Machine learning is a broad and fascinating field. EVEN today, machine learning technology runs a substantial part of your life, often without you knowing it. Any plausible approach to artifi- cial intelligence must involve learning, at some level, if for no other reason than it’s hard to call a system intelligent if it cannot learn. Machine learning is also fascinating in its own right for the philo- sophical questions it raises about what it means to learn and succeed at tasks.
標簽： Learning Machine Course in
上傳時間： 2020-06-10
上傳用戶：shancjb
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.
標簽： Auto-Machine-Learning-Methods-Sys tems-Challenges
上傳時間： 2020-06-10
上傳用戶：shancjb
We’re living through exciting times. The landscape of what computers can do is changing by the week. Tasks that only a few years ago were thought to require higher cognition are getting solved by machines at near-superhuman levels of per- formance. Tasks such as describing a photographic image with a sentence in idiom- atic English, playing complex strategy game, and diagnosing a tumor from a radiological scan are all approachable now by a computer. EVEN more impressively, computers acquire the ability to solve such tasks through examples, rather than human-encoded of handcrafted rules.
標簽： Deep-Learning-with-PyTorch
上傳時間： 2020-06-10
上傳用戶：shancjb
Design for manufacturability and statistical design encompass a number of activities and areas of study spanning the integrated circuit design and manufacturing worlds. In the early days of the planar integrated circuit, it was typical for a handful of practitioners working on a particular design to have a fairly complete understanding of the manufacturing process, the resulting semiconductor active and passive devices, as well as the resulting circuit - often composed of as few as tens of devices. With the success of semiconductor scaling, predicted and - to a certain extent EVEN driven - by Moore’s law, and the vastly increased complexity of modern nano-meter scale processes and the billion-device circuits they allow, there came a necessary separation between the various disciplines.
標簽： Manufacturability Statistical Design for and
上傳時間： 2020-06-10
上傳用戶：shancjb
sscom是一款主要是用于藍牙的調試的專業(yè)串口調試軟件。用戶可以通過該多串口調試工具來調試藍牙，檢測串口的狀況。該軟件可以及時顯示存在的串口號，如果用戶增加了usb轉串口設備，串口號就會出現在列表內。能夠支持110-256000bps波特率，設置數據位（5678），校驗（odd，EVEN，mark，space）、停止位（1，1.5，2），并發(fā)送任意的字符串。對于dtr，rts信號線也能自由控制輸出狀態(tài)。功能介紹 1.顯示流暢,不容易丟數據.中文顯示無亂碼. 2.USB串口誤拔不易死機,大部分型號的USB芯片插回能自動恢復(pl2303除外). 3.通訊方面支持串口和網卡TCP/IP,UDP通訊. 4.波特率支持自定義,最 5.可以實現“幀頭+數據+校驗+幀尾”的 6.支持字符串和十六進制方式顯示,和加時間戳分數據包顯示,支持數據波形(示波器)顯示. 7.支持字符串和十進制方式發(fā)送,支持預定義99組數據串發(fā)送.支持發(fā)送數據包加校驗.支持轉義符號輸入. 8.支持接收數據直接保存到文件,也可保存窗口數據和原始接收數據. 9.支持終端仿真,STM32的ISP程序下載功能. 10.窗口大小位置,接收窗口的字體顏色背景色均可保存.
上傳時間： 2022-06-12
上傳用戶：
Agenda■Motor Types Overview■BLDC Motor Applications■Comparison of DC to Brushless DC Motors■Hall Sensors■Six-Step Commutation■Sensorless Commutation with Back-EMFVector Motor Control basicsClosed-Loop Speed Control■Introduction to BLDC Motor Control Evaluation Kit■SummaryAll the popular motor types have their specific applications, and all can be controlled with microcontrollers.We wll talk about Brushless DC motors as it is the fast growing motor type today.Motors used in modern Air conditioners, home appliances, tools, EVEN electric bikes are all going to Brushless DC.
上傳時間： 2022-07-07
上傳用戶：
