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Deep Learning Prerequisites: Linear Regression in Python


Deep Learning Prerequisites: Linear Regression in Python, Data science: Learn linear regression from scratch and build your own working program in Python for data analysis. Created by Lazy Programmer Inc. Preview this Course  - GET COUPON CODE 100% Off Udemy Coupon . Free Udemy Courses . Online Classes

Latent gaze information in highly dynamic decision-tasks Artificial Intelligence

Digitization is penetrating more and more areas of life. Tasks are increasingly being completed digitally, and are therefore not only fulfilled faster, more efficiently but also more purposefully and successfully. The rapid developments in the field of artificial intelligence in recent years have played a major role in this, as they brought up many helpful approaches to build on. At the same time, the eyes, their movements, and the meaning of these movements are being progressively researched. The combination of these developments has led to exciting approaches. In this dissertation, I present some of these approaches which I worked on during my Ph.D. First, I provide insight into the development of models that use artificial intelligence to connect eye movements with visual expertise. This is demonstrated for two domains or rather groups of people: athletes in decision-making actions and surgeons in arthroscopic procedures. The resulting models can be considered as digital diagnostic models for automatic expertise recognition. Furthermore, I show approaches that investigate the transferability of eye movement patterns to different expertise domains and subsequently, important aspects of techniques for generalization. Finally, I address the temporal detection of confusion based on eye movement data. The results suggest the use of the resulting model as a clock signal for possible digital assistance options in the training of young professionals. An interesting aspect of my research is that I was able to draw on very valuable data from DFB youth elite athletes as well as on long-standing experts in arthroscopy. In particular, the work with the DFB data attracted the interest of radio and print media, namely DeutschlandFunk Nova and SWR DasDing. All resulting articles presented here have been published in internationally renowned journals or at conferences.

Learning Resources for Machine Learning - Programmathically


Familiarity with basic statistics and mathematical notation is helpful. An Introduction to Statistical Learning is one of the best introductory textbooks on classical machine learning techniques such as linear regression. It was the first machine learning book I've bought and has given me a great foundation. The explanations are held on a high level, so you don't need advanced math skills. Every chapter comes with code examples and labs in R. It is a great book to work through cover-to-cover. Get "An Introduction to Statistical Learning" on Amazon

Challenges of Artificial Intelligence -- From Machine Learning and Computer Vision to Emotional Intelligence Artificial Intelligence

Artificial intelligence (AI) has become a part of everyday conversation and our lives. It is considered as the new electricity that is revolutionizing the world. AI is heavily invested in both industry and academy. However, there is also a lot of hype in the current AI debate. AI based on so-called deep learning has achieved impressive results in many problems, but its limits are already visible. AI has been under research since the 1940s, and the industry has seen many ups and downs due to over-expectations and related disappointments that have followed. The purpose of this book is to give a realistic picture of AI, its history, its potential and limitations. We believe that AI is a helper, not a ruler of humans. We begin by describing what AI is and how it has evolved over the decades. After fundamentals, we explain the importance of massive data for the current mainstream of artificial intelligence. The most common representations for AI, methods, and machine learning are covered. In addition, the main application areas are introduced. Computer vision has been central to the development of AI. The book provides a general introduction to computer vision, and includes an exposure to the results and applications of our own research. Emotions are central to human intelligence, but little use has been made in AI. We present the basics of emotional intelligence and our own research on the topic. We discuss super-intelligence that transcends human understanding, explaining why such achievement seems impossible on the basis of present knowledge,and how AI could be improved. Finally, a summary is made of the current state of AI and what to do in the future. In the appendix, we look at the development of AI education, especially from the perspective of contents at our own university.

Artificial Intellgence -- Application in Life Sciences and Beyond. The Upper Rhine Artificial Intelligence Symposium UR-AI 2021 Artificial Intelligence

The TriRhenaTech alliance presents the accepted papers of the 'Upper-Rhine Artificial Intelligence Symposium' held on October 27th 2021 in Kaiserslautern, Germany. Topics of the conference are applications of Artificial Intellgence in life sciences, intelligent systems, industry 4.0, mobility and others. The TriRhenaTech alliance is a network of universities in the Upper-Rhine Trinational Metropolitan Region comprising of the German universities of applied sciences in Furtwangen, Kaiserslautern, Karlsruhe, Offenburg and Trier, the Baden-Wuerttemberg Cooperative State University Loerrach, the French university network Alsace Tech (comprised of 14 'grandes \'ecoles' in the fields of engineering, architecture and management) and the University of Applied Sciences and Arts Northwestern Switzerland. The alliance's common goal is to reinforce the transfer of knowledge, research, and technology, as well as the cross-border mobility of students.

