## 后記
> 探索是促進創新的引擎。創新促進經濟增長。讓我們一起去探索吧。
>
> Edith Widder
在這里,我提供了一個介紹性指南,解釋了如何使用 TensorFlow,為這種技術提供熱身,這無疑將在迫在眉睫的技術場景中發揮主導作用。 事實上,還有 TensorFlow 的其他替代方案,每個方案最適合特定問題;我想邀請讀者探索 TensorFlow 包之外的內容。
這些包有很多不同之處。 有些更專業,有些更不專業。 有些比其他更難安裝。 其中一些有很好的文檔,而另一些盡管運作良好,但更難找到如何使用它們的詳細信息。
重要的是:之后的日子里,TensorFlow 由谷歌發布,我在推文 [49] 中讀到了 2010-2014 期間,新的深度學習包每 47 天發布一次,2015 年每 22 天發布一次。 這很驚人,不是嗎? 正如我在本書的第一章中提出的那樣,作為讀者的起點,可以在 Awesome Deep Learning [50] 找到一個廣泛的列表。
毫無疑問,2015 年 11 月,隨著 Google TensorFlow 的發布,深度學習的格局受到影響,現在它是 Github 上最受歡迎的開源機器學習庫 [51]。
請記住,Github 的第二個最著名的機器學習項目是 Scikit-learn [52],事實上的 Python 官方的通用機器學習框架。 這些用戶可以通過 Scikit Flow(skflow)[53] 使用 TensorFlow,這是來自 Google 的 TensorFlow 的簡化接口。
實際上,Scikit Flow 是 TensorFlow 庫的高級包裝,它允許使用熟悉的 Scikit-Learn 方法訓練和擬合神經網絡。 該庫涵蓋了從線性模型到深度學習應用的各種需求。
在我看來,在 TensorFlow 分布式,TensorFlow 服務和 Scikit Flow 發布后,TensorFlow 將成為事實上的主流深度學習庫。
深度學習大大提高了語音識別,視覺對象識別,對象檢測和許多其他領域的最新技術水平。 它的未來會是什么? 根據 Yann LeCun,Yoshua Bengio 和 Geoffrey Hilton 在 Nature 雜志上的精彩評論,答案是無監督學習 [54]。 他們期望從長遠來看,無監督學習比監督學習更重要。 正如他們所提到的,人類和動物的學習基本上沒有受到監督:我們通過觀察世界來發現它的結構,而不是通過被告知每個物體的名稱。
他們對系統的未來進展有很多期望,系統將 CNN 與遞歸神經網絡(RNN)相結合,并使用強化學習。 RNN 處理一個輸入,該輸入一次編碼一個元素,在其隱藏單元中維護序列的所有過去元素的歷史的信息。 對于 TensorFlow 中 RNN 實現的介紹,讀者可以查看 TensorFlow 教程中的循環神經網絡 [55] 部分。
此外,深度學習還面臨許多挑戰;訓練它們的時間推動了新型超級計算機系統的需求。 為了將最佳的知識分析與新的大數據技術和新興計算系統的強大功能相結合,以前所未有的速度解釋大量異構數據,仍然需要進行大量研究。
科學進步通常是大型社區的跨學科,長期和持續努力的結果,而不是突破,深度學習和機器學習一般也不例外。 我們正在進入一個非常激動人心的跨學科研究時期,其中像巴塞羅那那樣的生態系統,如 UPC 和 BSC-CNS,在高性能計算和大數據技術方面具有豐富的知識,將在這個新場景中發揮重要作用。
## 參考
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[[10]](https://jorditorres.org/research-teaching/tensorflow/first-contact-with-tensorflow-book/first-contact-with-tensorflow/#_ftnref10)?TensorFlow, (2016)_Python API – Summary Operations_. [Online].?Available at:[https://www.tensorflow.org/versions/master/api_docs/python/train.html#summary-operations](https://www.tensorflow.org/versions/master/api_docs/python/train.html#summary-operations)?[Accessed: 03/01/2016].
[[11]](https://jorditorres.org/research-teaching/tensorflow/first-contact-with-tensorflow-book/first-contact-with-tensorflow/#_ftnref11)? I recommend using Google Chrome to ensure proper display.
[[12]](https://jorditorres.org/research-teaching/tensorflow/first-contact-with-tensorflow-book/first-contact-with-tensorflow/#_ftnref12)TensorFlow, (2016) TensorBoard: Graph Visualization.[Online].?Available at:[https://www.tensorflow.org/versions/master/how_tos/graph_viz/index.html](https://www.tensorflow.org/versions/master/how_tos/graph_viz/index.html)[Accessed: 02/01/2016].
[[13]](https://jorditorres.org/research-teaching/tensorflow/first-contact-with-tensorflow-book/first-contact-with-tensorflow/#_ftnref13)?One reviewer of this book has indicated that he also had to?install the package_python-gi-cairo_.
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[[17]](https://jorditorres.org/research-teaching/tensorflow/first-contact-with-tensorflow-book/first-contact-with-tensorflow/#_ftnref17)??_Github_, (2016) Book source code [Online].?Available at:[https://github.com/jorditorresBCN/TutorialTensorFlow](https://github.com/jorditorresBCN/TutorialTensorFlow). [Accessed: 16/12/2015].
