Due to the success of deep learning and its growing job market, students and researchers from many areas are getting interested in learning about deep learning technologies. Visualization has proven to be of great help during this learning process, while most current educational visualizations are targeted towards one specific architecture or use case. Unfortunately, recurrent neural networks (RNNs), which are capable of processing sequential data, are not covered yet, despite the fact that tasks on sequential data, such as text and function analysis, are at the forefront of deep learning research. Therefore, we propose exploRNN, the first interactively explorable, educational visualization for RNNs. exploRNN allows for interactive experimentation with RNNs, and provides in-depth information on their functionality and behavior during training. By defining educational objectives targeted towards understanding RNNs, and using these as guidelines throughout the visual design process, we have designed exploRNN to communicate the most important concepts of RNNs directly within a web browser. By means of exploRNN, we provide an overview of the training process of RNNs at a coarse level, while also allowing detailed inspection of the data-flow within LSTM cells. Within this paper, we motivate our design of exploRNN, detail its realization, and discuss the results of a user study investigating the benefits of exploRNN.

In this Guided Project, you will: How to get familiar with Machine Learning basics and how to start a model prediction using basic supervised Machine Learning models. Build, train, test and evaluate the performance of some models. In this 1-hour long project, you will be able to understand how to predict which passengers survived the Titanic shipwreck and make your first submission in an Machine Learning competition inside the Kaggle platform. Also, you as a beginner in Machine Learning applications, will get familiar and get a deep understanding of how to start a model prediction using basic supervised Machine Learning models. We will choose classifiers to learn, predict, and make an Exploratory Data Analysis (also called EDA).

A recently developed data science pathway for fourth-year radiology residents will help prepare the next generation of radiologists to lead the way into the era of artificial intelligence and machine learning (AI-ML), according to a special report published in Radiology: Artificial Intelligence. AI-ML has the potential to transform medicine by delivering better and more efficient healthcare. Applications in radiology are already arriving at a staggering rate. Yet organized AI-ML curricula are limited to a few institutions and formal training opportunities are lacking. Three senior radiology residents at Brigham and Women's Hospital (BWH) in Boston recently helped devise a data science pathway to provide a well-rounded introductory experience in AI-ML for fourth-year residents.

This is currently in an Early Bird Beta access, meaning we are still going to be continually adding content to the course (even though we are already at over 22 hours of content!) Since we're still adding content and taking student feedback as we complete the course through the start of 2021, students who enroll now will get access to a wide variety of benefits! NOTE: The additional content will focus on Machine Learning. We'll be going in-depth on all the major machine learning algorithms including both theory and practical examples with code. Welcome to the Learn Data Science and Machine Learning with R from A-Z Course! In this practical, hands-on course you'll learn how to program in R and how to use R for effective data analysis, visualization and how to make use of that data in a practical manner.

Analyzing data and building machine learning models is one thing. Packaging these analyses and models such that they are sharable is a different ball game altogether. This course aims at teaching you the fastest and easiest way to build and share data applications using Streamlit. You don't need any experience in building front-end applications for this.

Through the EpiMed Open Course initiative, the Institut pour l’Avancée des Biosciences (IAB) and Grenoble Alpes University have launched courses available on Youtube. Researcher Ekaterina Flin offers here an introduction to artificial intelligence for the analysis of biological data and the study of cancer. The course provides an overview of application areas and scientific questions […]

Humans, as the most powerful learners on the planet, have accumulated a lot of learning skills, such as learning through tests, interleaving learning, self-explanation, active recalling, to name a few. These learning skills and methodologies enable humans to learn new topics more effectively and efficiently. We are interested in investigating whether humans' learning skills can be borrowed to help machines to learn better. Specifically, we aim to formalize these skills and leverage them to train better machine learning (ML) models. To achieve this goal, we develop a general framework -- Skillearn, which provides a principled way to represent humans' learning skills mathematically and use the formally-represented skills to improve the training of ML models. In two case studies, we apply Skillearn to formalize two learning skills of humans: learning by passing tests and interleaving learning, and use the formalized skills to improve neural architecture search. Experiments on various datasets show that trained using the skills formalized by Skillearn, ML models achieve significantly better performance.

Advanced Neural Networks in R - A Practical Approach Boost your data science skills - learn to build and train complex neural network using the R program. Neural networks are powerful predictive tools that can be used for almost any machine learning problem with very good results. If you want to break into deep learning and artificial intelligence, learning neural networks is the first crucial step. This course contains four comprehensive sections. Learn to use multilayer perceptrons to make predictions for both categorical and continuous variables.

Welcome to KGP Talkie's Natural Language Processing course. It is designed to give you a complete understanding of Text Processing and Mining with the use of State-of-the-Art NLP algorithms in Python. We will learn Spacy in details and we will also explore the uses of NLP in real-life. This course covers the basics of NLP to advance topics like word2vec, GloVe, Deep Learning for NLP like CNN, ANN, and LSTM. I will also show you how you can optimize your ML code by using various tools of sklean in python.

In this Scikit-learn tutorial introduces you to machine learning in Python and introduction to Machine Learning with Scikit-Learn. It will explain how to use scikit-learn to do machine learning. Welcome to this video course on Scikit-Learn. This course will explain how to use scikit-learn to do machine learning. Methods to load Toy Datasets and exploring their feature names, number of instances and other details have been shown.