GitHub 地址→https://github.com/osnr/TabFS 1.2 源码合集:500-AI-Machine-learning-Deep-learning-Computer-vision-NLP-Projects-with-code 本周star 增长数:2,700+ New500-AI-Machine-learning-Deep-learning-Computer-vision-NLP-
500 AI Machine learning Deep learning Computer vision NLP Projects with code!!! Follow me on LinkedIn : This list is continuously updated.- You can take pull requests and contribute. All Links are tested and working fine. Please ping if any link doesn't work ...
This idea was possible within the MathWorks framework, where control tools like Simulink live in the same environment as discipline-based toolboxes like Deep Learning Toolbox™ and Computer Vision Toolbox™. From Whiteboard to Code with Deep Learning Being the guinea pig meant drawing lot...
There are kernels which have been written by authors and also you can contribute to those and they are good sources for learning artificial intelligence in R and Python. Moreover, you can use its data set as reference and test your code with prepared data. I want to practice convolutional,...
今天再推荐一个更强大的,可以复现机器学习论文代码的工具——Paper2Code 开源代码不支持DeepSeek,稍作修改就可以使用DeepSeekAPI了 https://arxiv.org/pdf/2504.17192 https://arxiv.org/pdf/2504.17192 一句话总结,PaperCoder旨在通过利用大型语言模型(LLM)在多代理系统中直接从机器学习研究论文中生成完整的、可执行...
Given the immense competition in Deep Learning and Artificial Intelligence job roles and the application scope as a whole in the different sectors of modern technology, it should be a passion for aspirants to keep learning and implementing the learned concepts through a few projects. Learning ...
With GPU Coder™, you can generate optimized code for prediction of a variety of trained deep learning networks from Deep Learning Toolbox™. The generated code implements the deep convolutional neural network (CNN) by using the architecture, the layers, and parameters that you specify in the...
前面的博客中有提到过要开源最近写的code,seq2seq-attention,今天正式开源了,欢迎各路大神来fork和star。这是我从5月中旬开始决定用torch框架来写deep learning code以来写的第一个完整的program,在写的过程中走过不少弯路,尤其是在选择demo进行学习的过程中,被HarvardNLP组的seq2seq-attn难以阅读的代码搞得非常崩溃...
In this paper, we develop deep learning-based choice models under two settings of choice modeling: (i) feature-free and (ii) feature-based. Our model captures both the intrinsic utility for each candidate choice and the effect that the assortment has on the choice probability. Synthetic and ...
We then show how the game can be extended to general acyclic neural networks with differentiable convex gates, establishing a bijection between the Nash equilibria and critical (or KKT) points of the deep learning problem. Based on these connections we investigate alternative learning methods, and ...