神经网络与深度学习
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Neural Networks and Deep Learning 神经网络与深度学习 的代码 评分:
Neural Networks and Deep Learning 神经网络与深度学习 的代码 Neural Networks and Deep Learning 神经网络与深度学习 的代码
上传时间:2018-09 大小:18.16MB
- 3.56MB
neural-networks-and-deep-learning.pdf
2021-10-05neural-networks-and-deep-learning.pdf
- 18.6MB
Neural Networks and Deep Learning的手写数字识别python3代码
2019-04-24Neural Networks and Deep Learning的手写数字识别例程的python3版本,亲自在python shell下修改仿真后,测试无bug
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neural-networks-and-deep-learning书籍源代码
2018-10-04neural-networks-and-deep-learning书籍的一些demo,使用python2编写,有需要可以下载
- 34.43MB
neural-networks-and-deep-learning-master 深度学习与神经网络.zip
2019-05-18neural-networks-and-deep-learning-master 深度学习与神经网络中英文,以及源码2.7,至于3.0本人实践后会在博客更新
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Udemy - Deep Learning Recurrent Neural Networks in Python
2017-10-29https://www.udemy.com/deep-learning-recurrent-neural-networks-in-python/ Deep Learning: Recurrent Neural Networks in Python GRU, LSTM, + more modern deep learning, machine learning, and data science for sequences Created by Lazy Programmer Inc. Last updated 5/2017 English What Will I Learn? Understand the simple recurrent unit (Elman unit) Understand the GRU (gated recurrent unit) Understand the LSTM (long short-term memory unit) Write various recurrent networks in Theano Understand backpropagation through time Understand how to mitigate the vanishing gradient problem Solve the XOR and parity problems using a recurrent neural network Use recurrent neural networks for language modeling Use RNNs for generating text, like poetry Visualize word embeddings and look for patterns in word vector representations Requirements Calculus Linear algebra Python, Numpy, Matplotlib Write a neural network in Theano Understand backpropagation Probability (conditional and joint distributions) Write a neural network in Tensorflow Description Like the course I just released on Hidden Markov Models, Recurrent Neural Networks are all about learning sequences – but whereas Markov Models are limited by the Markov assumption, Recurrent Neural Networks are not – and as a result, they are more expressive, and more powerful than anything we’ve seen on tasks that we haven’t made progress on in decades. So what’s going to be in this course and how will it build on the previous neural network courses and Hidden Markov Models? In the first section of the course we are going to add the concept of time to our neural networks. I’ll introduce you to the Simple Recurrent Unit, also known as the Elman unit. We are going to revisit the XOR problem, but we’re going to extend it so that it becomes the parity problem – you’ll see that regular feedforward neural networks will have trouble solving this problem but recurrent networks will work because the key is to treat the input as a sequence. In the next section of the course, we are going to revisit one of the most popular applications of recurrent neural networks – language modeling. You saw when we studied Markov Models that we could do things like generate poetry and it didn’t look too bad. We could even discriminate between 2 different poets just from the sequence of parts-of-speech tags they used. In this course, we are going to extend our language model so that it no longer makes the Markov assumption. Another popular application of neural networks for language is word vectors or word embeddings. The most common technique for this is called Word2Vec, but I’ll show you how recurrent neural networks can also be used for creating word vectors. In the section after, we’ll look at the very popular LSTM, or long short-term memory unit, and the more modern and efficient GRU, or gated recurrent unit, which has been proven to yield comparable performance. We’ll apply these to some more practical problems, such as learning a language model from Wikipedia data and visualizing the word embeddings we get as a result. All of the materials required for this course can be downloaded and installed for FREE. We will do most of our work in Numpy, Matplotlib, and Theano. I am