An Overview of Deep Learning and Neural Networks
Comprehensive introduction to deep learning concepts, covering neural networks, convolution, pooling, normalization, and more. Includes code examples for practical implementation.
Deep Generative Modeling
Explore deep generative modeling, including autoencoders, variational autoencoders, and GANs. Learn about latent variable models, reparameterization tricks, and recent advances in generative AI.
Convolutional Neural Networks
Explore convolutional neural networks for computer vision, covering feature extraction, network architecture, and real-world applications like self-driving cars in this comprehensive lecture.
MIT 6.S191 - Recurrent Neural Networks
Explore recurrent neural networks, LSTMs, and attention mechanisms for sequence modeling. Learn backpropagation through time, gradient issues, and practical applications in deep learning.
Introduction to Deep Learning
Comprehensive introduction to deep learning fundamentals, covering perceptrons, neural networks, loss functions, gradient descent, backpropagation, and regularization techniques.
MIT: Image Domain Transfer - NVIDIA Guest Lecture
Explore image domain transfer techniques, from artistic style transfer to multimodal translations, with applications in photo smoothing, seasonal transformations, and game engines.
Biologically Inspired Neural Networks
Explore biologically plausible learning algorithms for neural networks, inspired by brain function. Discover synaptic plasticity rules and their application in deep learning architectures.
MIT 6.S191 - Deep Learning Limitations and New Frontiers
Explore deep learning limitations, adversarial attacks, uncertainty quantification, and AutoML in this lecture. Gain insights into neural network challenges and emerging frontiers in AI research.
Deep Generative Modeling
Explore deep generative modeling, including autoencoders, VAEs, and GANs. Learn about latent variables, representation learning, and applications in image generation and domain transformation.
Convolutional Neural Networks
Explore convolutional neural networks for computer vision tasks, covering feature extraction, convolution operations, and applications in classification, segmentation, and image captioning.
MIT: Recurrent Neural Networks
Explore deep sequence modeling with recurrent neural networks, including LSTMs and attention mechanisms. Learn to tackle challenges like vanishing gradients and long-term dependencies in sequence tasks.
MIT: Introduction to Deep Learning
Foundations of deep learning: perceptrons, neural networks, loss functions, backpropagation, optimization techniques, and strategies to prevent overfitting in neural network training.
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 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.
How Data Science Works
Explore the fundamentals of data science through plain English explanations, covering key concepts like feature engineering, machine learning, and deep learning without technical jargon.