Daily, Weekly, Monthly, and Yearly Goal Tips to Guide a Self-Taught Data Scientist in 2023 (December 2022, 11 minutes) A good plan is key to reaching your learning goals, and Madison Hunter is here to help with a robust roadmap for building one that is both ambitious and sustainable. How to Explore Machine Learning and Natural Language Processing as a High School Student (July 2022, 12 minutes) This helpful guide by Carolyn Wang might be framed around her own experience as a high school student, but it's a helpful introduction to ML and NLP for aspiring practitioners of all ages. The Simple Things a Data Science Beginner Needs to Know (December 2022, 11 minutes) Ken Jee's recent resource is an accessible, up-to-date primer for anyone taking their first steps in data science this year. Here Are My 3 Suggestions for Newcomers (April 2022, 5 minutes) For all the independent learners out there who choose not to follow an established curriculum, Soner Yıldırım offers a few key insights based on his own experience as a self-taught data professional. A Brief Introduction to Neural Networks: A Regression Problem (December 2022, 12 minutes) How do you go about learning a complex technical topic from scratch?

Good morning Diane and everyone, I don't know about anyone else, but to me writing is hard as the devil! If you don't think writing is damn HARD, you must have great talent. I do so for different purposes. In this recent discussion about the value of having students learn to write, I tried to think back to how writing helped me in my life. In what way was it valuable to me?

Graph Neural Networks (GNNs) have shown satisfying performance on various graph learning tasks. To achieve better fitting capability, most GNNs are with a large number of parameters, which makes these GNNs computationally expensive. Therefore, it is difficult to deploy them onto edge devices with scarce computational resources, e.g., mobile phones and wearable smart devices. Knowledge Distillation (KD) is a common solution to compress GNNs, where a light-weighted model (i.e., the student model) is encouraged to mimic the behavior of a computationally expensive GNN (i.e., the teacher GNN model). Nevertheless, most existing GNN-based KD methods lack fairness consideration. As a consequence, the student model usually inherits and even exaggerates the bias from the teacher GNN. To handle such a problem, we take initial steps towards fair knowledge distillation for GNNs. Specifically, we first formulate a novel problem of fair knowledge distillation for GNN-based teacher-student frameworks. Then we propose a principled framework named RELIANT to mitigate the bias exhibited by the student model. Notably, the design of RELIANT is decoupled from any specific teacher and student model structures, and thus can be easily adapted to various GNN-based KD frameworks. We perform extensive experiments on multiple real-world datasets, which corroborates that RELIANT achieves less biased GNN knowledge distillation while maintaining high prediction utility.

Multi-task learning aims to boost the generalization performance of multiple related tasks simultaneously by leveraging information contained in those tasks. In this paper, we propose a multi-task learning framework, where we utilize prior knowledge about the relations between features. We also impose a penalty on the coefficients changing for each specific feature to ensure related tasks have similar coefficients on common features shared among them. In addition, we capture a common set of features via group sparsity. The objective is formulated as a non-smooth convex optimization problem, which can be solved with various methods, including gradient descent method with fixed stepsize, iterative shrinkage-thresholding algorithm (ISTA) with back-tracking, and its variation -- fast iterative shrinkage-thresholding algorithm (FISTA). In light of the sub-linear convergence rate of the methods aforementioned, we propose an asymptotically linear convergent algorithm with theoretical guarantee. Empirical experiments on both regression and classification tasks with real-world datasets demonstrate that our proposed algorithms are capable of improving the generalization performance of multiple related tasks.

This course introduces you to one of the main types of modeling families of supervised Machine Learning: Classification. You will learn how to train predictive models to classify categorical outcomes and how to use error metrics to compare across different models. The hands-on section of this course focuses on using best practices for classification, including train and test splits, and handling data sets with unbalanced classes. By the end of this course you should be able to: -Differentiate uses and applications of classification and classification ensembles -Describe and use logistic regression models -Describe and use decision tree and tree-ensemble models -Describe and use other ensemble methods for classification -Use a variety of error metrics to compare and select the classification model that best suits your data -Use oversampling and undersampling as techniques to handle unbalanced classes in a data set Who should take this course? This course targets aspiring data scientists interested in acquiring hands-on experience with Supervised Machine Learning Classification techniques in a business setting.

What does the future of education have in store for us all, and how will artificial intelligence change the teaching landscape? One key goal in teaching is to develop knowledge; to effect a permanent change in knowledge, and help students transfer this information into new scenarios. Many teachers have discovered ChatGPT, an artificial intelligence (AI) platform that generates text-based content. This tool enables an individual to pose a question or a statement. The software then responds, and at times, at great length!

I have been intending to send you an email expressing my gratitude for the work that you have done to create all of these data science courses in Machine Learning and Artificial Intelligence. I have been looking long and hard for courses that have mathematical rigor relative to the application of the ML & AI algorithms as opposed to just exhibit some'canned routine' and then viola here is your neural network or logistical regression. I have been looking long and hard for courses that have mathematical rigor relative to the application of the ML & AI algorithms as opposed to just exhibit some'canned routine' and then viola here is your neural network or logistical regression. Your courses are just what I have been seeking. I am a retired mathematician, statistician and Supply Chain executive from a large Fortune 500 company in Ohio.

With the trend of chatGPT there have been a lot of questions we have been asking ourselves like what is openAI,chatGPT alternatives among those questions and topic here is another topic worth discussing what on earth is a GPT? lets dive deep GPT, or Generative Pre-training Transformer, is a type of artificial intelligence (AI) technology that has revolutionized the field of natural language processing (NLP). Natural language processing (NLP) refers to the branch of computer science -- and more specifically, the branch of artificial intelligence or AI -- concerned with giving computers the ability to understand text and spoken words in much the same way human beings can . GPT has become widely used in a variety of applications, from language translation to text generation to language understanding it was developed by openAI . There are several types of GPT systems, including GPT-1, GPT-2, and GPT-3. These systems are distinguished by the size of their training data sets and the complexity of their models obviously.

Can black women exceed in the new world of artificial intelligence in 2023? The simply answer is that, one way more black women can gain knowledge in AI tech is by seeking out educational and professional resources that specialize in the field. For example, there are many online courses and tutorials available that can provide a comprehensive overview of the technology and its applications. Additionally, there are a variety of professional organizations, such as the Association for Black Women in Computing and the Black Women in AI group, that provide networking opportunities, resources, and scholarships to help black women gain knowledge and access to AI tech.

The monograph summarizes and analyzes the current state of development of computer and mathematical simulation and modeling, the automation of management processes, the use of information technologies in education, the design of information systems and software complexes, the development of computer telecommunication networks and technologies most areas that are united by the term Industry 4.0