Backpropagation - PowerPoint PPT Presentation

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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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 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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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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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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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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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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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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Dive into the world of deep learning and neural networks with "Tricks of the Trade II" from Spring 2015. Explore topics such as perceptron, linear regression, logistic regression, softmax networks, backpropagation, loss functions, hidden units, and autoencoders. Discover the secrets behind training

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Bayesian belief networks, also known as Bayesian networks, are graphical models that allow class conditional independencies between subsets of variables. These networks represent dependencies among variables and provide a specification of joint probability distribution. Learn about classification me

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The article discusses neural networks for spatial resolution optimization in detectors, covering the basics of artificial neural networks and training methods like backpropagation. Neural networks have wide applications beyond signal processing, enabling precise function approximation and simulation

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Discover the power of Convolutional Neural Networks (CNN) for image classification. Learn about convolutions, pooling, and training a CNN using techniques like backpropagation and dropout. Explore popular libraries like Keras, Pytorch, and TensorFlow for efficient CNN training on GPUs.

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Dive into the world of Convolutional Neural Networks (CNNs) for image classification. Explore the concepts of convolutions, pooling, and CNN training techniques like backpropagation, dropout, and stochastic gradient descent. Learn how to optimize parameters and train CNNs efficiently using GPUs and

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Learn about computing gradients efficiently through backpropagation and understanding computational graphs. Dive into examples, chain rule reviews, and computation scenarios.

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Delve into the world of deep learning and Boltzmann machines, exploring the history, challenges, and solutions in training deep neural networks. Learn about the vanishing gradient problem, backpropagation algorithm, and the role of Restricted Boltzmann Machines in overcoming training obstacles. Disc

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Explore learning methods in neuromorphic systems, including supervised and unsupervised techniques like backpropagation and STDP learning. Learn about the challenges and techniques involved in converting artificial neural networks (ANN) to spiking neural networks (SNN) for improved performance.

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Explore the Universal Approximation Theorem, which states that a feedforward single hidden layer network can approximate continuous functions under mild assumptions on the activation function. Learn about practical and theoretical uses, including replicating black box functions, visualization tools,

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Learn about backpropagation (BP) in adaptive networks, a systematic way to compute gradients, reinvented in 1986 for multilayer perceptrons (MLP), and its applications in two-layer and three-layer adaptive networks.

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Explore examples and computations in neural networks, covering architectures, activations, backpropagation, and training. Dive into weight updates, errors, and expectations for final weights, providing insights into deep learning concepts.

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