Learning Steering for Parallel Autonomy
Explores end-to-end learning for parallel autonomy in self-driving cars, focusing on handling ambiguity, uncertainty estimation, and decision-making capabilities beyond reactionary control.
Computer Vision Meets Social Networks
Explore the intersection of computer vision and social networks, covering low-light enhancement, pose estimation, video attractiveness, classification, captioning, and natural language localization.
Sequence Modeling with Neural Networks
Explore sequence modeling with neural networks, focusing on RNNs, their challenges, and solutions like gated cells. Learn applications in music generation and machine translation.
Beyond Deep Learning - Learning+Reasoning
Explore advanced AI concepts beyond deep learning, focusing on integrating reasoning with learning for more robust and interpretable systems in this IBM Research lecture.
Deep Learning Limitations and New Frontiers
Explore deep learning limitations, new frontiers, and advancements in AI, including uncertainty estimation, adversarial attacks, and meta-learning, with insights on future developments and applications.
Convolutional Neural Networks
Explore deep learning for computer vision, covering CNNs, object detection, and image segmentation. Learn about architectures, applications, and impacts in various fields.
Introduction to Deep Learning - MIT 2018
Explore foundations of deep learning, including perceptrons, neural networks, loss functions, and optimization techniques. Gain insights into training challenges and strategies for effective model development.
How Convolution Works
Guided tour of 2D convolution for CNNs and image processing, covering element-wise operations, feature detection, kernels, and practical applications.
How Convolutional Neural Networks Work, in Depth
Comprehensive exploration of convolutional neural networks, covering their structure, operation, and training process, including key concepts like filtering, pooling, and backpropagation.
Getting Closer to Human Intelligence Through Robotics
Explore how robotics research can advance AI towards human-level intelligence by relaxing current assumptions and focusing on physical interaction and broader capabilities.
Recurrent Neural Networks and Long Short-Term Memory
Explore recurrent neural networks and LSTM, their applications in sequence prediction, and practical uses in machine learning and AI.
Listen Carefully - Speech-to-Speech Launch by Resemble AI
Explore Resemble AI's Speech-to-Speech technology launch, featuring an overview of capabilities, supported languages, and innovative features like Neural Audio Editing and Neural Fill.
GPT-3 Demos Using AI Synthetic Voices - Resemble AI
Explore GPT-3 integration with AI voices for creating realistic ad copy, conversations, and voiceovers. Learn best practices, use cases, and get answers to top questions about this innovative technology.
3D Deep Learning for Gaming with Srinath Sridhar and Stanford Artificial Intelligence
Explore 3D deep learning for gaming, covering human pose estimation, object reconstruction, and computer vision applications with Stanford AI expert Srinath Sridhar.
How to Build AI Voices with Emotions Using Resemble AI
Create lifelike AI voices with emotions, emphasis, and dramatic pauses using Resemble AI's innovative techniques and tools.
Deep Learning in Gaming with Idan Beck
Explore AI/ML applications in gaming, covering navigation, task planning, procedural generation, and reinforcement learning, with insights from Unity expert Idan Beck.
Denoising 3D Multi-Channel Scientific Images Using Noise2Void Deep Learning Approach
Learn to denoise 2D and 3D multichannel scientific images using Noise2Void deep learning, focusing on CZI files from ZEISS microscopes. Explore implementation, training, and practical applications for improved image quality.
Denoising RGB Images Using Deep Learning - Noise2Void
Learn to denoise RGB images using Noise2Void deep learning technique, focusing on implementation, data preparation, and model training for improved image quality without clean data.
How to Get Started with Data Science and Machine Learning
Explore the fundamentals of data science, analytics, AI, and machine learning. Learn essential skills and create a structured learning plan for these in-demand fields.
An Introduction to Object Segmentation Using StarDist Library in Python
Learn object segmentation using StarDist in Python for star-convex shapes in 2D and 3D. Covers installation, implementation, and HD image segmentation with practical examples.
Processing Whole Slide Images as Tiles
Efficient processing of whole slide images using OpenSlide, including H&E normalization and signal separation. Demonstrates tile extraction, processing, and saving for large-scale histopathology analysis.