The past decade has seen an explosion of machine learning research and appli- cations; especially, deep learning methods have enabled key advances in many applicationdomains,suchas computervision,speechprocessing,andgameplaying. However, the performance of many machine learning methods is very sensitive to a plethora of design decisions, which constitutes a considerable barrier for new users. This is particularly true in the booming field of deep learning, where human engineers need to select the right neural architectures, training procedures, regularization methods, and hyperparameters of all of these components in order to make their networks do what they are supposed to do with sufficient performance. This process has to be repeated for every application. Even experts are often left with tedious episodes of trial and error until they identify a good set of choices for a particular dataset.
标签： Auto-Machine-Learning-Methods-Sys tems-Challenges
上传时间： 2020-06-10
上传用户：shancjb
Although state of the art in many typical machine learning tasks, deep learning algorithmsareverycostly interms ofenergyconsumption,duetotheirlargeamount of required computations and huge model sizes. Because of this, deep learning applications on battery-constrained wearables have only been possible through wireless connections with a resourceful cloud. This setup has several drawbacks. First, there are privacy concerns. Cloud computing requires users to share their raw data—images, video, locations, speech—with a remote system. Most users are not willing to do this. Second, the cloud-setup requires users to be connected all the time, which is unfeasible given current cellular coverage. Furthermore, real-time applications require low latency connections, which cannot be guaranteed using the current communication infrastructure. Finally, wireless connections are very inefficient—requiringtoo much energyper transferredbit for real-time data transfer on energy-constrained platforms.
标签： Embedded_Deep_Learning Algorithms
上传时间： 2020-06-10
上传用户：shancjb
Artificial Intelligence (AI) has undoubtedly been one of the most important buz- zwords over the past years. The goal in AI is to design algorithms that transform com- puters into “intelligent” agents. By intelligence here we do not necessarily mean an extraordinary level of smartness shown by superhuman; it rather often involves very basic problems that humans solve very frequently in their day-to-day life. This can be as simple as recognizing faces in an image, driving a car, playing a board game, or reading (and understanding) an article in a newspaper. The intelligent behaviour ex- hibited by humans when “reading” is one of the main goals for a subfield of AI called Natural Language Processing (NLP). Natural language 1 is one of the most complex tools used by humans for a wide range of reasons, for instance to communicate with others, to express thoughts, feelings and ideas, to ask questions, or to give instruc- tions. Therefore, it is crucial for computers to possess the ability to use the same tool in order to effectively interact with humans.
标签： Embeddings Processing Language Natural in
上传时间： 2020-06-10
上传用户：shancjb
General paradigm in solving a computer vision problem is to represent a raw image using a more informative vector called feature vector and train a classifier on top of feature vectors collected from training set. From classification perspective, there are several off-the-shelf methods such as gradient boosting, random forest and support vector machines that are able to accurately model nonlinear decision boundaries. Hence, solving a computer vision problem mainly depends on the feature extraction algorithm
标签： Convolutional Networks Neural Guide to
上传时间： 2020-06-10
上传用户：shancjb
Machinelearninghasgreatpotentialforimprovingproducts,processesandresearch.Butcomputers usually do not explain their predictions which is a barrier to the adoption of machine learning. This book is about making machine learning models and their decisions interpretable. After exploring the concepts of interpretability, you will learn about simple, interpretable models such as decision trees, decision rules and linear regression. Later chapters focus on general model- agnosticmethodsforinterpretingblackboxmodelslikefeatureimportanceandaccumulatedlocal effects and explaining individual predictions with Shapley values and LIME.
标签： interpretable-machine-learning
上传时间： 2020-06-10
上传用户：shancjb
The present era of research and development is all about interdisciplinary studies attempting to better comprehend and model our understanding of this vast universe. The fields of biology and computer science are no exception. This book discusses some of the innumerable ways in which computational methods can be used to facilitate research in biology and medicine—from storing enormous amounts of biological data to solving complex biological problems and enhancing the treatment of various diseases.
上传时间： 2020-06-10
上传用户：shancjb
Machine learning is about designing algorithms that automatically extract valuable information from data. The emphasis here is on “automatic”, i.e., machine learning is concerned about general-purpose methodologies that can be applied to many datasets, while producing something that is mean- ingful. There are three concepts that are at the core of machine learning: data, a model, and learning.
上传时间： 2020-06-10
上传用户：shancjb
modbus-demo, Public M_3W_D(50) As Long Public M_4W_D(500) As Long Public M_0W_B(100) As Long Public M_1W_B(100) As Long Public PLC_B(50) As Single Public PLC_C(50) As Long Public T1t(500) As Long Public M_3x(500) As Long Public M_4x(500) As Long Public MODEL As Long Public Party As Long
标签： modbusdemo
上传时间： 2020-11-09
上传用户：
Instrukcja programowania trójosiowego sterownika ruchu „przemysłowego wyświetlania ekranugo” V2.0 Model kontrolera : HYCNC-WPRS232-P3 , Wersja oprogramowania : V2.0 , Data : 2020-7-12 Autor : Guilin Hengyuan Technology Co., Ltd. ( Pan Lee )
标签： programowania Instrukcja HYCNC-WPRS sterownika 232 PLC ver P3 PL
上传时间： 2021-06-23
上传用户：DawidoCezaro
%this is an example demonstrating the Radial Basis Function %if you select a RBF that supports it (Gausian, or 1st or 3rd order %polyharmonic spline), this also calculates a line integral between two %points.
上传时间： 2021-07-02
上传用户：19800358905
