Supply chain and price management were among the first areas of enterprise operations that adopted data science and combinatorial optimization methods and have a long history of using these techniques with great success. Although a wide range of traditional optimization methods are available for inventory and price management applications, deep reinforcement learning has the potential to substantially improve the optimization capabilities for these and other types of enterprise operations due to impressive recent advances in the development of generic self-learning algorithms for optimal control. In this article, we explore how deep reinforcement learning methods can be applied in several basic supply chain and price management scenarios. The traditional price optimization process in retail or manufacturing environments is typically framed as a what-if analysis of different pricing scenarios using some sort of demand model. In many cases, the development of a demand model is challenging because it has to properly capture a wide range of factors and variables that influence demand, including regular prices, discounts, marketing activities, seasonality, competitor prices, cross-product cannibalization, and halo effects. Once the demand model is developed, however, the optimization process for pricing decisions is relatively straightforward, and standard techniques such as linear or integer programming typically suffice. For instance, consider an apparel retailer that purchases a seasonal product at the beginning of the season and has to sell it out by the end of the period. Assuming that a retailer chooses pricing levels from a discrete set (e.g., \$59.90, \$69.90, etc.) and can make price changes frequently (e.g., weekly), we can pose the following optimization problem: The first constraint ensures that each time interval has only one price, and the second constraint ensures that all demands sum up to the available stock level. This is an integer programming problem that can be solved using conventional optimization libraries.

Combine the power of Data Science, Machine Learning and Deep Learning to create powerful AI for Real-World applications! Your CCNA start Deep Learning A-Z: Hands-On Artificial Neural Networks Deep Learning and Computer Vision A-Z: OpenCV, SSD & GANs Artificial Intelligence for Business ZERO to GOD Python 3.8 FULL STACK MASTERCLASS 45 AI projects Comment Policy: Please write your comments that match the topic of this page's posts. Comments that contain links will not be displayed until they are approved.

HIGHEST RATED Created by Andrei Neagoie, Daniel Bourke English [Auto-generated] Students also bought Learn Data Wrangling with Python Machine Learning A-Z: Hands-On Python & R In Data Science Python for Data Science and Machine Learning Bootcamp The Data Science Course 2020: Complete Data Science Bootcamp R Programming A-Z: R For Data Science With Real Exercises! Preview this course GET COUPON CODE Description Become a complete Data Scientist and Machine Learning engineer! Join a live online community of 200,000 engineers and a course taught by industry experts that have actually worked for large companies in places like Silicon Valley and Toronto. This is a brand new Machine Learning and Data Science course just launched January 2020! Graduates of Andrei's courses are now working at Google, Tesla, Amazon, Apple, IBM, JP Morgan, Facebook, other top tech companies.

Mixing quantum computing and Artificial Intelligence (AI) may sound like a new buzzword. However, since quantum computing advances are hinting at profound changes in the very notions of computation, it is natural to reexamine various branches of computer science in the light of these disruptions. As usual, before entering the quantum realm, it is important to get an overview of the classical world. Artificial Intelligence is difficult to define. Probably because intelligence, by itself, is difficult to define.

Let's take a detailed look. This is the most common form of AI that you'd find in the market now. These Artificial Intelligence systems are designed to solve one single problem and would be able to execute a single task really well. By definition, they have narrow capabilities, like recommending a product for an e-commerce user or predicting the weather.This is the only kind of Artificial Intelligence that exists today. They're able to come close to human functioning in very specific contexts, and even surpass them in many instances, but only excelling in very controlled environments with a limited set of parameters. AGI is still a theoretical concept. It's defined as AI which has a human-level of cognitive function, across a wide variety of domains such as language processing, image processing, computational functioning and reasoning and so on.

Facial recognition is arguably the most talked-about technology within the artificial intelligence landscape due to its wide range of applications and biased outputs. Several countries are adopting this technology for surveillance purposes, most notably China and India. Both are among the first countries to make use of this technology on a large scale. Even the EU has pulled back from banning this technology for some years and has left it for the countries to decide. This will increase the demand for professionals who can develop solutions around facial recognition technology to simplify life and make operations efficient. Data science influencer Andrew Ng, along with teaching assistants from Stanford University, have devised a course that includes neural style transfer which enables working with facial images effectively.

TensorFlow remains the dominant AI modeling framework. Most AI (artificial intelligence) developers continue to use it as their primary open source tool or alongside PyTorch, in which they develop most of their ML (machine learning), deep learning, and NLP (natural language processing) models. In the most recent O'Reilly survey on AI adoption in the enterprise, more than half of the responding data scientists cited TensorFlow as their primary tool. This finding is making me rethink my speculation, published just last month, that TensorFlow's dominance among working data scientists may be waning. Neverthless, PyTorch remains a strong second choice, having expanded its usage in the O'Reilly study to more than 36 percent of respondents, up from 29 percent in the previous year's survey. As the decade proceeds, the differences between these frameworks will diminish as data scientists and other users value feature parity over strong functional differentiation.

Google Brain had recently launched the TensorFlow Developer Certificate program which would enable machine learning (ML) enthusiasts to demonstrate their skills in using TensorFlow to solve deep learning and ML problems. According to the blog post, the goal of this certificate is to provide them with the opportunity to showcase their expertise in ML in an increasingly AI-driven job market. TensorFlow is one of the popular open-source libraries in ML which provides a suitable abode with essential tools for ML researchers and developers to perform SOTA ML applications. The developers at Google Brain claim that this is intended as a foundational certificate for students, developers, and data scientists who want to demonstrate practical ML skills through building and training of models using TensorFlow. Currently, this is a level one certificate exam which tests a developer's foundational knowledge of integrating ML into tools and applications.

We consider neural networks with rational activation functions. The choice of the nonlinear activation function in deep learning architectures is crucial and heavily impacts the performance of a neural network. We establish optimal bounds in terms of network complexity and prove that rational neural networks approximate smooth functions more efficiently than ReLU networks. The flexibility and smoothness of rational activation functions make them an attractive alternative to ReLU, as we demonstrate with numerical experiments.

We consider direct modeling of underlying stock value movement sequences over time in the news-driven stock movement prediction. A recurrent state transition model is constructed, which better captures a gradual process of stock movement continuously by modeling the correlation between past and future price movements. By separating the effects of news and noise, a noisy random factor is also explicitly fitted based on the recurrent states. Results show that the proposed model outperforms strong baselines. Thanks to the use of attention over news events, our model is also more explainable. To our knowledge, we are the first to explicitly model both events and noise over a fundamental stock value state for news-driven stock movement prediction.