always available to answer your questions and help you along your data science journey. This course focuses on “how to build and understand“, not just “how to use”. Anyone can learn to use an API in 15 minutes after reading some documentation. It’s not about “remembering facts”, it’s about “seeing for yourself” via experimentation. It will teach you how to visualize what’s happening in the model internally. If you want more than just a superficial look at machine learning models, this course is for you. See you in class! NOTES: All the code for this course can be downloaded from my github: /lazyprogrammer/machine_learning_examples In the directory: rnn_class Make sure you always “git pull” so you have the latest version! HARD PREREQUISITES / KNOWLEDGE YOU ARE ASSUMED TO HAVE: calculus linear algebra probability (conditional and joint distributions) Python coding: if/else, loops, lists, dicts, sets Numpy coding: matrix and vector operations, loading a CSV file Deep learning: backpropagation, XOR problem Can write a neural network in Theano and Tensorflow TIPS (for getting through the course): Watch it at 2x. Take handwritten notes. This will drastically increase your ability to retain the information. Write down the equations. If you don’t, I guarantee it will just look like gibberish. Ask lots of questions on the discussion board. The more the better! Realize that most exercises will take you days or weeks to complete. Write code yourself, don’t just sit there and look at my code. USEFUL COURSE ORDERING: (The Numpy Stack in Python) Linear Regression in Python Logistic Regression in Python (Supervised Machine Learning in Python) (Bayesian Machine Learning in Python: A/B Testing) Deep Learning in Python Practical Deep Learning in Theano and TensorFlow (Supervised Machine Learning in Python 2: Ensemble Methods) Convolutional Neural Networks in Python (Easy NLP) (Cluster Analysis and Unsupervised Machine Learning) Unsupervised Deep Learning (Hidden Markov Models) Recurrent Neural Networks in Python Artificial Intelligence: Reinforcement Learning in Python Natural Language Processing with Deep Learning in Python Who is the target audience? If you want to level up with deep learning, take this course. If you are a student or professional who wants to apply deep learning to time series or sequence data, take this course. If you want to learn about word embeddings and language modeling, take this course. If you want to improve the performance you got with Hidden Markov Models, take this course. If you’re interested the techniques that led to new developments in machine translation, take this course. If you have no idea about deep learning, don’t take this course, take the prerequisites.
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《Neural Networks and Deep Learning》中文版
2018-11-14非常清晰非常清晰!!!Michael Nielsen 大神的 《Neural Networks and Deep Learning》 网络教程一直是很多如我一样的小白入门深度学习的很好的一本初级教程。不过其原版为英文,对于初期来说我们应该以了解原理和基本用法为主,所以中文版其实更适合初学者。幸好国内有不少同好辛苦翻译了一个不错的中文版本,并且使用 LaTex 进行排版以方便阅读。
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Neural networks and deep learning.pdf
2019-07-11这是深度学习的一本入门书籍(英文版),书中理论与实践有机结合,易于上手。
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Neural Networks and Deep Learning A Textbook 完整版
2019-01-28amazon上评价不错的一本新书,对于深度学习的入门和提高是一本不错的书,值得花时间钻研学习一下。
- 12.95MB
Neural Networks and Deep Learning神经网络与深度学习.zip
2019-07-17Neural Networks and Deep Learning神经网络与深度学习 Neural Networks and Deep Learning神经网络与深度学习
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Deep Learning in Neural Networks
2016-08-06深度学习介绍
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Neural Networks and Deep Learning.神经网络与深度学习
2018-03-13Michael Nielsen的Neural Networks and Deep Learning,由Xiaohu Zhu,Freeman Zhang等人提供中文翻译的开源版本,这个是最新的v0.5中文版。
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Neural Networks and Deep Learning
2018-06-11Neural Networks and Deep Learning。神经网络和深度学习的ppt
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Neural Networks and Deep Learning.zip
2019-07-16Michael Nielsen写的《Neural Networks and Deep Learning》。原载于 http://neuralnetworksanddeeplearning.com/ 为了方便我制作成了PDF,欢迎下载阅读!
