Beginner Local Models & Custom Datasets Illustrates two different things together: how to use local models (Llama-2-13B in particular) and how to use your own data examples for training and development. Intermediate The DSPy Paper N/A Sections 3, 5, 6, and 7 of the DSPy paper can be ...
解释表中各个变量的含义:先看每列变量的含义,Program表示前面定义的程序流程,Compilation表示前面定义的各种编译方式(或者说提示词优化器的定义方式),Training表示采用的训练集,最后是分别对两个模型GPT-3.5和Llama2-13b-chat的测试结果,都包括在开发集合(300个)dev和测试集合(1300个)test上面的测试结果。 再看每行变...
Browse the DaVinci questions and answers to find out everything you need to know about the DaVinci processors, development tools, reference designs, applications frameworks, training and support at www.ti.com/davincifaq CD Provides Comprehensive View of DaVinci Technology This highly informative CD...
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Design, develop, and test DSP algorithm-based applications and implement them on FPGA or ASIC chips with DSP HDL Toolbox.
The Certification Program allowed us to accomplish both a personal goal of continued education development and quicken a comprehensive training to be a secondary in-house trader. Hope there will be future learnings as DSP/DMP continue to evolve. Katie Cable, Programmatic and Platforms Manager As a...
Advanced time domain analysis methods Zero-crossings detector, peak detector and a Savitzky-Golay filter. Integrated signal generator Generate a custom test signal: Sine/cosine, square, triangle, sawtooth, step, impulse, white noise, DC offset, AM, or load an external datafile or audio datafile....
We have no problem using the binary and root file system provided by TI with the am57xx-evm board but we cannot test them on our board because it is a proprietary board. As our board is really different from the am57xx-evm, we have to customize Linux for our nee...
This web-based tool answers the following primary questions: T M • What is DSP? S • Which TI DSP should I use? 3 2 0 • How do I get going? C 5 • What else can help me? 0 0 0 ™ Basic Steps to Starting Development Step One: Selecting a DSP Processor If you are...
Deep learning, a subset of machine learning, has progressed rapidly during the past decade due toBig data, Deeper nets, Clever training. In deep learning, the key computational kernels involve linear algebra, including matrix and tensor arithmetic. A deep neural net can have millions or even ...