InspirED Education Global’s Post

These certificates are designed for aspiring AI/ML engineers and the skills they need to succeed in the field.

MIT 6.S191: Introduction to Deep Learning MIT's introductory program on deep learning methods with applications to natural language processing, computer vision, biology, and more Eight lectures and three software labs. Link: https://lnkd.in/ejA59uw8 #mit #ml #ai #deeplearning #artificialintelligence #tech #technology #education

It has been roughly a decade since the first papers using #deepneuralnetworks (#DNNs) for radar applications were published. This issue of IEEE Transactions for #Aerospace and #ElectronicSystems (TAES) includes a special section on the topic #Deeplearning has revolutionized almost every technical area, from computer vision and natural language processing to health, finance, and biology—any field where data can be analyzed to provide insight. However, in #radar applications, deep learning faces unique challenges due to the phenomenology of radio frequency (RF) propagation that creates essential differences in the data itself and impacts the design of DNNs for radar signal analysis

Recently, I defended my Ph.D. dissertation titled "Automation of HAZOP Study of Chemical Processes Using Machine Learning." In this project, I utilized natural language processing and machine learning for HAZOP study automation, comparing these methods to other approaches such as rule-based, model-based, case-based, ontology, and graph theory. I am deeply grateful to Professors Rashtchian and Boroushaki for their invaluable assistance in completing this thesis. #chemicalengineering #processsafety #processdesign #machinelearning #naturallangugeprocess #psm #ai #artificialintelligence #safety My google scholar: https://lnkd.in/dsyivfji

🎓 I am pleased to announce that I have successfully completed the Post Graduate Program in Artificial Intelligence and Machine Learning: Business Applications from the University of Texas at Austin, presented by the McCombs School of Business. This program has profoundly enriched my understanding and skills in the latest AI and ML technologies, focusing on their applications within business environments. The curriculum provided comprehensive insights into data analysis, neural networks, natural language processing, and more, preparing me to effectively integrate these technologies into business strategies. I am enthusiastic about the opportunities to apply this advanced knowledge to foster innovation and efficiency in future projects. https://lnkd.in/g6AEpn5Y #ArtificialIntelligence #MachineLearning #McCombs #BusinessAnalytics #ProfessionalGrowth #UTAustin

【𝐈𝐧𝐟𝐨𝐫𝐦𝐚𝐭𝐢𝐨𝐧-𝐓𝐡𝐞𝐨𝐫𝐞𝐭𝐢𝐜 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐌𝐞𝐞𝐭𝐬 𝐍𝐞𝐮𝐫𝐚𝐥 𝐍𝐞𝐭𝐰𝐨𝐫𝐤𝐬📊】   Information-theoretic learning encompasses a framework that leverages principles from information theory to guide the learning process within machine learning algorithms. By incorporating these principles, deep learning algorithms can achieve enhanced feature extraction, improved model interpretability, and increased resilience against heavy-tailed noise and outliers. In the era of AI, convolutional neural networks (CNNs) have experienced remarkable popularity across diverse domains, including computer vision, natural language processing, and bioinformatics. However, in certain scenarios where random noise is present in the data, many studies often assume that the noise conforms to a Gaussian distribution, which may not always hold true. Making such an assumption can lead to suboptimal performance of CNNs. The research led by 𝐃𝐫 𝐉𝐮𝐧 𝐅𝐀𝐍, Associate Professor in the Department of Mathematics, and his PhD student, Mr Yingqiao ZHANG, delve into the realm of deep information-theoretic learning and specifically investigates the generalization error of deep CNNs when faced with robust regression problems.   🔎Read more: bityl.co/PYcw   #HKBU #HKBUScience #HKBUSCI #MATH #Mathematics #浸會大學 #浸大理學院 #數學系

TECHEM-360 AI TIPS🦾🦾🦾💫💫 Do you know the founder of AI as a field?👇 The phrase "Artificial Intelligence" was first used by John McCarthy in the year 1955 . John McCarthy was a young assistant professor of Mathematics at Dartmouth College who organised a group to clarify and develop ideas on thinking machines. Today it is widely accepted that the Dartmouth summer Research project on AI (to discuss automatic computers, natural Language processing, neural networks, theory of computation, abstraction and creativity) in 1956 is the founding event of AI as a field.🥺 Artificial intelligence has become one of the fascinating technologies and has become common in application in diverse areas of Science such as Chemistry, Physics, Biochemistry etc. #AI #Datascience #DataAnalytics #TECHEM360

Yes, I dropped out of the Penn State World Campus GIS program to do AI back in 2022. I saw that USF Computer Science and Engineering was offering folks a second shot at glory with their MSCS Pathway program and immediately cleared my calendar. Two years later, Penn State is offering a graduate certificate that competes with the online AI graduate certificate program I am in currently. It looks fine if you plan on hiking it up to State College, PA a few times for the CS job fair. The problem for aspiring GeoAI practitioners is that this program does look gated to CS/Engineering folks. You could do an MSCS bridge program and apply and probably get in there. I did some homework on Penn State's MS AI while investigating if I could get away with doing one #GeoAI thesis and get two master's degrees. Wishful thinking on my part. Fun fact: Penn State will only accept 9 transfer credits max if you are coming from another CS program and will only take Bs or better. For my aspiring GeoAI readers: these CS graduate certificates are merely your ticket into the CS career fair where you can sell your GitHub to shoppers. You actually have to build stuff on top of the coursework. It's kind of insane. The Computer Vision certificate looks like it would pair well with the Remote Sensing certificate offered by the GIS program. If I were unemployed and going to YOLO my savings on Penn State AI, I would probably be building an imagery pipeline in the cloud and flying over to State College, PA a few times a year to sell the pipeline to my target firms. The online job hunt sucks that much. That time is better spent building stuff and talking to human beings. Thankfully, I think you can get similar results from your local CS department. I am clinging on to my 3.0 AI GPA by the skin of my teeth here at USF in Tampa and am amazed that the ship is finally sailing the right direction after years of drifting. Not all of my target firms recruit here. Nobody seems terribly impressed by the brand or the ranking. Who cares? In-person is totally the way to go - just so that you are not going into debt repeatedly flying to another corner of the country and walking away empty-handed. I just happened to luck out and enroll in an online program close by and do the in-person stuff. It's really unique meeting and networking with folks from other disciplines trying to stay ahead of the curve. I wish everyone stuck in their GIS careers had this experience and would at least give this game plan some serious consideration.

"In recent years, artificial intelligence technologies, especially machine learning algorithms, have made great strides. These technologies have enabled unprecedented efficiency in tasks such as image recognition, natural language generation and processing, and object detection, but such outstanding functionality requires substantial computational power as a foundation. Current computational resources are approaching their limit, so effectively reducing the training cost of machine learning models and improving their training efficiency is an important issue in the research field." #opticalneuralnetworks