Prediction and Planning Under Uncertainty
Explore prediction and planning in uncertain environments, covering model predictive control, stochastic scenarios, and practical applications in AI decision-making.
Joint Embedding Method and Latent Variable Energy Based Models
Explore joint embedding methods and latent variable energy-based models with Yann LeCun. Learn about predictive systems, inference, probabilistic models, and various training techniques for advanced machine learning applications.
Latent Variable Energy Based Models - Inference
Explore Latent Variable Energy Based Models and inference techniques, focusing on energy functions, manifold generation, and free energy computation in unconditional cases.
Semantic Scholar- Tera-Scale NLP to Accelerate Scientific Research--Daniel Weld
Explore Semantic Scholar's AI-powered tools for accelerating scientific research, including adaptive feeds, personalized alerts, and advanced summarization techniques.
How to Implement a CNN for Music Genre Classification
Implement a CNN for music genre classification using TensorFlow. Learn cross-validation, inference, and practical application with code examples and step-by-step guidance.
How to Deploy ML Models in Production with BentoML
Learn to deploy Machine Learning models in production using BentoML, covering installation, model saving, service creation, bento building, and Docker containerization. Includes hands-on examples and practical deployment steps.
Probabilistic Numerics for Inference with Simulations
Explore probabilistic numerics for inference with simulations, covering high-dimensional models, numerical methods, and innovative approaches to machine learning challenges.
Technical Overview of Machine Learning Life Cycle
Comprehensive technical overview of the machine learning lifecycle, covering data preparation, model training, deployment, and inference. Ideal for AI engineers seeking in-depth understanding of ML processes.
An Improved and Functional Design of End-to-End Machine Learning Life Cycle
Comprehensive overview of the machine learning lifecycle, covering data preparation, model training, deployment, and inference, with practical insights for effective implementation and monitoring.
What Are CycleGANs? - A Novel Deep Learning Tool in Pathology
Explore CycleGANs for unpaired image conversion, including applications in pathology and microscopy. Learn model architecture, training, and inference through code demos and real-world examples.
Captcha Recognition Using PyTorch - Convolutional-RNN + CTC Loss
Learn to implement a Convolutional-RNN model for captcha recognition using PyTorch. Covers data preparation, model architecture, CTC loss, and training/inference processes.
Speech-to-Intent on MCU: TinyML for Efficient Device Control - Lecture 6
Learn efficient speech-to-intent techniques for MCUs, focusing on direct parsing of user utterances into actionable output. Explore model training, deployment, and testing on Wio Terminal.
Machine Learning Inference on Raspberry Pi Pico 2040 via Edge Impulse
Learn to implement machine learning inference on Raspberry Pico 2040 using Edge Impulse, covering environment setup, sample data inference, sensor data processing, and porting details.
Probabilistic Programming
Explore probabilistic programming concepts, from intuitive physics to computational theory of mind, with hands-on practice in web-based probabilistic programming languages.
Stable Diffusion Dreambooth Tutorial - Just Like Corridor Crew
Learn to use Dreambooth SD Optimised for adding custom elements to Stable Diffusion. This tutorial covers setup, training, and inference, with tips for different hardware configurations.
From Denoising Diffusion Models to Transport for Generative Modeling and Inference
Explore advanced generative modeling techniques, from denoising diffusion models to transport-based approaches, focusing on diffusion Schrödinger bridge and its numerical approximation for improved inference.
Achieving Real-Time Generative AI During Gameplay for Games on Kubernetes
Explore real-time GenAI integration in Kubernetes-based games. Learn deployment strategies, low-latency solutions, and scalability techniques for enhanced player experiences and broader ecosystem benefits.
Introduction to Large Language Models - Future and Security Challenges
Comprehensive introduction to Large Language Models, covering their functionality, training, future potential, and security challenges. Explores applications, scaling, tool use, and emerging paradigms in AI.
Subtractive Mixture Models: Representation and Learning
Exploring subtractive mixtures in probabilistic circuits: increased expressiveness, efficient learning, and inference for complex distributions. Theoretical and empirical advantages over traditional additive mixtures.
Bayesian Networks for Causal Reasoning - Lecture 1
Introduction to Bayesian networks for causal reasoning in healthcare, exploring their application in analyzing clinical data and inferring patterns to improve patient outcomes and medical decision-making.
PyTorch 2.1 - New Features and Accelerating Generative AI Models
Explore PyTorch 2.1's new features for AI model acceleration, including compile, distributed, inference, and edge technologies, with insights on optimizing generative AI models for enhanced performance.