data pre-processing for the public datasets as well as your own local datasets in CSV, JSON, text, PNG, JPEG, WAV, MP3, Parquet, etc. With simple commands likeprocessed_dataset = dataset.map(process_example), efficiently prepare the dataset for inspection and ML model evaluation and training...
SFML is a simple, fast, cross-platform and object-oriented multimedia API. It provides access to windowing, graphics, audio and network. It is written in C++, and has bindings for various languages such as C, .Net, Ruby, Python.
then the DOM implementation is a reasonable choice. Later versions of MSXML introduced an implementation of Simple API for XML (SAX2). Whether it is actually "simple" is debatable. When using SAX2, before you even get off the ground, you need to implement at least two COM interface...
For the worker thread, the simplest approach is to write the code as a normal sequential piece of code. But what if you want to use the "worker thread as event source" model I've just described? This model is desirable, but it places a constraint on the interaction this code has with...
NimbusML (Latest) Overview nimbusml Overview nimbusml.cluster nimbusml.datasets nimbusml.decomposition nimbusml.ensemble Overview nimbusml.ensemble.booster nimbusml.ensemble.feature_selector nimbusml.ensemble.output_combiner nimbusml.ensemble.sub_model_selector ...
As a first step, a graph ML scientist has to build a graph ML model for a given use case using a framework like the Deep Graph Library (DGL). Training such models is challenging due to the size and complexity of graphs in enterprise applications, which routi...
“I am focusing my time to help advance @Modular. I may be starting from scratch but I feel it’s what I need to do to contribute to #AI for the next generation.” mytechnotalent “Mojo and the MAX Graph API are the surest bet for longterm multi-arch future-substrate NN compilation...
dataloader=DataLoader(dataset,collate_fn=DataCollator(tokenizer))forepochinrange(N):fori,batchinenumerate(tqdm(dataloader)):batch={k:v.to(device)fork,vinbatch.items()}outputs=model(**batch)...loss.backward()optimizer.step() 2. 基于 transformers Trainer API 的流式训练 ...
通过Fastchat平台,企业级用户可一键启动标准API(OpenAI标准)服务,满足企业需求的定制化应用开发,轻松对接口进行封装,高效且安全地开发智能对话系统。在保证数据私密性和安全性的同时,极大地提升了模型本地化部署的效率、应用性能及稳定性。 基于FastChat使用Yuan2.0大模型,Step by Step实操教程!
Visit ourInstallation Guidefor detailed instructions, including GPU support, Conda installs, and optional dependencies. ⚡ Quickstart Build accurate end-to-end ML models in just 3 lines of code! fromautogluon.tabularimportTabularPredictorpredictor=TabularPredictor(label="class").fit("train.csv")predic...