It is the extension of a Machine Learning, this course is for beginner who wants to learn the fundamental of deep learning and artificial intelligence. The course includes video explanation with introductions (basics), detailed theory and graphical explanations. Some daily life projects have been solved by using Python programming. Downloadable files of ebooks and Python codes have been attached to all the sections. The lectures are appealing, fancy and fast. They take less time to walk you through the whole content.

Artificial Intelligence (AI) simulates human thinking and behavior through intelligent machines. In essence, AI enables computers to learn from their experiences and carry out activities similar to those performed by humans. However, machine learning is the component of AI that allows machines to learn from the massive amounts of data they receive without being explicitly programmed. For instance, ML can carry out tasks that weren't specifically coded for it, such as employing statistical techniques to generate predictions. A wide range of businesses and applications are impacted and helped by machine learning in their day-to-day operations.

The use of Artificial Intelligence is expanding in a variety of fields. In this article, you will learn about the Top 7 Artificial Intelligence Applications. The use of artificial intelligence (AI) is expanding in a variety of fields. AI has come a long way in the last few years and may be used in almost any field. AI is defined by its capacity for systemic or programmed learning and thought.

This course takes you step-by-step through the process of becoming a professional AI artist. By the end of the programme you'll have a mastery Are you a creative looking to explore the possibilities of making art with artificial intelligence? If so, then this course is for you! With Midjourney, you'll learn how to create impressive AI art in minutes. You'll learn how to use Midjourney to create art, we'll go through all of tools available so you can master this revolutionary new medium.

This paper investigates a new online learning problem with doubly-streaming data, where the data streams are described by feature spaces that constantly evolve, with new features emerging and old features fading away. The challenges of this problem are two folds: 1) Data samples ceaselessly flowing in may carry shifted patterns over time, requiring learners to update hence adapt on-the-fly. 2) Newly emerging features are described by very few samples, resulting in weak learners that tend to make error predictions. A plausible idea to overcome the challenges is to establish relationship between the pre-and-post evolving feature spaces, so that an online learner can leverage the knowledge learned from the old features to better the learning performance on the new features. Unfortunately, this idea does not scale up to high-dimensional media streams with complex feature interplay, which suffers an tradeoff between onlineness (biasing shallow learners) and expressiveness(requiring deep learners). Motivated by this, we propose a novel OLD^3S paradigm, where a shared latent subspace is discovered to summarize information from the old and new feature spaces, building intermediate feature mapping relationship. A key trait of OLD^3S is to treat the model capacity as a learnable semantics, yields optimal model depth and parameters jointly, in accordance with the complexity and non-linearity of the input data streams in an online fashion. Both theoretical analyses and empirical studies substantiate the viability and effectiveness of our proposal.

Recent years have witnessed great progress on applying pre-trained language models, e.g., BERT, to information retrieval (IR) tasks. Hyperlinks, which are commonly used in Web pages, have been leveraged for designing pre-training objectives. For example, anchor texts of the hyperlinks have been used for simulating queries, thus constructing tremendous query-document pairs for pre-training. However, as a bridge across two web pages, the potential of hyperlinks has not been fully explored. In this work, we focus on modeling the relationship between two documents that are connected by hyperlinks and designing a new pre-training objective for ad-hoc retrieval. Specifically, we categorize the relationships between documents into four groups: no link, unidirectional link, symmetric link, and the most relevant symmetric link. By comparing two documents sampled from adjacent groups, the model can gradually improve its capability of capturing matching signals. We propose a progressive hyperlink predication ({PHP}) framework to explore the utilization of hyperlinks in pre-training. Experimental results on two large-scale ad-hoc retrieval datasets and six question-answering datasets demonstrate its superiority over existing pre-training methods.

Dental disease is one of the most common chronic diseases despite being largely preventable. However, professional advice on optimal oral hygiene practices is often forgotten or abandoned by patients. Therefore patients may benefit from timely and personalized encouragement to engage in oral self-care behaviors. In this paper, we develop an online reinforcement learning (RL) algorithm for use in optimizing the delivery of mobile-based prompts to encourage oral hygiene behaviors. One of the main challenges in developing such an algorithm is ensuring that the algorithm considers the impact of the current action on the effectiveness of future actions (i.e., delayed effects), especially when the algorithm has been made simple in order to run stably and autonomously in a constrained, real-world setting (i.e., highly noisy, sparse data). We address this challenge by designing a quality reward which maximizes the desired health outcome (i.e., high-quality brushing) while minimizing user burden. We also highlight a procedure for optimizing the hyperparameters of the reward by building a simulation environment test bed and evaluating candidates using the test bed. The RL algorithm discussed in this paper will be deployed in Oralytics, an oral self-care app that provides behavioral strategies to boost patient engagement in oral hygiene practices.

Organizations are increasingly adopting AI as a way to enable data-driven decision making, and as a great source of automated predictions that will potentially generate interesting savings or new sources of revenue. Even our personal devices such as smartphones or voice assistants are already leveraging AI technologies. However, the level of AI maturity within the companies varies a lot, as well as the needs for AI-savvy professionals. Reality is that not everyone needs to be an AI expert or a data scientist. Companies need other kinds of profiles for which at least AI knowledge is required, such as product managers or top executives managing innovation initiatives. This course is designed to give you an introduction to the amazing world of Artificial Intelligence.

The graph represents a network of 1,369 Twitter users whose tweets in the requested range contained "iiot machinelearning", or who were replied to or mentioned in those tweets. The network was obtained from the NodeXL Graph Server on Friday, 09 September 2022 at 11:21 UTC. The requested start date was Friday, 09 September 2022 at 00:01 UTC and the maximum number of tweets (going backward in time) was 7,500. The tweets in the network were tweeted over the 2-day, 10-hour, 8-minute period from Tuesday, 06 September 2022 at 13:51 UTC to Thursday, 08 September 2022 at 23:59 UTC. Additional tweets that were mentioned in this data set were also collected from prior time periods.

The graph represents a network of 1,430 Twitter users whose tweets in the requested range contained "#iiot", or who were replied to or mentioned in those tweets. The network was obtained from the NodeXL Graph Server on Tuesday, 01 February 2022 at 22:57 UTC. The requested start date was Tuesday, 01 February 2022 at 01:01 UTC and the maximum number of tweets (going backward in time) was 7,500. The tweets in the network were tweeted over the 3-day, 6-hour, 55-minute period from Friday, 28 January 2022 at 18:04 UTC to Tuesday, 01 February 2022 at 01:00 UTC. Additional tweets that were mentioned in this data set were also collected from prior time periods.