Lecture 10

The Transformer Model

The Transformer architecture follows an encoder-decoder structure but does not rely on recurrence and convolutions in order to generate an output.
In a nutshell, the task of the encoder, on the left half of the Transformer architecture, is to map an input sequence to a sequence of continuous representations, which is then fed into a decoder.
The decoder, on the right half of the architecture, receives the output of the encoder together with the decoder output at the previous time step to generate an output sequence.
read more

Could generative AI solve fashion’s excess stock problems?

Emerging technology has the potential to transform the fashion supply chain by enabling real-time tracking of demand and inventory. The rewards could be huge — but there are still big barriers to overcome. Amid fluctuating global demand and macro-economic pressures, optimising and allocating inventory for multiple locations, across multiple channels and without holding excess stock remains a challenge for many fashion businesses. Could generative artificial intelligence be the answer?
read more

AI at Work: Friend and Foe

Transforming Demand Planning With Generative AI

In today's rapidly evolving business landscape, staying ahead of the curve requires a sophisticated approach to demand planning. Traditional methods often fall short in navigating the complexities of modern markets, forcing businesses to constantly operate in a fire-fighting mode while being blindsided by marketplace changes. However, the integration of generative AI and large language models (LLMs) presents an unprecedented opportunity to transform demand planning processes.
read more

Linear Regression to GPT in Seven Steps

There are numerous writings about Generative AI. There are essays dedicated to its applications, ethical and moral issues, and its risk to human society. If you want to understand the technology itself, there is a range of available material from the original research papers to introductory articles and videos. Depending on your current level and interest, you can find the right resources for study.

read more

This Article from IBM, shows how AI can help in Fast Fashion

High-tech solutions aren’t yet mainstream in the world of fashion, but we believe they should be. We first became convinced of Watson’s power after a visit to the IBM Labs in Bangalore. Bearing data about our brands, we presented a problem to solve: of two similar products, why did one sell and the other didn’t? IBM developed a proof-of-concept solution that gave us the answer. Clearly, AI could help our company succeed.
read more

Large Language Models for Supply Chain Optimization

Supply chain operations traditionally involve a variety of complex decision making problems. Motivated by the recent advances in Large Language Models (LLMs), we study how this disruptive technology can help bridge the gap between supply chain automation and human comprehension and trust thereof. We design OptiGuide – a framework that accepts as input queries in plain text, and outputs insights about the underlying optimization outcomes.
read more

CatBoost regression in 6 minutes

CatBoost builds upon the theory of decision trees and gradient boosting. The main idea of boosting is to sequentially combine many weak models (a model performing slightly better than random chance) and thus through greedy search create a strong competitive predictive model. Because gradient boosting fits the decision trees sequentially, the fitted trees will learn from the mistakes of former trees and hence reduce the errors. This process of adding a new function to existing ones is continued until the selected loss function is no longer minimized.

read more

High-Level Overview of Popular AI/ML Applications in Upstream Oil & Gas

We aren’t moving away from hydrocarbons as fast as we would like. It’s a fact that 2022 has forced us to face, and while they’re here, it’s essential to produce them in the cleanest, safest, and most cost-efficient manner possible. Let’s review some interesting AI/ML applications that are helping reduce waste and costs in the upstream sector of the oil and gas industry.

read more

The Encoder - Decoder Architecture

The paper’s author says the architecture is simple because it has no recurrence and convolutions. In other words, it uses other common concepts like an encoder-decoder architecture, word embeddings, attention mechanisms, softmax, and so on without the complication introduced by recurrent neural networks or convolutional neural networks.The transformer is an encoder-decoder network at a high level, which is very easy to understand. So, this article starts with a bird-view of the architecture and aims to introduce essential components and give an overview of the entire model architecture.

read more

Page updated

Google Sites

Report abuse