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Fundamentals of Neuroscience, Part 1: The Electrical Properties of the Neuron
Organic Chemistry 1
Mountains 101
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Explore time series prediction using Informer, a transformer-based model. Learn to train and make predictions through hands-on coding, enhancing your skills in advanced forecasting techniques.
Line-by-line exploration of the time series transformer, focusing on implementation details and code structure for deep learning enthusiasts and practitioners.
Comprehensive exploration of Informer encoder architecture, detailing its components and implementation for advanced time series forecasting and sequence modeling tasks.
Explore the Informer Encoder architecture, its key components, and implementation details for advanced time series forecasting and sequence modeling tasks.
Explore generative AI's unique features and how it differs from other AI buzzwords in this concise, informative video.
Clarifies key AI terms: genAI, ChatGPT, and LLMs. Demystifies buzzwords, explaining differences and relationships between these concepts in artificial intelligence.
Explore RAG (Retrieval-Augmented Generation) to mitigate AI hallucinations. Learn key concepts, implementation details, and advanced techniques through multiple passes and quizzes.
Dive into parameter-efficient fine-tuning with LoRA, exploring low-rank matrices, adapters, and techniques to optimize neural network storage and performance while maintaining accuracy.
Explore the historical development of neural networks from McCulloch-Pitts neurons to backpropagation, covering key models like perceptrons, ADALINE, Hopfield networks, Boltzmann machines, and multilayer perceptrons.
Explore the journey of visual information from the retina to the brain, understanding the propagation process and key structures involved in our visual pathway.
Explore center-surround receptive fields in the human brain and their connection to computer vision, including different types, visual demonstrations, and practical applications.
Explore how Self-Taught Reasoning (STaR) enhances Large Language Models' arithmetic capabilities through step-by-step explanations and practical demonstrations of this innovative approach.
Master the mathematical foundations of back propagation through detailed hand calculations, covering forward/backward passes, gradient computation, and weight updates with verification.
Explore the fundamental differences between LLM Agents, traditional LLMs, and RAG systems through clear comparisons and practical insights into autonomous AI capabilities.
Explore the foundational concepts of neural networks through the Perceptron algorithm, from basic neuron mechanics to practical implementation details with interactive quizzes and demonstrations.
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