Introduction to Deep Learning: Neural Networks and Multilayer Perceptrons
Explore the fundamentals of neural networks, including artificial neurons and activation functions, in the context of deep learning. Learn about multilayer perceptrons and their role in forming decision regions for classification tasks. Understand forward propagation and backpropagation as essential
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Understanding Multi-Layer Perceptrons in Neural Networks
In this lecture by Dr. Erwin Sitompul at President University, the focus is on Multi-Layer Perceptrons (MLP) in neural networks, discussing their architecture, design considerations, advantages, learning algorithms, and training process. MLPs with hidden layers and sigmoid activation functions enabl
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The Ups and Downs of Backpropagation in Neural Networks
The history and failure of backpropagation in the 1990s are discussed, highlighting its potential and limitations. The challenges faced due to slow computers, small datasets, and network architecture are explained. The comparison with Support Vector Machines for Artificial Intelligence tasks is expl
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Understanding Principal Components Analysis (PCA) and Autoencoders in Neural Networks
Principal Components Analysis (PCA) is a technique that extracts important features from high-dimensional data by finding orthogonal directions of maximum variance. It aims to represent data in a lower-dimensional subspace while minimizing reconstruction error. Autoencoders, on the other hand, are n
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Exploring Monte Carlo Tree Search (MCTS) Algorithm in Online Planning
Monte Carlo Tree Search (MCTS) is an intelligent tree search algorithm that balances exploration and exploitation by using random sampling through simulations. It is widely used in AI applications such as games (e.g., AlphaGo), scheduling, planning, and optimization. This algorithm involves steps li
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Enhancing Sea Surface Temperature Data Using Hadoop-Based Neural Networks
Large-scale sea surface temperature (SST) data are crucial for analyzing vast amounts of information, but face challenges such as data scale, system load, and noise. A Hadoop-based Backpropagation Neural Network framework processes SST data efficiently using a Backpropagation algorithm. The system p
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Understanding Recurrent Neural Networks: Fundamentals and Applications
Explore the realm of Recurrent Neural Networks (RNNs), including Long Short-Term Memory (LSTM) models and sequence-to-sequence architectures. Delve into backpropagation through time, vanishing/exploding gradients, and the importance of modeling sequences for various applications. Discover why RNNs o
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Leveraging Graphics Processors for Accelerating Sonar Imaging via Backpropagation
Utilizing graphics processors to enhance synthetic aperture sonar imaging through backpropagation is a key focus in high-performance embedded computing workshops. The backpropagation process involves transmitting sonar pulses, capturing returns, and reconstructing images based on recorded samples. T
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