Autoregressive Models Courses and Certifications

Deep Generative Models - Autoregressive Models - Lecture 3

Explore autoregressive models in deep learning, focusing on their architecture, training, and applications in generating sequential data like text and images.

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

Yannic Kilcher

Scaling Autoregressive Models for Content-Rich Text-to-Image Generation

Explore Parti, a groundbreaking autoregressive text-to-image model. Learn about its architecture, datasets, experimental results, and capabilities in generating realistic, accurate images from text prompts.

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

Simons Institute

Tractable Control for Autoregressive Language Generation

Explores using tractable probabilistic models to impose lexical constraints in autoregressive text generation, achieving state-of-the-art performance on constrained text generation benchmarks.

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

Stanford Seminar - Decision Transformer: Reinforcement Learning via Sequence Modeling

Innovative approach to reinforcement learning using sequence modeling and Transformer architecture, offering simplicity and scalability while matching or exceeding state-of-the-art performance in various tasks.

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

Demand Forecasting Using Time Series

30 ratings at Coursera

Explore time series analysis for demand forecasting, covering stationarity, trends, autocorrelation, and predictive models like ARIMA. Gain practical skills in Python for effective supply chain management.

• Coursera
• 9 hours 23 minutes
• On-Demand
• Free Online Course (Audit)

Time Series Analysis in Python

19 ratings at DataCamp

In this four-hour course, you’ll learn the basics of analyzing time series data in Python.

• DataCamp
• 4 hours
• On-Demand
• Free Trial Available

Packt

Deep Learning - Recurrent Neural Networks with TensorFlow

Master recurrent neural networks for time series prediction and NLP tasks using TensorFlow. Implement advanced architectures like GRUs and LSTMs for stock forecasting, image classification, and text analysis.

• Coursera
• 5 hours 6 minutes
• On-Demand
• Free Online Course (Audit)

STAT 510: Applied Time Series Analysis

Learn to analyze and forecast time series data using ARIMA models, smoothing methods, and advanced techniques. Gain practical skills in R for handling financial, medical, and weather-related temporal data.

• OPEN.ED@PSU
• On-Demand
• Free Online Course

Aleksa Gordić - The AI Epiphany

VQ-VAEs - Neural Discrete Representation Learning - Paper + PyTorch Code Explained

Explore VQ-VAEs for neural discrete representation learning, covering key concepts, implementation details, and applications in generative modeling. Gain insights into this important AI technique.

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

Prodramp

Text to Image AI Models - Different Methodologies and How It Works

Explore various AI methodologies for text-to-image generation, including Autoregressive models, GANs, VQ-VAE Transformers, and Diffusion models. Learn their inner workings and compare advanced systems like DALL-E 2 and Google Imagen.

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

Alan Turing Institute

Time Series Class - Part 1 - Dr Ioannis Papastathopoulos, University of Edinburgh

Learn fundamental concepts and advanced techniques for analyzing time series data, including ARMA models, state-space models, and neural networks for forecasting and inference.

• YouTube
• 1 hour 50 minutes
• On-Demand
• Free Video

Alan Turing Institute

Time Series Class - Part 1 - Dr. Ioannis Papastathopoulos, University of Edinburgh

Learn fundamental concepts and advanced techniques in time series analysis, including ARMA models, state-space models, and neural networks for forecasting and inference.

• YouTube
• 1 hour 51 minutes
• On-Demand
• Free Video

Alan Turing Institute

Time Series Class - Part 2 - Professor Chris Williams, University of Edinburgh

Explore time series analysis, from moving average models to advanced state-space techniques and recurrent neural networks. Gain practical skills in parameter estimation, forecasting, and applying models to real-world data.

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

Pascal Poupart

Normalizing Flows - Priyank Jaini

Explore normalizing flows, density estimation, and autoregressive models. Learn about change of variables, increasing triangular maps, and neural autoregressive flows.

• YouTube
• 1 hour 5 minutes
• On-Demand
• Free Video

Yannic Kilcher

Imputer - Sequence Modelling via Imputation and Dynamic Programming

Explore a novel sequence-to-sequence model that balances efficiency and performance using imputation and dynamic programming for faster inference in various applications.

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