Neural Nets for NLP 2020: Advanced Search Algorithms
Explore advanced search algorithms for NLP, including beam search, minimum Bayes risk, and sampling-based methods. Learn to address search errors and model errors in neural networks.
Neural Nets for NLP - Minimum Risk Training and Reinforcement Learning
Explore minimum risk training and reinforcement learning in NLP, covering error analysis, policy gradients, and value-based methods to enhance neural network performance.
Neural Nets for NLP 2020 - Generating Trees Incrementally
Explore transition-based parsing, shift-reduce parsing with neural networks, stack LSTMs, and semantic parsing for natural language processing applications.
Neural Nets for NLP - Debugging Neural Nets
Learn techniques for identifying and resolving issues in neural networks for NLP, covering training and test-time problems, optimization strategies, and performance analysis.
CMU Neural Nets for NLP - Distributional Semantics and Word Vectors
Explore distributional semantics and word vectors in NLP, covering techniques like skip-grams, CBOW, and advanced methods for creating and evaluating word embeddings.
Neural Nets for NLP 2020 - Language Modeling, Efficiency/Training Tricks
Explore neural language models, optimization techniques, and evaluation metrics for NLP. Learn about feed-forward networks, overfitting prevention, and automatic optimization methods.
Neural Nets for NLP 2019 - Advanced Search Algorithms
Explore advanced search algorithms for NLP, including beam search, A* search, and search with future costs. Learn techniques to improve efficiency and effectiveness in natural language processing tasks.
Neural Nets for NLP - Multi-task, Multi-lingual Learning
Explore multi-task and multi-lingual learning in NLP, covering feature extraction, pre-training encoders, parameter adaptation, and structured prediction across languages.
Neural Nets for NLP 2019 - Models of Dialog
Learn about chat-based and task-based dialog models, covering generation, retrieval, and task completion approaches. Explore neural architectures, evaluation methods, and techniques for improving coherence and personality in dialog systems.
Neural Nets for NLP 2019 - Unsupervised and Semi-supervised Learning of Structure
Explore unsupervised and semi-supervised learning methods for NLP, covering feature and structure learning, design decisions, and practical examples in this advanced lecture.
CMU Advanced NLP: Text Classification
Comprehensive introduction to neural networks for NLP, covering core concepts like forward/backward algorithms, parameter updates, and training techniques for text classification tasks.
Neural Nets for NLP 2021 - Document-Level Models
Explores advanced NLP techniques for document-level tasks, including long-document modeling, entity coreference, and discourse parsing, with a focus on neural network approaches and their applications.
How Generative Models Work - Understanding Neural Networks and Auto Encoders
Explore various generative models, their inner workings, and see a live demo of StyleGAN creating new images in RunwayML. Learn about neural networks and auto-encoders.
Watch Tesla’s Self-Driving Car Learn in a Simulation
Explore Tesla's AI-powered self-driving technology, including neural networks, improved detection systems, and simulation-based learning for enhanced autonomous vehicle performance.
All Things VQGAN - Variational AutoEncoder and VQ-VAE with Codebook Explanations - Part 2
Explore Variational AutoEncoders and Vector Quantized VAE, focusing on codebook explanations. Learn how these advanced techniques improve upon traditional autoencoders for better image generation and representation.
All Things VQGAN - AutoEncoder with Latent Space and Reconstruction Error - Part 1
Explore AutoEncoders, latent space, and reconstruction error in VQGAN. Learn key concepts and their applications in AI, with practical examples and in-depth explanations.
Adding Layers and Forward Functions to Your Neural Network in PyTorch
Learn to create neural networks in PyTorch: design layers, implement forward functions, and adapt models for various data types. Hands-on examples cover basic to complex architectures for structured and unstructured data.
Graph Neural Networks Implementation in Python
Learn Graph Neural Networks implementation in Python, covering graph representations, node embeddings, message passing, and practical applications using NetworkX and PyG libraries.
Create and Visualize Your First Perceptron and Feed Forward Network in Python
Learn to build and visualize Perceptron and Feed Forward networks in Python using Keras and TensorFlow. Ideal for beginners, this hands-on tutorial covers network creation, visualization, and fundamental deep learning concepts.
Create and Visualize Your Very First Neural Network in Python
Create and visualize your first neural network in Python using Keras and TensorFlow. Learn step-by-step, from basic concepts to building a sequential model, with helpful visualizations and a pre-trained network example.
How to Transform and Improve Your Deep Learning Code to a Visual Neural Network
Learn to visualize and improve deep learning neural networks, making them easier to understand and explain through code transformation and network visualization techniques.