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Complete Machine Learning and Data Science: Zero to Mastery

#artificialintelligence

Created by Andrei Neagoie English [Auto] Students also bought The Complete Web Developer in 2020: Zero to Mastery Deno: The Complete Guide Zero to Mastery Learning to Learn [Efficient Learning]: Zero to Mastery Break Away: Programming And Coding Interviews How to Make Films With an iPhone: For Beginners Master the Coding Interview: Data Structures Algorithms Preview this course GET COUPON CODE Description This is a brand new Machine Learning and Data Science course just launched January 2020 and updated this month with the latest trends and skills! Become a complete Data Scientist and Machine Learning engineer! Join a live online community of 270,000 engineers and a course taught by industry experts that have actually worked for large companies in places like Silicon Valley and Toronto. Graduates of Andrei's courses are now working at Google, Tesla, Amazon, Apple, IBM, JP Morgan, Facebook, other top tech companies. Learn Data Science and Machine Learning from scratch, get hired, and have fun along the way with the most modern, up-to-date Data Science course on Udemy (we use the latest version of Python, Tensorflow 2.0 and other libraries).


Complete Machine Learning and Data Science: Zero to Mastery

#artificialintelligence

Created by Andrei Neagoie, Daniel Bourke Students also bought Machine Learning A-Z: Hands-On Python & R In Data Science Data Science A-Z: Real-Life Data Science Exercises Included Machine Learning, Data Science and Deep Learning with Python Statistics for Data Science and Business Analysis Data Science 2020: Complete Data Science & Machine Learning Preview this Udemy Course GET COUPON CODE Description This is a brand new Machine Learning and Data Science course just launched January 2020 and updated this month with the latest trends and skills! Become a complete Data Scientist and Machine Learning engineer! Join a live online community of 270,000 engineers and a course taught by industry experts that have actually worked for large companies in places like Silicon Valley and Toronto. Graduates of Andrei's courses are now working at Google, Tesla, Amazon, Apple, IBM, JP Morgan, Facebook, other top tech companies. Learn Data Science and Machine Learning from scratch, get hired, and have fun along the way with the most modern, up-to-date Data Science course on Udemy (we use the latest version of Python, Tensorflow 2.0 and other libraries).


AAAI 2020 A Turning Point for Deep Learning?

#artificialintelligence

This is an updated version. The Godfathers of AI and 2018 ACM Turing Award winners Geoffrey Hinton, Yann LeCun, and Yoshua Bengio shared a stage in New York on Sunday night at an event organized by the Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI 2020). The trio of researchers have made deep neural networks a critical component of computing, and in individual talks and a panel discussion they discussed their views on current challenges facing deep learning and where it should be heading. Introduced in the mid 1980s, deep learning gained traction in the AI community the early 2000s. The year 2012 saw the publication of the CVPR paper Multi-column Deep Neural Networks for Image Classification, which showed how max-pooling CNNs on GPUs could dramatically improve performance on many vision benchmarks; while a similar system introduced months later by Hinton and a University of Toronto team won the large-scale ImageNet competition by a significant margin over shallow machine learning methods.


AAAI 2020 A Turning Point for Deep Learning? Hinton, LeCun, and Bengio Might Have Different Approaches

#artificialintelligence

This is an updated version. The Godfathers of AI and 2018 ACM Turing Award winners Geoffrey Hinton, Yann LeCun, and Yoshua Bengio shared a stage in New York on Sunday night at an event organized by the Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI 2020). The trio of researchers have made deep neural networks a critical component of computing, and in individual talks and a panel discussion they discussed their views on current challenges facing deep learning and where it should be heading. Introduced in the mid 1980s, deep learning gained traction in the AI community the early 2000s. The year 2012 saw the publication of the CVPR paper Multi-column Deep Neural Networks for Image Classification, which showed how max-pooling CNNs on GPUs could dramatically improve performance on many vision benchmarks; while a similar system introduced months later by Hinton and a University of Toronto team won the large-scale ImageNet competition by a significant margin over shallow machine learning methods.


FPGA Arithmetic for Machine Learning

#artificialintelligence

Applications are invited for a PhD studentship, to be undertaken at Imperial College London (Electrical and Electronic Engineering Department). This studentship will form part of a newly established International Centre for Spatial Computational Learning http://spatialml.net, and a supervisory team will be allocated based on the student's interest from the Imperial College supervisors participating in the Centre. This is an exciting cutting-edge project involving close collaboration between Imperial College (UK), the University of California Los Angeles (USA), the University of Toronto (Canada), and the University of Southampton (UK). The successful candidate will be based at Imperial but will have the opportunity to travel frequently to America to attend research meetings and for a placement period at either UCLA or Toronto. Traditional deep learning has been based on the idea of large-scale linear arithmetic units, effectively computing matrix-matrix multiplication, combined with nonlinear activation functions.


Feature Selection and Feature Extraction in Pattern Analysis: A Literature Review

arXiv.org Machine Learning

Pattern analysis often requires a pre-processing stage for extracting or selecting features in order to help the classification, prediction, or clustering stage discriminate or represent the data in a better way. The reason for this requirement is that the raw data are complex and difficult to process without extracting or selecting appropriate features beforehand. This paper reviews theory and motivation of different common methods of feature selection and extraction and introduces some of their applications. Some numerical implementations are also shown for these methods. Finally, the methods in feature selection and extraction are compared.


A semi-supervised deep residual network for mode detection in Wi-Fi signals

arXiv.org Machine Learning

Due to their ubiquitous and pervasive nature, Wi-Fi networks have the potential to collect large-scale, low-cost, and disaggregate data on multimodal transportation. In this study, we develop a semi-supervised deep residual network (ResNet) framework to utilize Wi-Fi communications obtained from smartphones for the purpose of transportation mode detection. This framework is evaluated on data collected by Wi-Fi sensors located in a congested urban area in downtown Toronto. To tackle the intrinsic difficulties and costs associated with labelled data collection, we utilize ample amount of easily collected low-cost unlabelled data by implementing the semi-supervised part of the framework. By incorporating a ResNet architecture as the core of the framework, we take advantage of the high-level features not considered in the traditional machine learning frameworks. The proposed framework shows a promising performance on the collected data, with a prediction accuracy of 81.8% for walking, 82.5% for biking and 86.0% for the driving mode.


Exemplar-Centered Supervised Shallow Parametric Data Embedding

arXiv.org Machine Learning

Metric learning methods for dimensionality reduction in combination with k-Nearest Neighbors (kNN) have been extensively deployed in many classification, data embedding, and information retrieval applications. However, most of these approaches involve pairwise training data comparisons, and thus have quadratic computational complexity with respect to the size of training set, preventing them from scaling to fairly big datasets. Moreover, during testing, comparing test data against all the training data points is also expensive in terms of both computational cost and resources required. Furthermore, previous metrics are either too constrained or too expressive to be well learned. To effectively solve these issues, we present an exemplar-centered supervised shallow parametric data embedding model, using a Maximally Collapsing Metric Learning (MCML) objective. Our strategy learns a shallow high-order parametric embedding function and compares training/test data only with learned or precomputed exemplars, resulting in a cost function with linear computational complexity for both training and testing. We also empirically demonstrate, using several benchmark datasets, that for classification in two-dimensional embedding space, our approach not only gains speedup of kNN by hundreds of times, but also outperforms state-of-the-art supervised embedding approaches.