ETA Prediction with Graph Neural Networks in Google Maps - Paper Explained
Explore Graph Neural Networks for ETA prediction in Google Maps, covering graph formation, features, GNN architecture, loss functions, baselines, inference, and experimental results.
Machine Learning + Neural Networks Live Sessions - SVM
Explore Support Vector Machines (SVM) in machine learning and neural networks, focusing on their applications and implementation techniques.
Creating a Machine Learning GUI App with Taipy and TensorFlow
Build a stylish GUI for image classification using Taipy and TensorFlow. Learn to process images, create neural networks, and design user-friendly interfaces for machine learning applications.
Introduction to Soft Computing
Explore fuzzy logic, genetic algorithms, and artificial neural networks in this comprehensive introduction to soft computing techniques and their applications.
STM32Cube.AI Workshop
Hands-on workshop on implementing Neural Networks on STM32 devices using STM32CubeMX and X-Cube-AI tools, covering AI basics, dataset preparation, model creation, and practical labs.
Graph Neural Networks- A Gentle Introduction
Gentle introduction to Graph Neural Networks, covering key concepts, properties, and variants. Learn about graph representation, information propagation, and common tasks in this emerging field of machine learning.
Programming LSTM with Keras and TensorFlow
Explore LSTM and GRU layers for recurrent neural networks in Keras, laying the groundwork for NLP and time series prediction with practical examples and implementations.
Neurobiology Basics
Comprehensive exploration of nervous system anatomy, physiology, and function, covering neurons, brain structures, sensory systems, and related disorders through detailed animations.
Artificial Intelligence
This whole playlist is going to instroduce you to Artificial Intelligence and Problem Solving using AI in Hindi.
AI, Machine Learning and Deep Learning
Explore AI, machine learning, and deep learning concepts, including supervised, unsupervised, and reinforcement learning paradigms. Learn when to use traditional ML or deep neural networks for audio applications.
Next in Data Visualization - Interactive Systems for Intelligence Augmentation
Explore interactive data visualization as a tool for studying user interface design and human-computer interaction, with insights from award-winning systems used by Wikipedia and Jupyter/Python communities.
Domain Adaptation with Invariant Representation Learning - What Transformations to Learn?
Explore domain adaptation in machine learning, focusing on invariant representation learning and its challenges. Learn about novel techniques for optimal feature mapping across domains.
Optimizing for Interpretability in Deep Neural Networks - Mike Wu
Explore techniques for making deep neural networks more interpretable through regularization, focusing on medical applications in critical care and HIV treatment. Insights on balancing model performance with human-understandable decision processes.
GANs in Medical Image Synthesis, Translation, and Augmentation - Jason Jeong
Explore GANs in medical imaging for synthetic data generation, addressing scarcity and imbalance issues. Learn about applications in CT, MRI, and disease detection, with insights on challenges and future directions.
Self-Training - Weak Supervision Using Untrained Neural Nets for MR Reconstruction - Beliz Gunel
Exploring untrained neural networks for MR reconstruction, comparing self-training and weak supervision methods to improve performance with limited data while addressing slow inference times.
Neural Networks in R and the MNIST Data - Lecture 10.R.1
Explore neural networks using R and MNIST data, learning practical implementation of deep learning techniques for image recognition and statistical analysis.
Stanford Seminar 2022 - Transformer Circuits, Induction Heads, In-Context Learning
Explore reverse engineering of transformer language models, focusing on induction head circuits and their role in in-context learning. Gain insights into neural network interpretability and AI safety.
Stanford Seminar 2022 - Self Attention and Non-Parametric Transformers
Explore self-attention and Non-Parametric Transformers with PhD students discussing Transformer origins, intuitions, and recent advancements in neural network architecture.
Stanford Seminar 2022 - DeepMind's Perceiver and Perceiver IO: New Data Family Architecture
Exploring DeepMind's Perceiver architecture for flexible AI processing across diverse data types, offering domain-general solutions comparable to specialized models in various tasks.
Stanford Seminar - Mixture of Experts Paradigm and the Switch Transformer
Explore the Mixture of Experts paradigm and Switch Transformer, revolutionizing deep learning with sparsely-activated models for improved efficiency and performance in language processing tasks.
Information Theory of Deep Learning - Naftali Tishby
Explores the connection between deep learning and information theory, offering new insights into neural network design, generalization, and optimization through an information bottleneck framework.