Federated Analytics & Learning Explained with Examples
Federated learning is proposed as an alternative to centralized machine learning since its client-server structure provides better privacy protection and scalability in real-world applications. It is experiencing a fast boom with the wave of distributed machine learning and ever-increasing privacy concerns. With the increased computing and communicating capabilities of edge and IoT devices, applying federated learning on heterogeneous devices to train machine learning models is becoming a trend. The federated analytics approach enables extracting insights from data residing on different systems without requiring the data to be brought to the central location. By leveraging these different data sources, federated analytics can provide powerful insights in relation to different areas such …
Graph Neural Networks Explained with Examples
Graph neural networks (GNNs) are a relatively new area in the field of deep learning. They arose from graph theory and machine learning, where the graph is a mathematical structure that models pairwise relations between objects. Graph Neural Networks are able to learn graph structures for different data sets, which means they can generalize well to new datasets – this makes them an ideal choice for many real-world problems like social network analysis or financial risk prediction. This post will cover some of the key concepts behind graph neural networks with the help of multiple examples. What are graph neural networks (GNNs)? Graphs are data structures which are used to …
Drug Discovery & Deep Learning: A Starter Guide
The drug discovery process is tedious, time-consuming, and expensive. A drug company has to identify the compounds that are most likely to be successful in drug development. The drug discovery process can take up to 15 years with an average cost of $1 billion for each drug candidate that passes clinical trials. With AI and deep learning models becoming more popular in recent years, scientists have been looking at ways to use these tools in the drug discovery process. This article will explore how deep learning generative models (GANs) could be used as a starting point for data scientists to get started drug discovery AI projects! What is the drug …
Supplier risk management & machine learning techniques
Supplier risk management (SRM) is a serious issue for procurement professionals. Suppliers can be unreliable, have poor quality products, or fail to meet specifications. In this blog post we will discuss AI / machine learning algorithms / techniques that you can use to manage supplier risk and make your procurement process more efficient. What is supplier risk management? Supplier Risk Management (SRM) also known as Supplier Risk Optimization (SRO), refers to policies and technology that enables organizations to manage risks related with suppliers. This can be done by analyzing data about past purchases from the supplier, predicting future risks related with purchases from this particular company. It’s crucial for procurement …
Key Deep Learning Techniques for Disease Diagnosis
The disease diagnosis process has been the same for decades- a physician would analyze symptoms, perform lab tests, and refer to medical diagnostic guidelines. However, recent advances in AI/machine learning / deep learning have made it possible for computers to diagnose or detect diseases with human accuracy. This blog post will introduce some machine learning / deep learning techniques that can be used by data scientists for training models related to disease diagnosis. What are different types of diseases that can be diagnosed using AI-based techniques? The following is a list of different types of diseases that can be diagnosed using machine learning or deep learning-based techniques: Cancer prognosis and …
Key Architectural Components of a Data Lake
Data lakes are data storage systems that allow data to be stored, managed and accessed in a way that is cost-effective and scalable. They can provide a significant competitive advantage for any organization by enabling data-driven decision-making, but they also come with challenges in architecture design. In this blog post, we will explore the different components of data lakes, including the data lake architecture. Before getting to learn about data lake architectural component, lets quickly recall what is a data lake. What is a data lake? A data lake is a data storage system that allows data to be stored, managed, and accessed in a way that is cost-effective and …
14 Python Automl Frameworks Data Scientists Can Use
In this post, you will learn about Automated Machine Learning (AutoML) frameworks for Python that can use to train machine learning models. For data scientists, especially beginners, who are unfamiliar with Automl, it is a tool designed to make the process of generating machine learning models in an automated manner, user-friendly, and less time-consuming. The goal of Automl is not just about making it easier for machine learning (ML) developers but also democratizing access to model development. What is AutoML? AutoML refers to automating some or all steps of building machine learning models, including selection and configuration of training data, tuning the performance metric(s), selecting/constructing features, training multiple models, evaluating …
Data Analytics – Different Career Options / Opportunities
Data analytics career paths span a wide range of career options, from data scientist to data engineer. Data scientists are often interested in what they can do with the data that is analyzed, while data engineers are more focused on the analysis itself. Whether you’re looking for a career as a data scientist, data analyst, ML engineer, or AI researcher, there’s something for everyone! In this blog post, we will different types of jobs and careers available to those interested in data analytics and data science. What are some of the career paths in data analytics? Here are different career paths for those interested in data analytics career: Data Scientists: …
Using Theory of Change to Design Data-driven Solutions