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Deep Learning in Neural Networks: An Overview
2019-03-30《Deep Learning in Neural Networks: An Overview》深度学习deep learning原版论文
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《神经⽹络与深度学习》(Neural Networks and Deep Learning)
2019-06-11(美)Michael Nielsen 著,Neural+Networks+and+Deep+Learning-神经网络与深度学习.pdf
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Neural Networks and Deep Learning 中文版
2018-08-01Neural Networks and Deep Learning is a free online book:http://neuralnetworksanddeeplearning.com/
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Neural Networks and Deep Learning中文版
2017-12-29Michael Nielsen著,Neural Networks and Deep Learning 的中文翻译版,质量非常好!
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YOLOv8-deepsort 实现智能车辆目标检测+车辆跟踪+车辆计数
2023-10-06本资源纯属免费,不收任何钱和任何积分,纯粹为爱发电,本资源已经为大家整合好了的,看我的博客部署好直接用:https://blog.csdn.net/Little_Carter/article/details/133610076?spm=1001.2014.3001.5501 资源原本项目源码地址:https://github.com/MuhammadMoinFaisal/YOLOv8-DeepSORT-Object-Tracking 本资源提供了基于YOLOv8-deepsort算法的智能车辆目标检测、车辆跟踪和车辆计数的实现方案。首先,利用YOLOv8算法对视频中的车辆目标进行检测,并对检测到的目标进行标记。然后,通过deepsort算法对标记的车辆目标进行跟踪,实现车辆目标的持续跟踪。最后,根据跟踪结果对车辆数量进行统计,实现车辆计数功能。本资源提供了完整的代码实现和详细的使用说明,帮助读者快速掌握基于YOLOv8-deepsort的智能车辆目标检测、车辆跟踪和车辆计数技术。
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YOLOv8网络结构图,自制visio文件,yolov8.vsds,需要的自取,在原有的基础上直接改就行了
2024-03-12YOLOv8网络结构图,自制visio文件,yolov8.vsds,需要的自取,在原有的基础上直接改就行了
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yolov8(2023年8月版本),已经下好yolov8s.pt和yolov8n.pt
2023-10-09yolov8(2023年8月版本),已经下好yolov8s.pt和yolov8n.pt,需要创建的文件夹都以创建,方便大家不用再去GitHub下载 可以搭配该博客:https://blog.csdn.net/weixin_43366149/article/details/132206526?spm=1001.2014.3001.5501
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Transformer模型实现长期预测并可视化结果(附代码+数据集+原理介绍)
2023-11-12这篇文章给大家带来是Transformer在时间序列预测上的应用,这种模型最初是为了处理自然语言处理(NLP)任务而设计的,但由于其独特的架构和能力,它也被用于时间序列分析。Transformer应用于时间序列分析中的基本思想是:Transformer 在时间序列分析中的应用核心在于其自注意力机制,这使其能够有效捕捉时间序列数据中的长期依赖关系。通过并行处理能力和位置编码,Transformer 不仅提高了处理效率,而且确保了时间顺序的准确性。其灵活的模型结构允许调整以适应不同复杂度这篇文章给大家带来是Transformer在时间序列预测上的应用,这种模型最初是为了处理自然语言处理(NLP)任务而设计的,但由于其独特的架构和能力,它也被用于时间序列分析。Transformer应用于时间序列分析中的基本思想是:Transformer 在时间序列分析中的应用核心在于其自注意力机制,这使其能够有效捕捉时间序列数据中的长期依赖关系。通过并行处理能力和位置编码,Transformer 不仅提高了处理效率,而且确保了时间顺序的准确性。定制化训练个人数据集进行训练利用python和pytorch实现
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社交平台上经济类话题的文章热度信息,数据是真实的,但不是真实日期
2023-03-16使用LSTM模型进行时序预测的代码与说明见:https://blog.csdn.net/Q_M_X_D_D_/article/details/109366895
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YOLOV5 + 双目相机实现三维测距(新版本)
2024-04-14YOLOV5 + 双目相机实现三维测距(新版本)