Attention and Transformer Encoder Architecture
Master the inner workings of Transformer encoders and attention mechanisms in deep learning, exploring their architecture, self-attention calculations, and practical applications in modern AI.
Finetuning a Sequence-to-Sequence Model with RLHF
Master sequence-to-sequence model finetuning techniques and reinforcement learning from human feedback (RLHF) through hands-on demonstrations and practical implementations.
Transformer Decoder - Pretraining and Finetuning
Master transformer decoder architecture fundamentals, exploring pretraining methods and effective finetuning techniques for advanced natural language processing applications.
Generative Models: Controllable Generation and Learning
Dive into advanced generative AI models with Prof. Patras, exploring controllable generation techniques and learning mechanisms for creating more precise and manageable AI-generated content.
From Handcrafted to End-to-End Learning and Back: A Journey Through Multi-Object Tracking Paradigms
Explore the evolution of multi-object tracking paradigms, from tracking-by-detection to end-to-end learning approaches, examining their effectiveness and current state-of-the-art status.
Machine Learning - Lecture 25
Dive into advanced data science concepts through comprehensive coverage of machine learning principles, algorithms, and practical applications in this graduate-level lecture from University of Utah.
Machine Learning - Lecture 24
Dive into advanced data science concepts through comprehensive coverage of machine learning principles, algorithms, and practical applications in this graduate-level lecture from University of Utah.
Machine Learning - Lecture 23
Dive into advanced data science concepts through comprehensive coverage of machine learning principles, algorithms, and practical applications in this graduate-level academic lecture.
The Unreasonable Effectiveness of Large Language-Vision Models for Video Domain Adaptation - Lecture 31
Explore the groundbreaking potential of large language-vision models in video domain adaptation through Prof. Ricci's expert analysis of their effectiveness and applications.
The Inner Workings of Large Language Models - Visualizing Self-Attention Mechanisms
Dive into the mechanics of Large Language Models, exploring self-attention mechanisms, architectural designs, and performance optimization through clear visualizations and practical examples.
The ALPACA Code: Self-Instruct Fine-Tuning of Large Language Models
Dive into the technical implementation of self-instruct fine-tuning for Large Language Models using ALPACA, covering synthetic dataset generation and parallel task optimization.
Battling Deepfakes and Disinformation with Project Origin
Explore Project Origin's innovative approach to combating disinformation and deepfakes, creating trust between media publishers and consumers. Learn about the fusion of science and technology's impact on society.
Using MPT-7B in Hugging Face and LangChain
Explore MPT-7B LLM implementation in Hugging Face and LangChain, covering setup, initialization, tokenization, and text generation. Learn to leverage open-source models for AI applications.
Build Conversational Agents with Vector DBs - LangChain
Learn to create powerful conversational agents using vector databases and LangChain, combining retrieval augmentation with chatbots for enhanced data freshness and domain-specific knowledge.
Agents Masterclass from LangChain Founder - LLM Bootcamp
Explore tool-using language models with long-term memory in this comprehensive overview of AI agents, covering key concepts, challenges, and real-world examples from LangChain's co-creator.
Troubleshooting Deep Neural Networks - Full Stack Deep Learning - Spring 2021
Learn a systematic approach to troubleshoot deep neural networks, from starting simple to tuning hyperparameters, with practical strategies for implementation, debugging, evaluation, and improvement.
Infrastructure & Tooling - Full Stack Deep Learning - Spring 2021
Comprehensive overview of ML infrastructure and tools, covering software engineering, computing, resource management, frameworks, experiment management, and hyperparameter optimization for practitioners.
ML Projects - Full Stack Deep Learning - Spring 2021
Learn to set up ML projects professionally, covering lifecycle, feasibility, impact, archetypes, metrics, and baselines. Gain insights into project management and optimization for successful machine learning implementations.
Transfer Learning and Transformers - Full Stack Deep Learning - Spring 2021
Explore transfer learning's evolution from computer vision to NLP, diving into embeddings, language models, and the revolutionary Transformer architecture. Gain insights into various models and their applications.
Recurrent Neural Networks - Full Stack Deep Learning - Spring 2021
Explore Recurrent Neural Networks: architecture, challenges, solutions, and applications. Dive into LSTMs, bidirectionality, attention mechanisms, CTC loss, and non-recurrent sequence models like WaveNet.
Computer Vision Applications - Full Stack Deep Learning - Spring 2021
Comprehensive overview of deep learning applications in computer vision, covering ConvNet architectures, detection methods, and advanced tasks like 3D shape inference and style transfer.