Have you ever wanted to design a solution for an issue but weren’t sure how to do it? One theory that can help is the theory of change. The theory of change provides a framework for designing solutions by focusing on the steps needed to achieve desired outcomes or results. It also helps identify what needs to happen in order for the solution to be implemented successfully and realizing the desired outcomes. The theory of change when combined with data-driven decision making can result in great impact. In order to design solutions that have an impact and are sustainable, it is important to understand the theory of change as well …
Top 50 Interview Questions for Beginner Data Scientists
What interview questions should a beginner data scientist prepare for? This is an important question that many interviewees have. If you are going for a data scientist interview and don’t know what interview questions will you be asked, this blog post has some of the common interview questions that will help you excel in your interview. These interview questions are perfect for beginners because they cover basic topics about data science and machine learning and how it works. We hope this list helps! What is the difference between AI, machine learning, deep learning? Do you know how machine learning works? How is machine learning different from statistical modeling techniques like linear …
How to Create & Detect Deepfakes Using Deep Learning
Deepfake are becoming a more common occurrence in today’s world. What is deepfake and how can you create it using deep learning? This blog post will help data scientists learn techniques for creating and detecting deepfakes, so they can stay ahead of this technology. A deepfake is a video or audio that alters reality by changing the way something appears. For example, someone could place your face onto someone else’s body in a video to make it seem like you were there when you really weren’t. There are many ways that one can detect if a photo has been manipulated with software such as Photoshop or Gimp. What is deepfake? …
50+ Machine learning & Deep learning Youtube Courses
In this post, you get an access to curated list of 50+ Youtube courses on machine learning, deep learning, NLP, optimization, computer vision, statistical learning etc. You may want to bookmark this page for quick reference and access to these courses. This page will be updated from time-to-time. Enjoy learning! Course title Course type URL MIT 6.S192: Deep Learning for Art, Aesthetics, and Creativity Deep learning https://www.youtube.com/playlist?list=PLCpMvp7ftsnIbNwRnQJbDNRqO6qiN3EyH AutoML – Automated Machine Learning AutoML https://ki-campus.org/courses/automl-luh2021 Probabilistic Machine Learning Machine learning https://www.youtube.com/playlist?list=PL05umP7R6ij1tHaOFY96m5uX3J21a6yNd Geometric Deep Learning Geometric deep learning https://www.youtube.com/playlist?list=PLn2-dEmQeTfQ8YVuHBOvAhUlnIPYxkeu3 CS224W: Machine Learning with Graphs Machine learning https://www.youtube.com/playlist?list=PLoROMvodv4rPLKxIpqhjhPgdQy7imNkDn MIT 6.S897 Machine Learning for Healthcare Machine learning https://www.youtube.com/playlist?list=PLUl4u3cNGP60B0PQXVQyGNdCyCTDU1Q5j Deep Learning and Combinatorial Optimization Deep …
Online AI News from Top Global Universities – List
In this post, you will get an access to a list of web pages representing latest news related to artificial intelligence from top universities across the globe. This page will be updated from time-to-time for including new pages from different universities across the globe. These URLs will be very useful for those machine learning / data science enthusiasts who want to keep tab on current news and events in the field of artificial intelligence. MIT Stanford Stanford university – Human-centered AI (HAI) Stanford university – Center for AI in medicine and imaging Stanford AI research and ideas Harvard university JHU Malone center for Engg. in healthcare Yale university Princeton university …
MOSAIKS for creating Climate Change Models
In this post, you will learn about the framework, MOSAIKS (Multi-Task Observation using Satellite Imagery & Kitchen Sinks) which can be used to create machine learning linear regression models for climate change. Here is the list of few prediction use cases which has already been tested with MOSAIKS and found to have high model performance: Forest cover Elevation Population density Nighttime lights Income Road length Housing price Crop yields Poverty mapping What is MOSAIKS? MOSAIKS provides a set of features created from Satellite imagery dataset. We are talking about 90TB of data gathered per day from 700+ satellites. These features can be combined with machine learning algorithms to address global …
Machine Learning for predicting Ice Shelves Vulnerability
In this post, you will learn about usage of machine learning for predicting ice shelves vulnerability. Before getting into the details, lets understand what is ice shelves vulnerability and how it is impacting global warming / climate change. What are ice shelves? Ice shelves are permanent floating sheets of ice that connect to a landmass. Most of the world’s ice shelves hug the coast of Antarctica. Ice from enormous ice sheets slowly oozes into the sea through glaciers and ice streams. If the ocean is cold enough, that newly arrived ice doesn’t melt right away. Instead it may float on the surface and grow larger as glacial ice behind it continues to flow into the …
Top Data Sources for Climate Change Research
In this post, you will get to learn about top data sources online from where you can learn and get data for doing research on climate change. Vitalflux is committing itself to AI and climate change research for next 15 years. You will get to learn about climate change and how data science / machine learning can be leveraged to tackle climate change in time to come. Without further ado, lets list down the data sources related to climate change research: United Kingdom’s Met Office Hadley Centre: Researchers at the Met Office Hadley Centre produce and maintain a range of gridded datasets of meteorological variables for use in climate monitoring and climate …
I found it very helpful. However the differences are not too understandable for me