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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Learn how to build artificial neural networks from scratch, focusing on multi-level feedforward networks like multi-level perceptrons. Discover how neural networks function, including training large networks in parallel and distributed systems, and grasp concepts such as learning non-linear function

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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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Unveil the limitations of linear and classic non-linear models in machine learning, showcasing the emergence of neural networks like Multi-layer Perceptrons (MLPs) as powerful tools to tackle non-linear functions and decision boundaries efficiently. Discover the essence of neural networks and their

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This content explores the concept of advanced classifiers like Neural Networks which compose complex relationships through combining perceptrons. It delves into the workings of the classic perceptron and how modern neural networks use more complex decision functions. The visuals provided offer a cle

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Explore the world of neural network learning, including topics like support vector machines, unsupervised learning, and the use of feed-forward perceptrons. Dive into the concepts of gradient descent and how it helps in minimizing errors in neural networks. Visualize the process through graphical ex

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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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Kernels and Perceptrons are fundamental concepts in machine learning. This overview covers the Perceptron algorithm, Kernel Perceptron, and Common Kernels, along with Duality and Computational properties. It also explores mapping to Hilbert space and the computational approaches for achieving desire

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