First, The Data! • Over 1 million unique chat users • Over 150 million conversation data points focused on mental health and emotional wellbeing • Over 1.1 million manually labeled data points by our highly trained moderators • Over 1.2 million data points on moods and emotions • Over 480 thousand resources and services matched in real-time (patented) • Over 10 real-time AI engines Our own corpus and language models (patented) • Double feedback loop from users and moderators for ML and fine tuning • No hate/racism/trolls or other biases in the data, as all user chats supervised by our moderators Supportiv is driven by building a peer-to-peer mental well-being platform that helps users with their emotional needs 24/7/365 affordably. We are seeking a highly-analytical impact-oriented Ph.D. student to join our Data Science team. The role will serve as a core member within the team, focused on applying artificial intelligence (AI) to solve real business problems, along with analytics to drive insights from the user interactions on the platform to guide data-driven product development and business decisions. The Ph.D. student will work cross-functionally between various teams at Supportiv. Please include your resume and transcripts.

You're looking for a complete Artificial Neural Network (ANN) course that teaches you everything you need to create a Neural Network model in Python, right? A Verifiable Certificate of Completion is presented to all students who undertake this Neural networks course. If you are a business Analyst or an executive, or a student who wants to learn and apply Deep learning in Real world problems of business, this course will give you a solid base for that by teaching you some of the most advanced concepts of Neural networks and their implementation in Python without getting too Mathematical. This course covers all the steps that one should take to create a predictive model using Neural Networks. Most courses only focus on teaching how to run the analysis but we believe that having a strong theoretical understanding of the concepts enables us to create a good model .

Many of the online courses teach you to code but not the theory/way of thinking behind it why would we choose a while but not a for loop, why should we pass 2 parameters to a function but not only one? We provide a platform for thousands of people to expand the understanding of programming and computer science. Founded in 2013 our mission is to spread the love for programming. To achieve this, we're working hard on providing content that will help people build a solid foundation in those subjects. This course will help you to master the foundation and know-how to solve problems with Python code.

With the recent global and regional socio-economic disruptions caused by the pandemic, industries such as retail, consumer products, manufacturing, pharmaceutical, and life sciences all struggle to align production and stocking with rapidly shifting purchasing demands. At the same time, some channels have surged ahead: online retailers, delivery services, and pharmacies are thriving. In this latest Data Science Central podcast, we discuss how injecting AI into existing business intelligence solutions can greatly enhance the ability of organizations to predict future demand for goods, even in uncertain and dynamic times.

First, The Data! • Over 1 million unique chat users • Over 150 million conversation data points focused on mental health and emotional wellbeing • Over 1.1 million manually labeled data points by our highly trained moderators • Over 1.2 million data points on moods and emotions • Over 480 thousand resources and services matched in real-time (patented) • Over 10 real-time AI engines Our own corpus and language models (patented) • Double feedback loop from users and moderators for ML and fine tuning • No hate/racism/trolls or other biases in the data, as all user chats supervised by our moderators Supportiv is driven by building a peer-to-peer mental well-being platform that helps users with their emotional needs 24/7/365 affordably. We are seeking a highly-analytical impact-oriented Ph.D. student to join our Data Science team. The role will serve as a core member within the team, focused on applying artificial intelligence (AI) to solve real business problems, along with analytics to drive insights from the user interactions on the platform to guide data-driven product development and business decisions. The Ph.D. student will work cross-functionally between various teams at Supportiv. We are not just a highly ambitious technology company with a soul.

Role requiring'No experience data provided' months of experience in None Combine two of the fastest-growing fields on the planet with a culture of performance, collaboration and opportunity and this is what you get.

In this advanced course on deep reinforcement learning, you will learn how to implement policy gradient, actor critic, deep deterministic policy gradient (DDPG), twin delayed deep deterministic policy gradient (TD3), and soft actor critic (SAC) algorithms in a variety of challenging environments from the Open AI gym. There will be a strong focus on dealing with environments with continuous action spaces, which is of particular interest for those looking to do research into robotic control with deep reinforcement learning. Rather than being a course that spoon feeds the student, here you are going to learn to read deep reinforcement learning research papers on your own, and implement them from scratch. You will learn a repeatable framework for quickly implementing the algorithms in advanced research papers. Mastering the content in this course will be a quantum leap in your capabilities as an artificial intelligence engineer, and will put you in a league of your own among students who are reliant on others to break down complex ideas for them.

Experience with manipulating some datasets, even if in Excel only, will help. The course is suited to busy professionals and students who want to learn quickly and get to the important points without wasting time on long, boring videos. Also ideal for self-learners who need a solid "jump-start" for career acceleration, and interested in quickly working on real-life problems. Be able to complete machine learning projects from beginning to end, just like a professional working in the industry, for projects ranging from NLP, clustering, regression to computer vision. Learn how to learn and become independent to solve any future problems.

In the past decade, we have observed a growing interest in using technologies such as artificial intelligence (AI), machine learning, and chatbots to provide assistance to language learners, especially in second language learning. By using AI and natural language processing (NLP) and chatbots, we can create an intelligent self-learning environment that goes beyond multiple-choice questions and/or fill in the blank exercises. In addition, NLP allows for learning to be adaptive in that it offers more than an indication that an error has occurred. It also provides a description of the error, uses linguistic analysis to isolate the source of the error, and then suggests additional drills to achieve optimal individualized learning outcomes. In this paper, we present our approach for developing an Artificial Intelligence-based Arabic Language and Speech Tutor (AI-ALST) for teaching the Moroccan Arabic dialect. The AI-ALST system is an intelligent tutor that provides analysis and assessment of students learning the Moroccan dialect at University of Arizona (UA). The AI-ALST provides a self-learned environment to practice each lesson for pronunciation training. In this paper, we present our initial experimental evaluation of the AI-ALST that is based on MFCC (Mel frequency cepstrum coefficient) feature extraction, bidirectional LSTM (Long Short-Term Memory), attention mechanism, and a cost-based strategy for dealing with class-imbalance learning. We evaluated our tutor on the word pronunciation of lesson 1 of the Moroccan Arabic dialect class. The experimental results show that the AI-ALST can effectively and successfully detect pronunciation errors and evaluate its performance by using F_1-score, accuracy, precision, and recall.

The National Digital Library of India was conceptualized with an aim to bring equity of access to educational resources for every Indian through a single window access mechanism.