNetworks #LSTM #SequenceModeling #AI #ArtificialIntelligence #DataScience #SpeechRecognition #NaturalLanguageProcessing #MachineTranslation #ComputerVision