The terms Machine Learning and Deep Learning will be often put in the same basket, but what are they and what is their role? To understand these aspects, the first step is their positioning within the larger umbrella of AI (AGI). To address the limitations, characteristics and differences of these fields, it is necessary to know what an algorithm is since it is the raw material of artificial intelligence, and therefore machine learning and deep learning. An algorithm is a set of instructions that solve a problem. These instructions have to be finite, ordered and logical, in other words, they cannot be an infinite number of instructions.

Getting started with Pytorch using a cohesive, top down approach cheatsheet. This cheatsheet should be easier to digest than the official documentation and should be a transitional tool to get students and beginners to get started reading documentations soon. This article is being improved continuously. It is frequently updated and will remain under construction until it is significantly improved. Your feedback is appreciated hi@uniqtech.co Big news: we got published on Medium Machine Learning and Data Science homepage. Please clap and comment to show your support. This cheatsheet below is primarily narrative.

Recent news about the benefits of Machine Learning (ML) and Deep Learning (DL) has taken a slightly downbeat turn toward pointing out that there is a potential ecological cost associated with these systems. In particular, AI developers and AI researchers need to be mindful of the adverse and damaging carbon footprint that they are generating while crafting ML/DL capabilities. It is a so-called "green" or environmental wake-up call for AI that is worth hearing. Let's first review the nature of carbon footprints (CFPs) that are already quite familiar to all of us, such as the carbon belching transportation industry. A carbon footprint is usually expressed as the amount of carbon dioxide emissions spewed forth, including for example when you fly in a commercial plane from Los Angeles to New York, or when you drive your gasoline-powered car from Silicon Valley to Silicon Beach.

Pick up a magazine, scroll through the tech blogs, or simply chat with your peers at an industry conference. You'll quickly notice that almost everything coming out of the technology world seems to have some element of artificial intelligence or machine learning to it. The way artificial intelligence is discussed, it's starting to sound almost like propaganda. Here is the one true technology that can solve all of your needs! AI is here to save us all!

Ruth is a mathematician and data scientist specialising in operations research, machine learning and statistics. She holds a doctorate in mathematics for her research on dynamic resource allocation. She has nearly 20 years of project management, machine learning, programming, and solution development experience in the health, education, and private sectors. At Fireball, she leads the development team building an early bushfire notification platform that uses deep learning to detect fires within minutes of ignition. We're putting the people of Queensland front and centre to support Queensland AI Hub's mission - connecting Queensland's AI ecosystem.

On average, it takes ten years and costs $2.6 billion dollars to take a drug from the point of understanding the root cause of a disease to its availability in the marketplace. A large portion of this time and effort/cost is because we are literally looking for a needle in a haystack. We are looking for the one molecule that can turn off a disease at the molecular level in a solution space of between 10³⁰ to a google (yes, 10¹⁰⁰) synthetically feasible molecules. The chemical solution space is too vast to be efficiently screened for the particular molecule of interest. Pharmaceutical compound repositories contain only a fraction of the synthetically feasible molecules for research in a wet lab. Computational de novo drug design can be used as a tool to explore this vast chemical space and synthesize new, never before designed, molecules.

Different approaches have been employed to solve the growing need for accurate object detection models. More recently, with the popularization of the convolutional neural networks (CNN) and GPU-accelerated deep-learning frameworks, object- detection algorithms started being developed from a new perspective. CNNs such as R-CNN, Fast R-CNN, Faster R-CNN, R-FCN, SSD and Yolo have highly increased the performance standards on the field. Once you have trained your first object detector, the next step is to know its performance. Sure enough, you can see the model finds all the objects in the pictures you feed it.

After recently announcing Equity and Inclusion teams to investigate racial bias across their ... [ ] platforms, and undergoing an advertising boycott over alleged racial discrimination, is Facebook doing enough to tackle racial bias? Racial bias is a deeply rooted, insidious problem in our society, and that prejudice has already leaked into many of the algorithms we use today. With Facebook recently announcing their intention to "investigate" racial bias in their algorithms, the question arises about the role of Big Tech in perpetuating racially discriminatory AI - and what benefit they might receive by sitting on the fence at a time when the anti-racism movement is stronger than ever. As we put more trust in AI to make decisions for us, and data becomes increasingly intertwined with our daily lives, is it just a political move to simply investigate bias or improve diversity if algorithms continue to make racially biased decisions? Bias is an inherent problem to AI, particularly in the deep learning systems that power digital assistants, search engines and social media.

Emotional voice conversion aims to convert the emotion of speech from one state to another while preserving the linguistic content and speaker identity. The prior studies on emotional voice conversion are mostly carried out under the assumption that emotion is speaker-dependent. We consider that there is a common code between speakers for emotional expression in a spoken language, therefore, a speaker-independent mapping between emotional states is possible. In this paper, we propose a speaker-independent emotional voice conversion framework, that can convert anyone's emotion without the need for parallel data. We propose a VAW-GAN based encoder-decoder structure to learn the spectrum and prosody mapping. We perform prosody conversion by using continuous wavelet transform (CWT) to model the temporal dependencies. We also investigate the use of F0 as an additional input to the decoder to improve emotion conversion performance. Experiments show that the proposed speaker-independent framework achieves competitive results for both seen and unseen speakers.

A central role in process improvement is played by the process analyst [2], who is responsible for'monitoring, measuring, and providing feedback on the performance of a business process' [3, p.45]. The ongoing implementation of information systems in organisations, along with the subsequently enhanced availability of event log data, have enabled process analysts to discover as-is models of processes with process mining with relative ease [4]. However, the crucial challenge lies in identifying potential areas for process improvements (i.e., process analysis) with respect to a strategic goal [5]; this requires analytical capabilities such as Pareto or root cause analysis [2]. A business process can be defined as a'completely closed, timely, and logical sequence of activities' [6, p.3] that realises an outcome valuable to a customer [7]. The effectiveness (i.e., customer value) and efficiency (e.g., timely, logical sequence, resource utilisation) of a business process are monitored using key performance indicators (KPIs) as aggregated measures of process outcomes; in the context of BPM, these are often referred to as process performance indicators (PPIs) [8]. Thus, to improve a business process, it is essential for a process analyst to understand the relevance of individual process activities in terms of their impact on the dimensions expressed by these performance measures.