[[18]](https://jorditorres.org/research-teaching/tensorflow/first-contact-with-tensorflow-book/first-contact-with-tensorflow/#_ftnref18)?? TensorFlow, (2016) API de Python – Tensor Transformations?[Online]. Available at:[https://www.tensorflow.org/versions/master/api_docs/python/array_ops.html](https://www.tensorflow.org/versions/master/api_docs/python/array_ops.html)?[Accessed: 16/12/2015].
[[19]](https://jorditorres.org/research-teaching/tensorflow/first-contact-with-tensorflow-book/first-contact-with-tensorflow/#_ftnref19)? TensorFlow, (2016) Tutorial – Reading Data [Online].?Available at:[https://www.tensorflow.org/versions/master/how_tos/reading_data](https://www.tensorflow.org/versions/master/how_tos/reading_data)[Accessed: 16/12/2015].
[[20]](https://jorditorres.org/research-teaching/tensorflow/first-contact-with-tensorflow-book/first-contact-with-tensorflow/#_ftnref20)?_Github_, (2016) TensorFlow Book – Jordi Torres. [Online].?Available at:[https://github.com/jorditorresBCN/LibroTensorFlow/blob/master/input_data.py](https://github.com/jorditorresBCN/LibroTensorFlow/blob/master/input_data.py)[Accessed: 19/02/2016].
[[21]](https://jorditorres.org/research-teaching/tensorflow/first-contact-with-tensorflow-book/first-contact-with-tensorflow/#_ftnref21)??_Github_, (2016) Shawn Simister. [Online]. Available at:?[https://gist.github.com/narphorium/d06b7ed234287e319f18](https://gist.github.com/narphorium/d06b7ed234287e319f18)?[Accessed: 9/01/2016].
[[22]](https://jorditorres.org/research-teaching/tensorflow/first-contact-with-tensorflow-book/first-contact-with-tensorflow/#_ftnref22)? ?Wikipedia, (2016). Squared Euclidean distance. [Online].?Available at:[https://en.wikipedia.org/wiki/Euclidean_distance#Squared_Euclidean_distance](https://en.wikipedia.org/wiki/Euclidean_distance#Squared_Euclidean_distance)[Accessed: 9/01/2016].
[[23]](https://jorditorres.org/research-teaching/tensorflow/first-contact-with-tensorflow-book/first-contact-with-tensorflow/#_ftnref23)??_In my opinion, the level of explanation of each operation it’s enough for the purpose of this book._
[[24]](https://jorditorres.org/research-teaching/tensorflow/first-contact-with-tensorflow-book/first-contact-with-tensorflow/#_ftnref24)? TensorFlow, (2016) Python API. [online]. Available in:?[https://www.tensorflow.org/versions/master/api_docs/index.html](https://www.tensorflow.org/versions/master/api_docs/index.html)?[Accessed: 19/02/2016].
[[25]](https://jorditorres.org/research-teaching/tensorflow/first-contact-with-tensorflow-book/first-contact-with-tensorflow/#_ftnref25)Actually “_” is like any other variable, but many Python users,?by convention, we use it to discard results.
[[26]](https://jorditorres.org/research-teaching/tensorflow/first-contact-with-tensorflow-book/first-contact-with-tensorflow/#_ftnref26)? Github, (2016) TensorFlow Book – Jordi Torres. [online].?Available at:?[https://github.com/jorditorresBCN/LibroTensorFlow](https://github.com/jorditorresBCN/LibroTensorFlow)?[Accessed: 19/02/2016].
[[27]](https://jorditorres.org/research-teaching/tensorflow/first-contact-with-tensorflow-book/first-contact-with-tensorflow/#_ftnref27)?? TensorFlow, (2016) Tutorial MNIST beginners. [online].?Available at:[https://www.tensorflow.org/versions/master/?tutorials/mnist/beginners](https://www.tensorflow.org/versions/master/%C2%A0tutorials/mnist/beginners)[Accessed: 16/12/2015].
[[28]](https://jorditorres.org/research-teaching/tensorflow/first-contact-with-tensorflow-book/first-contact-with-tensorflow/#_ftnref28)?? Neural Networks and Deep Learning.[Michael Nielsen](http://michaelnielsen.org/).?[online]. Available at:?[http://neuralnetworksanddeeplearning.com/index.html](http://neuralnetworksanddeeplearning.com/index.html)?[Accessed: 6/12/2015].
[[29]](https://jorditorres.org/research-teaching/tensorflow/first-contact-with-tensorflow-book/first-contact-with-tensorflow/#_ftnref29)??? The MNIST database of handwritten digits.[online].?Available at:[http://yann.lecun.com/exdb/mnist](http://yann.lecun.com/exdb/mnist)?[Accessed: 16/12/2015].
