KL Divergence Courses and Certifications

DeepSeek R1 Theory Tutorial - Architecture, GRPO, KL Divergence

Dive into DeepSeek R1's innovative architecture, exploring Group Relative Policy Optimization (GRPO), KL divergence, and reinforcement learning techniques with practical code examples and mathematical explanations.

• freeCodeCamp
• 1 hour 8 minutes
• On-Demand
• Free Video

Yacine Mahdid

KL Divergence Implementation in DeepSeek R1 - A Deep Learning Tutorial

Dive into KL divergence implementation in DeepSeek R1, exploring mathematical foundations, Monte Carlo estimation, and practical benchmarking for deep learning applications.

• YouTube
• 20 minutes
• On-Demand
• Free Video

Coursera

Partition & Monitor AI Models Effectively

Discover how to build reliable AI systems that detect drift, prevent data leakage, and automatically retrain to maintain production performance over time.

• Coursera
• 2 hours 1 minute
• On-Demand
• Paid Course

Coursera

Validating and Safeguarding Production AI

Discover how to build robust production AI systems with automated monitoring, drift detection, secure deployment, and continuous retraining pipelines for reliable MLOps.

• Coursera
• 16 hours 44 minutes
• On-Demand
• Paid Course

Artem Kirsanov

The Key Equation Behind Probability - Entropy, Cross-Entropy, and KL Divergence

Explore probability theory fundamentals, from surprise to entropy, cross-entropy, and KL divergence. Learn key concepts applicable to neuroscience and machine learning.

• YouTube
• 26 minutes
• On-Demand
• Free Video

Serrano.Academy

Reinforcement Learning for Large Language Models - RLHF, PPO, DPO, and GRPO

Discover how to fine-tune LLMs using RLHF, PPO, DPO, and GRPO techniques with practical deep reinforcement learning methods for transformer models.

• YouTube
• 2 hours 28 minutes
• On-Demand
• Free Video

HuggingFace

Reinforcement Learning from Human Feedback - From Zero to ChatGPT

Explore Reinforcement Learning from Human Feedback, its role in ChatGPT, and key concepts in NLP and RL. Gain insights into RLHF's technical aspects and open research areas.

• YouTube
• 1 hour 1 minute
• On-Demand
• Free Video

Paul Hand

Variational Autoencoders

Explore generative models, autoencoders, and variational techniques in deep learning. Learn about architecture, optimization, and practical applications of variational autoencoders.

• YouTube
• 43 minutes
• On-Demand
• Free Video

Serrano.Academy

What Is Quantum Machine Learning?

Friendly introduction to quantum computing and machine learning, covering qubits, quantum gates, entanglement, and their applications in generative modeling and optimization.

• YouTube
• 52 minutes
• On-Demand
• Free Video

Ryan O'Donnell

Quantum "Supremacy" - Lecture 25 of Quantum Computation at CMU

Explore quantum supremacy, its challenges, and implications in computational theory. Delve into error correction, threshold theorems, and the potential of quantum systems.

• YouTube
• 1 hour 21 minutes
• On-Demand
• Free Video

Robust Pre-Training by Adversarial Contrastive Learning - CAP6412 Spring 2021

Explore robust pre-training techniques using adversarial contrastive learning, focusing on unsupervised and semi-supervised methods for improved model resilience and accuracy.

• YouTube
• 44 minutes
• On-Demand
• Free Video

Chemometrics & Machine Learning in Copenhagen

Generalized Tensor Decomposition - Utility for Data Analysis

Explore tensor decomposition methods beyond sum of squares error, including KL divergence and logistic odds, for improved data analysis in various fields like criminology and neuroscience.

• YouTube
• 57 minutes
• On-Demand
• Free Video

Graham Neubig

CMU Neural Nets for NLP - Models with Latent Random Variables

Explore neural network models with latent random variables for NLP, covering VAEs, generative models, and techniques for discrete latent variables and controllable text generation.

• YouTube
• 1 hour
• On-Demand
• Free Video

Simons Institute

Distribution Learning Meets Graph Structure Sampling

Explore the connection between PAC-learning of graphical models and efficient graph structure sampling, leveraging online learning algorithms for new insights in high-dimensional distribution learning.

• YouTube
• 27 minutes
• On-Demand
• Free Video

Trelis Research

Distillation of Transformer Models - Tutorial and Code Walk-through

Learn advanced techniques for distilling transformer models, including pruning, fine-tuning, and performance evaluation. Gain practical insights through code walkthroughs and real-world examples.

• YouTube
• 1 hour 21 minutes
• On-Demand
• Free Video