I'm looking for someone with a background in education and/or cognitive science to run an online seminar for non-rationalists on how humans learn things and how to efficiently teach a subject to others. A few examples of the sort of content I'm thinking of are: Ebbinghaus's research on memory, spaced repetition, the difference between shallow and deep learning of a subject. The exact content would be up to you. It would be a 1 hour seminar on May 29th, run via Zoom or a similar platform. If you're interested, please email me to discuss the details.
Suchitra is a professor by profession and learner by passion. She hold a PhD degree in Electronics and Communication Engineering with core competency in computer vision, pattern recognition, Artificial Intelligence,machine learning and deep learning. She is passionate about data science, Artificial Intelligence, natural language processing and firmly believes that future is Artificial Intelligence.
Deep learning is not like any other technology, but it is in many cases the only technology that can solve certain problems. We need to ensure that all people involved in the project have a common understanding of what is required, how the process works, and that we have a realistic view of what is possible with the tools at hand. In order to define AI, we must first define the concept of intelligence in general. Intelligence can be generally described as the ability to perceive information and retain it as knowledge to be applied towards adaptive behaviors within an environment or context. While there are many different definitions of intelligence, they all essentially involve learning, understanding, and the application of the knowledge learned to achieve one or more goals.
Single cell RNA sequencing (scRNA-Seq) is being widely used in biomedical research and generated enormous volume and diversity of data. The raw data contain multiple types of noise and technical artifacts, which need thorough cleaning. Existing denoising and imputation methods largely focus on a single type of noise (i.e., dropouts) and have strong distribution assumptions which greatly limit their performance and application. Here we design and develop the AutoClass model, integrating two deep neural network components, an autoencoder, and a classifier, as to maximize both noise removal and signal retention. AutoClass is distribution agnostic as it makes no assumption on specific data distributions, hence can effectively clean a wide range of noise and artifacts. AutoClass outperforms the state-of-art methods in multiple types of scRNA-Seq data analyses, including data recovery, differential expression analysis, clustering analysis, and batch effect removal. Importantly, AutoClass is robust on key hyperparameter settings including bottleneck layer size, pre-clustering number and classifier weight. We have made AutoClass open source at: https://github.com/datapplab/AutoClass . Single cell RNA sequencing (scRNA-Seq) is widely used in biomedical research. Here the authors develop a novel AI model-AutoClass, which effectively cleans a wide range of noise and artifacts in scRNA-Seq data and improves downstream analyses.
An optimizer is a function or an algorithm that customizes the attributes of the neural network, such as weights and discovering rate. Hence, it assists in decreasing the overall loss and also enhance the accuracy. The problem of picking the ideal weights for the version is an overwhelming job, as a deep learning version usually includes numerous parameters. It increases the requirement to pick an appropriate optimization algorithm for your application. You can utilize different optimizers to make changes in your weights as well as learning price.
Editor's note: The name of the NVIDIA Transfer Learning Toolkit was changed to NVIDIA TAO Toolkit in August 2021. All references to the name have been updated in this blog. You probably have a career. But hit the books for a graduate degree or take online certificate courses by night, and you could start a new career building on your past experience. Transfer learning is the same idea.
Udemy is one of the most popular MOOC-based e-learning platforms in the world. Udemy has a wide variety of Machine Learning courses. That's why in this article, I am going to share with you the 10 Best Udemy Courses for Machine Learning. So give your few minutes to this article and find out the Best Udemy Courses for Machine Learning. Now, without any further ado, let's get started- This is the Bestseller Course at Udemy.
In this tutorial, you'll see how to build a satellite image classifier using Python and Tensorflow. Satellite image classification is an important task when it comes down to agriculture, crop/forest monitoring, or even in urban scenarios, with planning tasks. We're going to use the EuroSAT dataset, which consists of Sentinel-2 satellite images covering…