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Advanced Deep Learning with Keras 高级进阶高清版 评分:
Advanced Deep Learning with Keras 高级进阶高清版 ,注意是Advanced
上传时间:2018-12 大小:9.98MB
- 9.98MB
Advanced_Deep_Learning_with_Keras.pdf
2018-12-02In recent years, deep learning has made unprecedented success stories in difficult problems in vision, speech, natural language processing and understanding, and all other areas with abundance of data. The interest in this field by companies, universities, governments, and research organizations has accelerated the advances in the field. This book covers select important advances in deep learning. The advanced theories are explained by giving a background of the principles, digging into the intuition behind the concepts, implementing the equations and algorithms using Keras, and examining the results. Artificial Intelligence (AI), as it stands today, is still far from being a well- understood field. Deep learning, as a sub field of AI, is in the same position. While it is far from being a mature field, many real-world applications such as vision-based detection and recognition, product recommendation, speech recognition and synthesis, energy conservation, drug discovery, finance, and marketing are already using deep learning algorithms. Many more applications will be discovered and built. The aim of this book is to explain advanced concepts, give sample implementations, and let the readers, as experts in their field, identify the target applications.
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《Deep Learning with Keras》随书的源代码
2017-05-04图书可以从下面的链接下载 http://download.csdn.net/detail/u013003382/9832573 Deep Learning with Keras by Antonio Gulli English | 26 Apr. 2017 | ASIN: B06Y2YMRDW | 318 Pages | AZW3 | 10.56 MB Key Features Implement various deep-learning algorithms in Keras and see how deep-learning can be used in games See how various deep-learning models and practical use-cases can be implemented using Keras A practical, hands-on guide with real-world examples to give you a strong foundation in Keras Book Description This book starts by introducing you to supervised learning algorithms such as simple linear regression, the classical multilayer perceptron and more sophisticated deep convolutional networks. You will also explore image processing with recognition of hand written digit images, classification of images into different categories, and advanced objects recognition with related image annotations. An example of identification of salient points for face detection is also provided. Next you will be introduced to Recurrent Networks, which are optimized for processing sequence data such as text, audio or time series. Following that, you will learn about unsupervised learning algorithms such as Autoencoders and the very popular Generative Adversarial Networks (GAN). You will also explore non-traditional uses of neural networks as Style Transfer. Finally, you will look at Reinforcement Learning and its application to AI game playing, another popular direction of research and application of neural networks. What you will learn Optimize step-by-step functions on a large neural network using the Backpropagation Algorithm Fine-tune a neural network to improve the quality of results Use deep learning for image and audio processing Use Recursive Neural Tensor Networks (RNTNs) to outperform standard word embedding in special cases Identify problems for which Recurrent Neural Network (RNN) solutions are suitable Explore the process required to implement Autoencoders Evolve a deep neural network using reinforcement learning About the Author Antonio Gulli is a software executive and business leader with a passion for establishing and managing global technological talent, innovation, and execution. He is an expert in search engines, online services, machine learning, information retrieval, analytics, and cloud computing. So far, he has been lucky enough to gain professional experience in four different countries in Europe and managed people in six different countries in Europe and America. Antonio served as CEO, GM, CTO, VP, director, and site lead in multiple fields spanning from publishing (Elsevier) to consumer internet (Ask.com and Tiscali) and high-tech R&D (Microsoft and Google). Sujit Pal is a technology research director at Elsevier Labs, working on building intelligent systems around research content and metadata. His primary interests are information retrieval, ontologies, natural language processing, machine learning, and distributed processing. He is currently working on image classification and similarity using deep learning models. Prior to this, he worked in the consumer healthcare industry, where he helped build ontology-backed semantic search, contextual advertising, and EMR data processing platforms. He writes about technology on his blog at Salmon Run. Table of Contents Neural Networks Foundations Keras Installation and API Deep Learning with ConvNets Generative Adversarial Networks and WaveNet Word Embeddings Recurrent Neural Network — RNN Additional Deep Learning Models AI Game Playing Conclusion
