How to Grid Search Triple Exponential Smoothing for Time Series Forecasting in Python

Exponential smoothing is a time series forecasting method for univariate data that can be extended to support data with a systematic trend or seasonal component. It is common practice to use an optimization process to find the model hyperparameters that result in the exponential smoothing…

PM Modi to attend fourth edition of NITI Lecture Series on artificial intelligence Monday

… Series focussed on ‘leveraging artificial intelligence for inclusive growth’, according … a national programme on employing artificial intelligence towards national development and since … published a National Strategy for artificial intelligence (AI). The Aayog said its … VISIT THE SOURCE ARTICLE Author:

Watch Q-learning Agent Play Game with Python – Reinforcement Learning Code Project

Welcome back to this series on reinforcement learning! In this video, we’ll write the code to enable us to watch our trained Q-learning agent play Frozen Lake. We’ll continue using Python and OpenAI Gym for this task. Last time, we left off having just finished…

Train Q-learning Agent with Python – Reinforcement Learning Code Project

Welcome back to this series on reinforcement learning! As promised, in this video, we’re going to write the code to implement our first reinforcement learning algorithm. Specifically, we’ll use Python to implement the Q-learning algorithm to train an agent to play OpenAI Gym’s Frozen Lake…

Importance of Data in Deep Learning – Fashion MNIST for Artificial Intelligence

This series is all about neural network programming and artificial intelligence. In this post, we will look closely at the importance of data in deep learning by exploring cutting edge concepts in software development, and taking a deep dive into a relatively new dataset. Check…

How to Develop Autoregressive Forecasting Models for Multi-Step Air Pollution Time Series Forecasting

Real-world time series forecasting is challenging for a whole host of reasons not limited to problem features such as having multiple input variables, the requirement to predict multiple time steps, and the need to perform the same type of prediction for multiple physical sites. The…

How to Develop Baseline Forecasts for Multi-Site Multivariate Air Pollution Time Series Forecasting

Real-world time series forecasting is challenging for a whole host of reasons not limited to problem features such as having multiple input variables, the requirement to predict multiple time steps, and the need to perform the same type of prediction for multiple physical sites. The…

OpenAI Gym and Python for Q-learning – Reinforcement Learning Code Project

Welcome back to this series on reinforcement learning! Over the next couple of videos, we’re going to be building and playing our very first game with reinforcement learning in code! We’re going to use the knowledge we gained last time about Q-learning to teach a…

AI Ethics, Strategic Decisioning and Game Theory with Osonde Osoba – TWiML Talk #192

In this episode of our Deep Learning Indaba Series, we’re joined by Osonde Osoba, Engineer at RAND Corporation and Professor at the Pardee RAND Graduate School. Osonde and I spoke on the heels of the Indaba, where he presented on AI Ethics and Policy. We…

Acoustic Word Embeddings for Low Resource Speech Processing with Herman Kamper – TWiML Talk #191

In this episode of our Deep Learning Indaba Series, we’re joined by Herman Kamper, Lecturer in the electrical and electronics engineering department at Stellenbosch University in SA and a co-organizer of the Indaba. Herman and I discuss his work on limited- and zero-resource speech recognition,…