Udemy Online Course - Deep learning Calculus - Data Science - Machine Learning AI Mastering Calculus for Deep learning / Machine learning / Data Science / Data Analysis / AI using Python You start by learning the definition of function and move your way up for fitting the data to the function which is the core for any Machine learning, Deep Learning, Artificial intelligence, Data Science Application. Once you have mastered the concepts of this course, you will never be blind while applying the algorithm to your data, instead you have the intuition as how each code is working in background. What you'll learn Build Mathematical intuition especially Calculus required for Deep learning, Data Science and Machine Learning The Calculus intuition required to become a Data Scientist / Machine Learning / Deep learning Practitioner How to take their Data Science / Machine Learning / Deep learning career to the next level Hacks, tips & tricks for their Data Science / Machine Learning / Deep learning career Implement Machine Learning / Deep learning Algorithms better Learn core concept to Implement in Machine Learning / Deep learning Who this course is for: Data Scientists who wish to improve their career in Data Science. Deep learning / Machine learning practitioner who wants to take the career to next level Any one who wants to understand the underpinnings of Maths in Data Science, Machine Learning, Deep Learning and Artificial intelligence Any Data Science / Machine Learning / Deep learning enthusiast Any student or professional who wants to start or transition to a career in Data Science / Machine Learning / Deep learning Students who want to refresh and learn important maths concepts required for Machine Learning, Deep Learning & Data Science. Data Scientists who wish to improve their career in Data Science.

Artificial intelligence (AI) continues to be one of the most popular technologies among business executives in decades. Increased pressures on financial services organizations is driven in part by investors' enthusiasm for digital capabilities. This has fueled the need for well-planned transformation strategies to innovate in order to enhance the customer service experience. Early adopters of AI tools such as chatbots for sales, forecasting, functions automation are already benefiting from an increase level of efficiency. An understanding up-front of what AI can do for the business is crucial.

Learn how BigML Deepnets help you automatically find the best neural network to solve your supervised learning problems.

Every person has their own way of learning. What helped me break into data science was books. There is nothing like opening your mind to a world of knowledge condensed into a few hundred pages. There is a magic and allure to books that I have never found in any other medium of learning. "If you only read the books that everyone else is reading, you can only think what everyone else is thinking." Learning Data Science on your own can be a very daunting task! There are numerous ways to learn today – MOOCs, workshops, degrees, diplomas, articles, and so on.

Model Interpretability - Machine learning interpretability allows data scientists to explain machine learning models globally on all data, or locally on a specific data point using the state-of-art technologies in an easy-to-use and scalable fashion. Machine Learning interpretability incorporates technologies developed by Microsoft and proven third-party libraries (for example, SHAP and LIME). The SDK creates a common API across the integrated libraries and integrates Azure Machine Learning services. Using this SDK, you can explain machine learning models globally on all data, or locally on a specific data point using the state-of-art technologies in an easy-to-use and scalable fashion. Forecasting via AutomatedML, Automated ML advancements and AutomatedML supported on Databricks, CosmosDB & HDInsight – Automated ML automates parts of the ML workflow, reducing the time it takes to build ML models, freeing data scientists to focus on their important work, while simplifying ML and opening it up to a wider audience.

Domain-specific community question answering is becoming an integral part of professions. Finding related questions and answers in these communities can significantly improve the effectiveness and efficiency of information seeking. Stack Overflow is one of the most popular communities that is being used by millions of programmers. In this paper, we analyze the problem of predicting knowledge unit (question thread) relatedness in Stack Overflow. In particular, we formulate the question relatedness task as a multi-class classification problem with four degrees of relatedness. We present a large-scale dataset with more than 300K pairs. To the best of our knowledge, this dataset is the largest domain-specific dataset for Question-Question relatedness. We present the steps that we took to collect, clean, process, and assure the quality of the dataset. The proposed dataset Stack Overflow is a useful resource to develop novel solutions, specifically data-hungry neural network models, for the prediction of relatedness in technical community question-answering forums. We adopt a neural network architecture and a traditional model for this task that effectively utilize information from different parts of knowledge units to compute the relatedness between them. These models can be used to benchmark novel models, as they perform well in our task and in a closely similar task.

