As an important technique for modeling the knowledge states of learners, the traditional knowledge tracing (KT) models have been widely used to support intelligent tutoring systems and MOOC platforms. Driven by the fast advancements of deep learning techniques, deep neural network has been recently adopted to design new KT models for achieving better prediction performance. However, the lack of interpretability of these models has painfully impeded their practical applications, as their outputs and working mechanisms suffer from the intransparent decision process and complex inner structures. We thus propose to adopt the post-hoc method to tackle the interpretability issue for deep learning based knowledge tracing (DLKT) models. Specifically, we focus on applying the layer-wise relevance propagation (LRP) method to interpret RNN-based DLKT model by backpropagating the relevance from the model's output layer to its input layer. The experiment results show the feasibility using the LRP method for interpreting the DLKT model's predictions, and partially validate the computed relevance scores from both question level and concept level. We believe it can be a solid step towards fully interpreting the DLKT models and promote their practical applications in the education domain.

This is Deep Learning, Machine Learning, and Data Science Prerequisites: The Numpy Stack in Python (V2). The reason I made this course is because there is a huge gap for many students between machine learning "theory" and writing actual code. As I've always said: "If you can't implement it, then you don't understand it". Without basic knowledge of data manipulation, vectors, and matrices, students are not able to put their great ideas into working form, on a computer. This course closes that gap by teaching you all the basic operations you need for implementing machine learning and deep learning algorithms.

So you want to learn the Mathematics for Machine Learning? Well, for Machine Learning or Deep Learning and AI, a thorough mathematical understanding is not an option. I know the options out there; prerequisites and the skills you need to become successful in Machine Learning and AI. If you want to learn Machine Learning, these classes will help you to master the mathematical foundation required for writing programs and algorithms for Machine Learning, Deep Learning and AI. My goal in this piece is to help you find the resources to gain good intuition and get you the hands-on experience you need with coding neural nets, stochastic gradient descent, and principal component analysis.

We address multi-armed bandits (MAB) where the objective is to maximize the cumulative reward under a probabilistic linear constraint. For a few real-world instances of this problem, constrained extensions of the well-known Thompson Sampling (TS) heuristic have recently been proposed. However, finite-time analysis of constrained TS is challenging; as a result, only O(\sqrt{T}) bounds on the cumulative reward loss (i.e., the regret) are available. In this paper, we describe LinConTS, a TS-based algorithm for bandits that place a linear constraint on the probability of earning a reward in every round. We show that for LinConTS, the regret as well as the cumulative constraint violations are upper bounded by O(\log T) for the suboptimal arms. We develop a proof technique that relies on careful analysis of the dual problem and combine it with recent theoretical work on unconstrained TS. Through numerical experiments on two real-world datasets, we demonstrate that LinConTS outperforms an asymptotically optimal upper confidence bound (UCB) scheme in terms of simultaneously minimizing the regret and the violation.

New and Updated Deep Learning Institute Courses Launched, With More Training Delivery Partners Added. Starting this month, NVIDIA's Deep Learning Institute is offering instructor-led workshops that are delivered remotely via a virtual classroom. DLI provides hands-on training in AI, accelerated computing and accelerated data science to help developers, data scientists and other professionals solve their most challenging problems. These in-depth classes are taught by experts in their respective fields, delivering industry-leading technical knowledge to drive breakthrough results for individuals and organizations. DLI has already trained more than 200,000 developers globally and is growing quickly to bridge the digital skills gap worldwide.

Are you ready to start your path to becoming a Data Scientist! This comprehensive course will be your guide to learning how to use the power of Python to analyze data, create beautiful visualizations, and use powerful machine learning algorithms! Data Scientist has been ranked the number one job on Glassdoor and the average salary of a data scientist is over $120,000 in the United States according to Indeed! Data Science is a rewarding career that allows you to solve some of the world's most interesting problems! This course is designed for both beginners with some programming experience or experienced developers looking to make the jump to Data Science!

GBM models have been battle-tested as powerful models but have been tainted by the lack explainability. Typically data scientists look at variable importance plots but they are not enough to explain how a model works. To maximize adoption by the model user, use SHAP values to answer common explainability questions and build trust in your models. In this post, we will train a GBM model on a simple dataset and you will learn how to explain how the model works. The goal here is not to explain how the math works, but to explain to a non-technical user how the input variables are related to the output variable and how predictions are made.

This mask will be automatically applied to the face by using the facial landmarks (namely the points along the chin and nose) to compute where the mask will be placed.

The Gradient Boosting Machine is a powerful ensemble machine learning algorithm that uses decision trees. Boosting is a general ensemble technique that involves sequentially adding models to the ensemble where subsequent models correct the performance of prior models. AdaBoost was the first algorithm to deliver on the promise of boosting. Gradient boosting is a generalization of AdaBoosting, improving the performance of the approach and introducing ideas from bootstrap aggregation to further improve the models, such as randomly sampling the samples and features when fitting ensemble members. Gradient boosting performs well, if not the best, on a wide range of tabular datasets, and versions of the algorithm like XGBoost and LightBoost often play an important role in winning machine learning competitions. In this tutorial, you will discover how to develop Gradient Boosting ensembles for classification and regression.

In an endeavour to upskill the youth and promote e-learning during the COVID-19 lockdown, IIT Roorkee had launched an Advanced Certification Course on Deep Learning at Cloudxlab.com. It is an advanced course on deep learning and would cover cutting edge techniques applicable to audio processing, image processing, video processing, self-driving cars etc. This came in the wake of the current economic crisis which underscores the significance of technical skills to tackle the global slowdown. Further to the launch IIT Roorkee and CloudxLab will conduct a webinar on careers in AI and machine learning. The webinar will include faculty members from IIT Roorkee as well as members of the industry.