The VectorBlox platform is a software development kit (SDK) that enables machine learning (ML) inference on PolarFire® FPGAs. This SDK has a variety of tools to convert and run various typical neural network models without reprogramming the FPGA. Since PolarFire FPGA is the mid-range FPGA with the lowest power consumption in the industry, VectorBlox SDK can implement a low-power inference solution, and its operating power consumption is reduced by 2-3 times compared with similar products with similar computing power. Leading solutions include a dual-pipeline convolutional neural network (CNN)-based face recognition solution that also does not require any FPGA programming. This can help you speed up time to market and reduce development costs for well-defined use cases, such as intruder detection for military/commercial or home surveillance customers.
Total of 1 lessons32 minutes and 53 seconds
Introduces the basic concepts of heterogeneous computing, the basic development methods of OpenCL and the implementation of artificial intelligence applications.
Total of 6 lessons1 hours and 46 minutes and 30 seconds
Total of 52 lessons20 hours and 23 minutes and 45 seconds
There are many kinds of intelligent phenomena in nature, such as the cognitive process of the human brain, bird migration, and lions foraging. So can machines be programmed to imitate the thought processes of these animals? The answer is yes, and this technology that uses programs to simulate natural phenomena in nature to serve the control process is intelligent control technology. It mainly talks about expert control, fuzzy control, artificial neural network control, genetic algorithm, and teaches MATLAB programming and SIMULINK knowledge to realize the above knowledge.
Total of 56 lessons10 hours and 28 minutes and 53 seconds
Intelligent controls refer to automatic control technology that can autonomously drive intelligent machines to achieve control goals without human intervention. The development of control theory has a history of more than 100 years. It has gone through the development stages of "classical control theory" and "modern control theory" and has entered the stage of "large system theory" and "intelligent control theory". This video talks about neural networks, fuzzy mathematics and fuzzy control, and intelligent optimization algorithms. Humanoid intelligent control
Total of 64 lessons22 hours and 32 minutes and 42 seconds
First, it briefly introduces the use of MATLAB software and commonly used built-in functions, and then introduces different types of neural networks such as BP network, radial basis network, self-organizing network, feedback network, etc., and gives examples at the end of each chapter. .
Total of 11 lessons9 hours and 53 minutes and 49 seconds
This course brings together the most frequently used functions in MATLAB, including basic operation functions, neural network functions, graphical user interface GUI functions, etc. It is taught in units of knowledge points and in an order from easy to difficult, allowing you to easily learn MATLAB
Total of 268 lessons11 hours and 46 minutes and 46 seconds
The topic of this course is artificial neural networks and their applications. This article discusses the basic units of artificial neural networks, network structures, several commonly used artificial neural network algorithms and their applications in power systems.
Total of 4 lessons1 hours and 26 minutes and 3 seconds
(01) Preliminary machine learning and related mathematics (02) Mathematical statistics and parameter estimation (03) Matrix analysis and application (04) Preliminary convex optimization (05) Regression analysis and engineering application (06) Feature engineering (07) Workflow and model Tuning (08) Maximum entropy model and EM algorithm (09) Recommended system and application (10) Clustering algorithm and application (11) Decision tree random forest and adaboost (12) SVM (13) Bayesian method (14) Topic Model (15) Bayesian inference sampling and variation (16) Artificial neural network (17) Convolutional neural network (18) Recurrent neural network and LSTM (19) Caffe&Tensor Flow&MxNet Introduction (20) Bayesian network and HMM (extra Supplement) word embedding word embedding
Total of 21 lessons1 days and 22 hours and 12 minutes and 36 seconds
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