tensorflow2.4使用CUDA11.1+cudnn时，发现问题缺少cusolver64_10.dll，报错如下。为了帮助大家解决，把这个dll放上来。
Could not load dynamic library ‘cusolver64_10.dll’; dlerror: cusolver64_10.dll not found
2021-01-31 02:11:18
72.7MB
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This book attempts to simplify and present the concepts of deep learning
in a very comprehensive manner, with suitable, full-fledged examples of
neural network architectures, such as Recurrent Neural Networks (RNNs)
and Sequence to Sequence (seq2seq), for Natural Language Processing
(NLP) tasks. The book tries to bridge the gap between the theoretical and
the applicable.
It proceeds from the theoretical to the practical in a progressive
manner, first by presenting the fundamentals, followed by the underlying
mathematics, and, finally, the implementation of relevant examples.
The first three chapters cover the basics of NLP, starting with the most
frequently used Python libraries, word vector representation, and then
advanced algorithms like neural networks for textual data.
The last two chapters focus entirely on implementation, dealing with
sophisticated architectures like RNN, Long Short-Term Memory (LSTM)
Networks, Seq2seq, etc., using the widely used Python tools TensorFlow
and Keras. We have tried our best to follow a progressive approach,
combining all the knowledge gathered to move on to building a questionand-
answer system.
The book offers a good starting point for people who want to get
started in deep learning, with a focus on NLP.
All the code presented in the book is available on GitHub, in the form
of IPython notebooks and scripts, which allows readers to try out these
examples and extend them in interesting, personal ways.
2020-11-10 22:22:20
4.76MB
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许多行业专家认为，无人监督学习人工智能的下一个前沿，这可能是人工智能研究的关键，即所谓的一般人工智能。由于世界上大多数数据都没有标记，因此无法应用传统的监督学习;这就是无监督学习的用武之地。无监督学习可以应用于未标记的数据集，以发现埋藏在数据深处的有意义的模式，人类几乎不可能发现这些模式。
作者Ankur Patel使用两个简单的，生产就绪的Python框架 - scikit-learn和使用Keras的TensorFlow，提供了有关如何应用无监督学习的实用知识。通过提供实际操作示例和代码，您将识别难以发现的数据模式，获得更深入的业务洞察力，检测异常，执行自动特征工程和选择，以及生成合成数据集。您只需要编程和一些机器学习经验即可开始使用。
2020-02-20 03:18:23
5.69MB
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非常不错的资源，内容详实丰富;daily work. If youre comfortable with Python and its libraries, including pandas and scikit-learn, youll be able to address specific problems such as loading data, handling text or numerical data, model selection, and dimensionality reduction and many other topics.
Each recipe includes code that you can copy and paste into a toy dataset to ensure that it actually works. From there, you can insert, combine, or adapt the code to help construct your application. Recipes also include a discussion that explains the solution and provides meaningful context. This cookbook takes you beyond theory and concepts by providing the nuts and bolts you need to construct working machine learning applications.
2020-01-10 03:07:00
3.42MB
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Cats vs, Dogs 的python原创代码，
ipynb格式。图片数据来源https://www.kaggle.com/c/dogs-vs-cats
任务如下：
Task:
In this competition, you'll write an algorithm to classify whether images contain either a dog or a cat. This is easy for humans, dogs, and cats. Your computer will find it a bit more difficult.
Data Description:
The training archive contains 25,000 images of dogs and cats. Train your algorithm on these files and predict the labels for test1.zip (1 = dog, 0 = cat).
Submission Format
Your submission should have a header. For each image in the test set, predict a label for its id (1 = dog, 0 = cat):
id,label
1,0
2,0
3,0
etc...
2020-01-03 11:39:35
96.84MB
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super pix图像清晰化（超级分辨率）采用python语言编写程序实现
2019-12-21 21:48:23
12.1MB
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该代码是使用TensorFlow库搭建AlexNet框架来实现mnist数据集，注释很清楚，有问题请联系
2019-12-21 21:46:11
6KB
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