Machine Learning and Deep Learning A-Z: Hands-On Python


Learn Machine Learning with Hands-On Examples What is Machine Learning? Machine Learning Terminology Evaluation Metrics for Python machine learning, Python Deep learning What are Classification vs Regression? Evaluating Performance-Classification Error Metrics Evaluating Performance-Regression Error Metrics Cross Validation and Bias Variance Trade-Off Use matplotlib and seaborn for data visualizations Machine Learning with SciKit Learn Linear Regression Algorithm Logistic Regresion Algorithm K Nearest Neighbors Algorithm Decision Trees And Random Forest Algorithm Support Vector Machine Algorithm Unsupervised Learning K Means Clustering Algorithm Hierarchical Clustering Algorithm Principal Component Analysis (PCA) Recommender System Algorithm Python, python machine learning and deep learning Machine Learning, machine learning A-Z Deep Learning, Deep learning a-z Machine learning is constantly being applied to new industries and new problems. Whether you're a marketer, video game designer, or programmer Machine learning describes systems that make predictions using a model trained on real-world data. Machine learning is being applied to virtually every field today. That includes medical diagnoses, facial recognition, weather forecasts, image processing It's possible to use machine learning without coding, but building new systems generally requires code. What is the best language for machine learning? Python is the most used language in machine learning. Engineers writing machine learning systems often use Jupyter Notebooks and Python together. Machine learning is generally divided between supervised machine learning and unsupervised machine learning. Python instructors on Udemy specialize in everything from software development to data analysis, and are known for their effective, friendly instruction What are the limitations of Python? Python is a widely used, general-purpose programming language, but it has some limitations.

Finetuning Transformer Models to Build ASAG System Artificial Intelligence

Research towards creating systems for automatic grading of student answers to quiz and exam questions in educational settings has been ongoing since 1966. Over the years, the problem was divided into many categories. Among them, grading text answers were divided into short answer grading, and essay grading. The goal of this work was to develop an ML-based short answer grading system. I hence built a system which uses finetuning on Roberta Large Model pretrained on STS benchmark dataset and have also created an interface to show the production readiness of the system. I evaluated the performance of the system on the Mohler extended dataset and SciEntsBank Dataset. The developed system achieved a Pearsons Correlation of 0.82 and RMSE of 0.7 on the Mohler Dataset which beats the SOTA performance on this dataset which is correlation of 0.805 and RMSE of 0.793. Additionally, Pearsons Correlation of 0.79 and RMSE of 0.56 was achieved on the SciEntsBank Dataset, which only reconfirms the robustness of the system. A few observations during achieving these results included usage of batch size of 1 produced better results than using batch size of 16 or 32 and using huber loss as loss function performed well on this regression task. The system was tried and tested on train and validation splits using various random seeds and still has been tweaked to achieve a minimum of 0.76 of correlation and a maximum 0.15 (out of 1) RMSE on any dataset.

How to Learn Machine Learning – Tips and Resources to Learn ML the Practical Way


How to Learn Machine Learning – Tips and Resources to Learn ML the Practical Way Yacine Mahdid A lot of people want to learn machine learning these days. But the daunting bottom-up curriculum that most ML teachers propose is enough discourage a lot of newcomers. In this tutorial I flip the curriculum upside down and will outline what I think is the fastest and easiest way to get a solid grasp of ML. Table of Contents Step 6: Repeat steps 0 to 5 This is a looping learning plan because the 6th step is actually a GOTO to Step 0! As a disclaimer, this curriculum might strange to you. But I've battle tested it when I was teaching machine learning to undergraduates at McGill University. I tried many iteration of this curriculum, starting with the theoretically superior bottom-up approach. But from experience, this pragmatic top-down approach is what gives the best results. One common critique I get is that people not starting with the basics, like statistics or linear algebra, will have a poor understanding of machine learning and they will not know what they are doing when modeling. In theory, yes, this is true and this is why I started teaching ML with the bottom up approach. In practice, this has never been the case. What actually ended up happening was that because the students knew how to do the high level modeling, they were much more inclined to delve into the low level stuff on their own as they saw the direct benefit it would bring to their higher level skills. This context that they were able to set for themselves wouldn't have been there if they'd started from the bottom – and this is where I believe most teachers lose their students. All that being said, let's jump into the actual learning plan!

Machine Learning & Deep Learning in Python & R


Free Coupon Discount - Machine Learning & Deep Learning in Python & R, Covers Regression, Decision Trees, SVM, Neural Networks, CNN, Time Series Forecasting and more using both Python & R Hot & New Created by Start-Tech Academy English [Auto] Preview this Udemy Course - GET COUPON CODE 100% Off Udemy Coupon . Free Udemy Courses . Online Classes

Jointly Modeling Heterogeneous Student Behaviors and Interactions Among Multiple Prediction Tasks Artificial Intelligence

Prediction tasks about students have practical significance for both student and college. Making multiple predictions about students is an important part of a smart campus. For instance, predicting whether a student will fail to graduate can alert the student affairs office to take predictive measures to help the student improve his/her academic performance. With the development of information technology in colleges, we can collect digital footprints which encode heterogeneous behaviors continuously. In this paper, we focus on modeling heterogeneous behaviors and making multiple predictions together, since some prediction tasks are related and learning the model for a specific task may have the data sparsity problem. To this end, we propose a variant of LSTM and a soft-attention mechanism. The proposed LSTM is able to learn the student profile-aware representation from heterogeneous behavior sequences. The proposed soft-attention mechanism can dynamically learn different importance degrees of different days for every student. In this way, heterogeneous behaviors can be well modeled. In order to model interactions among multiple prediction tasks, we propose a co-attention mechanism based unit. With the help of the stacked units, we can explicitly control the knowledge transfer among multiple tasks. We design three motivating behavior prediction tasks based on a real-world dataset collected from a college. Qualitative and quantitative experiments on the three prediction tasks have demonstrated the effectiveness of our model.