CMU Advanced NLP 2022 - Latent Variable Models
Explore advanced NLP concepts: latent variables, variational autoencoders, and their applications in natural language processing. Gain insights into generative models and feature learning.
CMU Advanced NLP: Pre-training Methods
Explore advanced NLP concepts including multi-task learning, sentence embeddings, BERT variants, and language modeling objectives. Gain insights into cutting-edge techniques for natural language processing.
Neural Nets for NLP 2021 - Multilingual Learning
Explore multilingual learning in NLP, covering models, data balancing, parameter sharing, and cross-lingual transfer techniques for improved language processing across diverse languages.
Neural Nets for NLP - Models with Latent Random Variables
Explore neural network models with latent random variables for NLP, covering variational autoencoders, discrete latent variables, and their applications in language processing.
Neural Nets for NLP 2021 - Attention
Explore attention mechanisms in neural networks for NLP, covering key concepts, improvements, and specialized varieties with practical examples and applications.
CMU Multilingual NLP - Machine Translation-Sequence-to-Sequence Models
Explore conditional language modeling, sequence-to-sequence models, generation methods, attention mechanisms, and transformers in multilingual natural language processing.
Neural Nets for NLP - Models with Latent Random Variables
Explore neural network models with latent random variables, covering variational autoencoders and their applications in NLP. Learn about generative vs. discriminative models and handling discrete latent variables.
Neural Nets for NLP 2020 - Attention
Explore attention mechanisms in neural networks for NLP, covering types, improvements, and specialized varieties. Dive into the "Attention is All You Need" paper and its impact on the field.
Neural Nets for NLP - Latent Random Variables
Explore latent random variables in neural networks for NLP, covering variational autoencoders, discrete latent variables, and applications in language processing and text generation.
Neural Nets for NLP 2020 - Document Level Models
Explore document-level NLP techniques including language modeling, coreference resolution, and discourse parsing. Learn advanced approaches for handling long-range dependencies and entity relationships in text.
CMU Advanced NLP 2021 - Latent Variable Models
Explore advanced NLP concepts like variational autoencoders, discrete latent variables, and their applications in natural language processing 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.
Video to Anime - Generate an Epic Animation from Your Phone Recording by Using Stable Diffusion AI
Transform phone recordings into stunning animations using Stable Diffusion AI. Learn video editing, frame extraction, AI training, and advanced techniques for creating high-quality animated content from real-life footage.
Designing a Metroidvania Game With AI - Stable Diffusion Walkthrough
Explore AI-powered game design using Stable Diffusion to create stunning Metroidvania-style visuals, from concept sketches to detailed environments and atmospheric effects.
Anime Character Design and Animation with Stable Diffusion, Photoshop, and After Effects - Part 1
Learn to design and animate anime characters using Stable Diffusion, Photoshop, and After Effects. Master character generation, refinement, background creation, and layer separation techniques.
GPT-2 and Transformers
Explore text-based AI models, focusing on GPT-2 and Transformers. Learn about their architecture, capabilities, and potential applications in natural language processing.
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.
Efficient Geometry-Aware 3D Generative Adversarial Networks - EG3D
Hands-on guide to using pre-built EG3D models on Ubuntu 22.04 with Python 3.9, PyTorch, and CuDA GPU libraries. Learn to visualize, export, and generate 3D shapes from various models.
TorchStudio - Deep Learning Toolkit
Hands-on guide to TorchStudio: Access PyTorch datasets, train models, and compare results. Ideal for beginners and experienced users in the PyTorch ecosystem.
One-shot Face Stylization with JoJoGAN - In-depth Tutorial - Part 1
Learn face stylization with JoJoGAN: transfer styles to images using minimal data and GPU assistance. Covers setup, existing models, custom training, and animal face styles.
Diffusion Models - Google Colab Experimentation with Code and Prebuilt Models - Part 2
Hands-on exploration of Diffusion Models using Google Colab, covering probabilistic implementations and prebuilt models. Includes code examples and practical demonstrations of the denoising process.