- 10.55MB
Deep Learning with Keras.azw3电子书下载
2017-05-03Deep Learning with Keras by Antonio Gulli English | 26 Apr. 2017 | ASIN: B06Y2YMRDW | 318 Pages | AZW3 | 10.56 MB Key Features Implement various deep-learning algorithms in Keras and see how deep-learning can be used in games See how various deep-learning models and practical use-cases can be implemented using Keras A practical, hands-on guide with real-world examples to give you a strong foundation in Keras Book Description This book starts by introducing you to supervised learning algorithms such as simple linear regression, the classical multilayer perceptron and more sophisticated deep convolutional networks. You will also explore image processing with recognition of hand written digit images, classification of images into different categories, and advanced objects recognition with related image annotations. An example of identification of salient points for face detection is also provided. Next you will be introduced to Recurrent Networks, which are optimized for processing sequence data such as text, audio or time series. Following that, you will learn about unsupervised learning algorithms such as Autoencoders and the very popular Generative Adversarial Networks (GAN). You will also explore non-traditional uses of neural networks as Style Transfer. Finally, you will look at Reinforcement Learning and its application to AI game playing, another popular direction of research and application of neural networks. What you will learn Optimize step-by-step functions on a large neural network using the Backpropagation Algorithm Fine-tune a neural network to improve the quality of results Use deep learning for image and audio processing Use Recursive Neural Tensor Networks (RNTNs) to outperform standard word embedding in special cases Identify problems for which Recurrent Neural Network (RNN) solutions are suitable Explore the process required to implement Autoencoders Evolve a deep neural network using reinforcement learning About the Author Antonio Gulli is a software executive and business leader with a passion for establishing and managing global technological talent, innovation, and execution. He is an expert in search engines, online services, machine learning, information retrieval, analytics, and cloud computing. So far, he has been lucky enough to gain professional experience in four different countries in Europe and managed people in six different countries in Europe and America. Antonio served as CEO, GM, CTO, VP, director, and site lead in multiple fields spanning from publishing (Elsevier) to consumer internet (Ask.com and Tiscali) and high-tech R&D (Microsoft and Google). Sujit Pal is a technology research director at Elsevier Labs, working on building intelligent systems around research content and metadata. His primary interests are information retrieval, ontologies, natural language processing, machine learning, and distributed processing. He is currently working on image classification and similarity using deep learning models. Prior to this, he worked in the consumer healthcare industry, where he helped build ontology-backed semantic search, contextual advertising, and EMR data processing platforms. He writes about technology on his blog at Salmon Run. Table of Contents Neural Networks Foundations Keras Installation and API Deep Learning with ConvNets Generative Adversarial Networks and WaveNet Word Embeddings Recurrent Neural Network — RNN Additional Deep Learning Models AI Game Playing Conclusion
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Deep Learning with Keras(带书签版)
2017-06-09Deep Learning with Keras的带书签版本
- 39.8MB
deep_learning_with_keras.pdf高清pdf
2018-07-18Keras是一个高层神经网络API,Keras由纯Python编写而成并基Tensorflow、Theano以及CNTK后端。Keras 为支持快速实验而生,能够把你的idea迅速转换为结果
- 16.38MB
Advanced Deep Learning with Keras(October 31, 2018).zip
2019-08-30This book covers advanced deep learning techniques to create successful AI. Using MLPs, CNNs, and RNNs as building blocks to more advanced techniques, you’ll study deep neural network architectures, ...
- 20.14MB
Deep Learning with Keras
2017-11-15You will also explore image processing with recognition of hand written digit images, classification of images into different categories, and advanced objects recognition with related image ...
- 17.44MB
Deep Learning with Keras 2017
2018-12-11Key FeaturesImplement various deep learning algorithms in Keras and see how deep learning can be used in gamesSee how various deep learning models and practical use cases can be implemented using ...
- 176.68MB
Reinforcement learning合集
2019-04-25this file contains:Advanced Deep Learning with Keras_ Apply deep learning techniques, autoencoders, GANs, variational autoencoders, deep reinforcement learning, policy gradients, and more (2018, Packt...
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Deep Learning with Keras source coude
2017-09-25Implement various deep-learning algorithms in Keras and see how deep-learning can be used in games See how various deep-learning models and practical use-cases can be implemented using Ke ras A ...