Encouraging sustainable mobility patterns is at the forefront of policymaking at all scales of governance as the collective consciousness surrounding climate change continues to expand. Not every community, however, possesses the necessary economic or socio-cultural capital to encourage modal shifts away from private motorized vehicles towards active modes. The current literature on `soft' policy emphasizes the importance of tailoring behavior change campaigns to individual or geographic context. Yet, there is a lack of insight and appropriate tools to promote active mobility and overcome transport disadvantage from the local community perspective. The current study investigates the promotion of walking and cycling adoption using a series of focus groups with local residents in two geographic communities, namely Chicago's (1) Humboldt Park neighborhood and (2) suburb of Evanston. The research approach combines traditional qualitative discourse analysis with quantitative text-mining tools, namely topic modeling and sentiment analysis. The analysis uncovers the local mobility culture, embedded norms and values associated with acceptance of active travel modes in different communities. We observe that underserved populations within diverse communities view active mobility simultaneously as a necessity and as a symbol of privilege that is sometimes at odds with the local culture. The mixed methods approach to analyzing community member discourses is translated into policy findings that are either tailored to local context or broadly applicable to curbing automobile dominance. Overall, residents of both Humboldt Park and Evanston envision a society in which multimodalism replaces car-centrism, but differences in the local physical and social environments would and should influence the manner in which overarching policy objectives are met.

In this work, we define a collaborative and privacy-preserving machine teaching paradigm with multiple distributed teachers. We focus on consensus super teaching. It aims at organizing distributed teachers to jointly select a compact while informative training subset from data hosted by the teachers to make a learner learn better. The challenges arise from three perspectives. First, the state-of-the-art pool-based super teaching method applies mixed-integer non-linear programming (MINLP) which does not scale well to very large data sets. Second, it is desirable to restrict data access of the teachers to only their own data during the collaboration stage to mitigate privacy leaks. Finally, the teaching collaboration should be communication-efficient since large communication overheads can cause synchronization delays between teachers. To address these challenges, we formulate collaborative teaching as a consensus and privacy-preserving optimization process to minimize teaching risk. We theoretically demonstrate the necessity of collaboration between teachers for improving the learner's learning. Furthermore, we show that the proposed method enjoys a similar property as the Oracle property of adaptive Lasso. The empirical study illustrates that our teaching method can deliver significantly more accurate teaching results with high speed, while the non-collaborative MINLP-based super teaching becomes prohibitively expensive to compute.

While the finite-sum setting is a particular case of expectation, the deterministic nature of the resulting cost function In this paper, we propose a unified view of drastically changes the performance guarantees an optimization gradient-based algorithms for stochastic convex method may achieve to solve (1). In particular, when an composite optimization by extending the concept algorithm is only allowed to access unbiased measurements of estimate sequence introduced by Nesterov. of the objective and gradient, it may be shown that the worstcase This point of view covers the stochastic gradient convergence rate in expected function value cannot be descent method, variants of the approaches better than O(1/k) in general, where k is the number of SAGA, SVRG, and has several advantages: (i) iterations (Nemirovski et al., 2009; Agarwal et al., 2012).

Composed of long chains of amino acids, proteins perform these myriad tasks by folding themselves into precise 3D structures that govern how they interact with other molecules. Because a protein's shape determines its function and the extent of its dysfunction in disease, efforts to illuminate protein structures are central to all of molecular biology -- and in particular, therapeutic science and the development of lifesaving and life-altering medicines. In recent years, computational methods have made significant strides in predicting how proteins fold based on knowledge of their amino acid sequence. If fully realized, these methods have the potential to transform virtually all facets of biomedical research. Current approaches, however, are limited in the scale and scope of the proteins that can be determined.