#Csce 526 Intelligent Machines Theory Design And Applications

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Spiking Neurons vs traditional artificial neurons.

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Deep Beliefs Network

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Bayes' Rule | Deep learnings

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How Restricted Boltzmann Machines Work? | Deep learning for Recommendation System

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Understanding GPT-3: From Embeddings to Predictions | Transformer

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What is GPT ? Transformer | Machine Learning Model

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Dynamics of a Small Dendritic Neural Network

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Qr Code to Deep learning Space

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The Building Blocks of Computer Vision: CNN Architecture 101

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CNN 102: Building an AI That Can See 👁️

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Building a Deep Neural Network in 6 Lines.

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Probability Theory 101: Building Blocks for Machine Learning. Chapter 1.

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What is Eigenvectors & Eigenvalues?

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The Battle of Activation Functions. Neural Network 101. Chapter 3

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Tensors and Tensor - Flow 101

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Linear Transformations & Their Applications in Machine Learning

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Linear Algebra 101: From Basic Concepts to Image Scaling

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What is Feature Scaling? Why is it crucial for Machine Learning?

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Understanding Derivatives: From Basic Concepts to Tangent Lines and Applications in Deep Learning

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From Continuous to Discrete: The Shift in Artificial Neuron Design

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Why is multi-layer network crucial?

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XOR Neural Network: A Case Study in Non-Linearity

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Neural Network 101. Chapter 2.

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Deep learning 101. Chapter 1.

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