- 54.58MB
Deep Learning with Keras+标签完整无水印+文字可编辑复制
2019-05-23Implement neural networks with Keras on Theano and TensorFlow The book presents more than 20 working deep neural networks coded in Python using Keras, a modular neural network library that runs on top of either Google's TensorFlow or Lisa Lab's Theano backends. The reader is introduced step by step to supervised learning algorithms such as simple linear regression, classical multilayer perceptron, and more sophisticated deep convolutional networks and generative adversarial networks. In addition, the book covers unsupervised learning algorithms such as autoencoders and generative networks. Recurrent networks and long short-term memory (LSTM) networks are also explained in detail. The book goes on to cover the Keras functional API and how to customize Keras in case the reader's use case is not covered by Keras's extensive functionality. It also looks at larger, more complex systems composed of the building blocks covered previously. The book concludes with an introduction to deep reinforcement learning and how it can be used to build game playing AIs. Practical applications include code for the classification of news articles into predefined categories, syntactic analysis of texts, sentiment analysis, synthetic generation of texts, and parts of speech annotation. Image processing is also explored, with recognition of handwritten digit images, classification of images into different categories, and advanced object recognition with related image annotations. An example of identification of salient points for face detection will be also provided. Sound analysis comprises recognition of discrete speeches from multiple speakers. Reinforcement learning is used to build a deep Q-learning network capable of playing games autonomously. Experiments are the essence of the book. Each net is augmented by multiple variants that progressively improve the learning performance by changing the input parameters, the shape of the network, loss functions, and algorithms used for optimizations. Several comparisons between training on CPUs and GPUs are also provided. 全书8章310页,主要聚焦于keras api的使用,推荐给炼丹的同学
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Deep-Learning-with-TensorFlow-2-and-Keras:Packt发行的《使用TensorFlow 2和Keras进行深度学习》
2021-03-21使用TensorFlow 2和Keras进行深度学习-第二版 这是发布的进行的代码存储库。它包含了从头到尾完成本书所必需的所有支持项目文件。 关于这本书 TensorFlow 2和Keras的深度学习,第二版与TensorFlow(TF)和Keras一起教深度学习技术。本书介绍了使用TensorFlow的神经网络,贯穿了主要应用程序,涵盖了两个有效的示例应用程序,然后深入探讨了TF和cloudin生产,TF mobile,以及将TensorFlow与AutoML结合使用。 说明和导航 所有代码都组织在文件夹中。每个文件夹均以数字开头,后跟应用程序名称。例如,第2章。 该代码将如下所示: @tf.function def fn(input, state): return cell(input, state) input = tf.zeros([100, 100]) state =
- 13.81MB
advanced-deep-learning:高级机器学习Coursera课程的文件
2021-05-14先进的机器学习课程 这是Yandex和HSE在Coursera上提供的AML专业化的github存储库。 模组 。 该课程涵盖深度学习的基础知识,从过度拟合和不足拟合的基本概念到最新的CNN和RNN。 在课程中,我用numpy编写了一个神经网络,这有助于我理解反向传播的工作原理。 作业是开放式的,鼓励进行实验以及反复试验,就像在实际应用中一样。 这些作业将numpy,keras和tensorflow进行了有趣的混合。 这有助于将这些模块视为同一工具箱中的工具,而不是孤立的工具。 最终项目是设计一个字幕神经网络,同时具有用于特征提取的CNN(Pretrained InceptionV3)和RNN。 在一组(图像,字幕)上对其进行训练,并且网络学习对任何图像(与该训练集相似的图像)进行字幕。 。 该课程涵盖探索性数据分析,特征生成以及特征调整和模型验证,所有这些均由专家kaggle竞
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Advanced Applied Deep Learning.pdf
2019-12-12Advanced Applied Deep Learning Advanced Applied Deep Learning Advanced Applied Deep Learning Advanced Applied Deep Learning
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Applied Deep Learning
2018-12-26使用深度学习中的高级主题,例如优化算法,超参数调整,丢失和错误分析,以及解决训练深度神经网络时遇到的典型问题的策略。您将首先研究激活函数,主要是使用单个神经元(ReLu,Sigmoid和Swish),了解如何使用TensorFlow执行线性和逻辑回归,并选择正确的成本函数。 下一节将讨论具有多个层和神经元的更复杂的神经网络架构,并探讨权重随机初始化的问题。整章专门介绍神经网络误差分析的完整概述,给出了解决来自不同分布的方差,偏差,过度拟合和数据集的问题的示例。 Applied Deep Learning还讨论了如何在不使用除NumPy之外的任何Python库的情况下完全从头开始实现逻辑回归,让您了解TensorFlow等库如何实现快速有效的实验。包括每种方法的案例研究,以实施所有理论信息。您将发现编写优化的Python代码的技巧和窍门(例如使用NumPy进行矢量化循环)。
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Deep Learning with R (MEAP v1)-Manning(2017).pdf
2018-04-01This book is an adaptation of my previously published Deep Learning with Python, with all of the code examples using the R interface to Keras. The goal of the book is to provide a learning resource ...
