Create a computer vision program with python and OpenCV.

Introduction to Machine Learning:- What is Machine Learning?,

Sometime ago, the world's most affable and recognizable AI leader, Andrew Ng launched a specialization called AI for medicine through his MOOC institution, deeplearning.ai. I have always been a big fan of Andrew Ng, and it was he who had introduced me to the world of machine learning through his grainy Youtube videos of Stanford lectures back in 2012. I was very excited that finally, Andrew Ng has finally turned his attention to the critical shortage of AI experts in the medical field . Truth be told, AI in the medical world has not seen as much progress as other domains like personalized advertisements, recommendations, autonomous driving etc. There are lot of complex issues like data privacy, small sample sizes etc. which I would prefer to discuss in depth in another post.

Image recognition is the process of identifying and detecting an object or a feature in a digital image or video. Some of its applications include systems for factory automation, face recognition, booth monitoring, and security surveillance. Image recognition is embedded in technologies that enable students with learning disabilities to receive the education they need -- in a form they can perceive. Apps powered by computer vision offer text-to-speech options, which allow students with impaired vision or dyslexia to'read' the content. By employing image recognition, Jetpac caught visual cues in the photos and analyzed them to offer live data to its users.

Nearly five dozen students from Baruch College and the Zicklin School of Business got to show off their data-crunching skills recently when they participated in the Baruch College – Pitney Bowes Data Challenge, held on May 1. The winning team of Zicklin graduate students -- Drace (Yilei) Zhan (MS Statistics, '20), Nishtha Ram (MS Quantitative Methods & Modeling, '21), Huimin Chen (MS Information Systems, '21), Kang Li (MS QMM, '20), and Rosario Campoverde (MBA, '20) -- outperformed 50 other undergraduate and graduate students across Baruch and Zicklin to take first place. The competition was the culmination of a year-long collaboration among Pitney Bowes and the Paul H. Chook Department of Information Systems and Statistics, the Graduate Career Management Center, and the Starr Career Development Center. The partnership included seminars held throughout the year on machine learning, design thinking, marketing analytics, and other topics, presented by Pitney Bowes data scientists; and a free bootcamp on Python and AWS that was led by Zicklin professors. It was funded by a $10,000 grant from the NYC/CUNY Workforce Development Initiative.

In this course you will learn all about the mathematical optimization of linear programming for data science and business analytics. This course is very unique and have its own importance in their respective disciplines. The data science and business study heavily rely on optimization. Optimization is the study of analysis and interpreting mathematical data under the special rules and formula. The length of the course is more than 6 hours and there are total more than 4 sections in this course.

Training Deep Neural Networks (DNNs) is still highly time-consuming and compute-intensive. It has been shown that adapting a pretrained model may significantly accelerate this process. With a focus on classification, we show that current fine-tuning techniques make the pretrained models catastrophically forget the transferred knowledge even before anything about the new task is learned. Such rapid knowledge loss undermines the merits of transfer learning and may result in a much slower convergence rate compared to when the maximum amount of knowledge is exploited. We investigate the source of this problem from different perspectives and to alleviate it, introduce Fast And Stable Task-adaptation (FAST), an easy to apply fine-tuning algorithm. The paper provides a novel geometric perspective on how the loss landscape of source and target tasks are linked in different transfer learning strategies. We empirically show that compared to prevailing fine-tuning practices, FAST learns the target task faster and forgets the source task slower. The code is available at https://github.com/fvarno/FAST.

Continuous learning seeks to perform the learning on the data that arrives from time to time. While prior works have demonstrated several possible solutions, these approaches require excessive training time as well as memory usage. This is impractical for applications where time and storage are constrained, such as edge computing. In this work, a novel training algorithm, regularized subspace approximation classifier (RSAC), is proposed to achieve lightweight continuous learning. RSAC contains a feature reduction module and classifier module with regularization. Extensive experiments show that RSAC is more efficient than prior continuous learning works and outperforms these works on various experimental settings.

We study the inverse reinforcement learning (IRL) problem under the \emph{transition dynamics mismatch} between the expert and the learner. In particular, we consider the Maximum Causal Entropy (MCE) IRL learner model and provide an upper bound on the learner's performance degradation based on the $\ell_1$-distance between the two transition dynamics of the expert and the learner. Then, by leveraging insights from the Robust RL literature, we propose a robust MCE IRL algorithm, which is a principled approach to help with this mismatch issue. Finally, we empirically demonstrate the stable performance of our algorithm compared to the standard MCE IRL algorithm under transition mismatches in finite MDP problems.

Learning to read words aloud is a major step towards becoming a reader. Many children struggle with the task because of the inconsistencies of English spelling-sound correspondences. Curricula vary enormously in how these patterns are taught. Children are nonetheless expected to master the system in limited time (by grade 4). We used a cognitively interesting neural network architecture to examine whether the sequence of learning trials could be structured to facilitate learning. This is a hard combinatorial optimization problem even for a modest number of learning trials (e.g., 10K). We show how this sequence optimization problem can be posed as optimizing over a time varying distribution i.e., defining probability distributions over words at different steps in training. We then use stochastic gradient descent to find an optimal time-varying distribution and a corresponding optimal training sequence. We observed significant improvement on generalization accuracy compared to baseline conditions (random sequences; sequences biased by word frequency). These findings suggest an approach to improving learning outcomes in domains where performance depends on ability to generalize beyond limited training experience.