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Explore the inner workings of Convolutional Neural Networks, from basic concepts to advanced techniques, in this comprehensive guided tour of deep learning architecture.
Explore the fundamentals of data science, from data acquisition to machine learning, with practical insights and best practices for effective analysis and decision-making.
Explore k-nearest neighbors algorithm for classification and regression, including key considerations like k value, feature scaling, and distance metrics.
Guided tour of 2D convolution for CNNs and image processing, covering element-wise operations, feature detection, kernels, and practical applications.
Explore four major challenges for home robots: data minimization, building trust, task complexity, and human-robot interaction. Insights from industry expert on future of domestic robotics.
Comprehensive exploration of convolutional neural networks, covering their structure, operation, and training process, including key concepts like filtering, pooling, and backpropagation.
Explore how robotics research can advance AI towards human-level intelligence by relaxing current assumptions and focusing on physical interaction and broader capabilities.
Explore recurrent neural networks and LSTM, their applications in sequence prediction, and practical uses in machine learning and AI.
Explore the fundamentals of data science through plain English explanations, covering key concepts like feature engineering, machine learning, and deep learning without technical jargon.
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