- 122.93MB
Python-Keras深度学习进阶随书代码
2019-08-11Advanced Deep Learning with Keras, published by Packt
- 6.98MB
Deep Learning with Theano [2017]
2017-08-16Starting with the very basics-NumPy, installing Theano, this book will take you to the smooth journey of implementing Theano for advanced computations for machine learning and deep learning.
- 27.86MB
Deep Learning with TensorFlow 2ed pdf+epub
2018-07-29Every week, we follow news of applications and the shocking results obtained from them, thanks to the artificial intelligence algorithms applied in different fields. What we are witnessing is one of the biggest accelerations in the entire history of this sector, and the main suspect behind these important developments is called deep learning. Deep learning comprises a vast set of algorithms that are based on the concept of neural networks and expand to contain a huge number of nodes that are disseminated at several levels of depth. Though the concept of neural networks, the so-called Artificial Neural Network (ANN), dates back to the late 1940s, initially, they were difficult to be used because of the need for huge computational power resources and the lack of data required to train the algorithms. Presently, the ability to use graphics processors (GPUs) in parallel to perform intensive calculation operations has completely opened the way to the use of deep learning. In this context, we propose the second edition of this book, with expanded and revised contents that introduce the core concepts of deep learning, using the last version of TensorFlow.
- 13.27MB
Deep Learning with TensorFlow第二版pdf
2018-05-24Apply deep machine intelligence and GPU computing with TensorFlow v1.7 Access public datasets and use TensorFlow to load, process, and transform the data Discover how to use the high-level TensorFlow API to build more powerful applications Use deep learning for scalable object detection and mobile computing Train machines quickly to learn from data by exploring reinforcement learning techniques Explore active areas of deep learning research and applications
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Deep-Learning-with-Keras-Antonio Gulli
2017-12-28本书首先介绍监督学习算法,如简单线性回归,经典多层感知器和更复杂的深度卷积网络。您还将探索识别手写数字图像的图像处理,将图像分类到不同类别以及使用相关图像注释进行高级对象识别。还提供了识别面部检测的显着点的示例。接下来,您将介绍Recurrent Networks,它们针对处理序列数据(如文本,音频或时间序列)进行了优化。接下来,您将了解无监督学习算法,如Autoencoders和非常流行的Generative Adversarial Networks(GAN)。您还将探索非传统的神经网络作为风格转换。
- 20.23MB
deep learning with keras
2017-08-21深度学习资料 pdf版本
- 38.84MB
Deep.Learning.with.Keras.epub
2017-11-13Implement various deep-learning algorithms in Keras and see how deep-learning can be used in games See how various deep-learning models and practical use-cases can be implemented using Keras A ...
- 20.66MB
Python Deep Learning: Exploring deep learning techniques, neural network
2019-02-07Exploring an advanced state of the art deep learning models and its applications using Popular python libraries like Keras, Tensorflow, and Pytorch Key Features • A strong foundation on neural ...
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Deep-Learning-with-Keras-master.zip
2019-06-14深度学习,Keras 框架,keras 适合快速构建神经网络,很适合新手学习!摘自GitHub,里面包含近些年有名的神经网络框架
- 1.41MB
源码Deep Learning with Theano
2018-08-06Deep Learning with Theano. It addresses the way to create new operators for the computation graph, either in Python for simplicity, or in C to overcome the Python overhead, either for the CPU or for ...
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Deep Learning with Keras代码
2017-09-21Deep Learning with Keras代码 Deep Learning with Keras代码 Deep Learning with Keras代码 Deep Learning with Keras代码 Deep Learning with Keras代码 Deep Learning with Keras代码