[[30]](https://jorditorres.org/research-teaching/tensorflow/first-contact-with-tensorflow-book/first-contact-with-tensorflow/#_ftnref30)??? Wikipedia, (2016). Antialiasing [online]. Available at:?[https://en.wikipedia.org/wiki/Antialiasing](https://en.wikipedia.org/wiki/Antialiasing)[Accessed: 9/01/2016].
[[31]](https://jorditorres.org/research-teaching/tensorflow/first-contact-with-tensorflow-book/first-contact-with-tensorflow/#_ftnref31)_??? Github_, (2016) Book TensorFlow – Jordi Torres. [online].?Available at:[https://github.com/jorditorresBCN/LibroTensorFlow/blob/master/input_data.py](https://github.com/jorditorresBCN/LibroTensorFlow/blob/master/input_data.py)?[Accessed: 9/01/2016].
[[32]](https://jorditorres.org/research-teaching/tensorflow/first-contact-with-tensorflow-book/first-contact-with-tensorflow/#_ftnref32)? ?Google (2016) TensorFlow. [online]. Available at:?[https://tensorflow.googlesource.com](https://tensorflow.googlesource.com/)[Accessed: 9/01/2016].
[[33]](https://jorditorres.org/research-teaching/tensorflow/first-contact-with-tensorflow-book/first-contact-with-tensorflow/#_ftnref33)? Wikipedia, (2016). Sigmoid function [online]. Avaliable at:?[https://en.wikipedia.org/wiki/Sigmoid_function](https://en.wikipedia.org/wiki/Sigmoid_function)?[Accessed: 12/01/2016].
[[34]](https://jorditorres.org/research-teaching/tensorflow/first-contact-with-tensorflow-book/first-contact-with-tensorflow/#_ftnref34)? Wikipedia, (2016). Softmax function [online]. Available at:?[https://en.wikipedia.org/wiki/Softmax_function](https://en.wikipedia.org/wiki/Softmax_function)?[Accessed: 2/01/2016].
[[35]](https://jorditorres.org/research-teaching/tensorflow/first-contact-with-tensorflow-book/first-contact-with-tensorflow/#_ftnref35)? ?TensorFlow, (2016) Tutorial MNIST beginners. [online].?Available at:[https://www.tensorflow.org/versions/master/tutorials/mnist/beginners](https://www.tensorflow.org/versions/master/tutorials/mnist/beginners)[Accessed: 16/12/2015].
[[36]](https://jorditorres.org/research-teaching/tensorflow/first-contact-with-tensorflow-book/first-contact-with-tensorflow/#_ftnref36)? Neural Networks & Deep Learning.[Michael Nielsen](http://michaelnielsen.org/). [online].?Available at:[http://neuralnetworksanddeeplearning.com/index.html](http://neuralnetworksanddeeplearning.com/index.html)[Accessed: 6/12/2015].
[[37]](https://jorditorres.org/research-teaching/tensorflow/first-contact-with-tensorflow-book/first-contact-with-tensorflow/#_ftnref37)? TensorFlow Github: tensorflow/tensorflow/python/ops/gradients.py [Online]. ?Available at:[https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/ops/gradients.py](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/ops/gradients.py)[Accessed: 16/03/2016].
[[38]](https://jorditorres.org/research-teaching/tensorflow/first-contact-with-tensorflow-book/first-contact-with-tensorflow/#_ftnref38)_? Github_, (2016) Libro TensorFlow – Jordi Torres. [online].?Available at:[https://github.com/jorditorresBCN/LibroTensorFlow](https://github.com/jorditorresBCN/LibroTensorFlow)[Accessed: 9/01/2016].
[[39]](https://jorditorres.org/research-teaching/tensorflow/first-contact-with-tensorflow-book/first-contact-with-tensorflow/#_ftnref39)? The reader can read more about the details of these parameters on the course website of CS231 –_Convolutional Neural Networks for Visual Recognition_(2015) [online]. Available at:[http://cs231n.github.io/convolutional-networks](http://cs231n.github.io/convolutional-networks)[Accessed: 30/12/2015].
[[40]](https://jorditorres.org/research-teaching/tensorflow/first-contact-with-tensorflow-book/first-contact-with-tensorflow/#_ftnref40)? GIMP –_Image processing software by GNU,_Convlution matrix documentation available at:[https://docs.gimp.org/es/plug-in-convmatrix.html](https://docs.gimp.org/es/plug-in-convmatrix.html)[Accessed: 5/1/2016].
[[41]](https://jorditorres.org/research-teaching/tensorflow/first-contact-with-tensorflow-book/first-contact-with-tensorflow/#_ftnref41)? TensorFlow, (2016_) Tutorials: Deep MNIST for experts_. [on line]. Availbile at:[https://www.tensorflow.org/versions/master/tutorials/mnist/pros/index.html](https://www.tensorflow.org/versions/master/tutorials/mnist/pros/index.html)?[Consulted on: 2/1/2016]
[[42]](https://jorditorres.org/research-teaching/tensorflow/first-contact-with-tensorflow-book/first-contact-with-tensorflow/#_ftnref42)? TensorFlow, (2016)_Python API. ADAM Optimizer_[on líne]. Available at:[https://www.tensorflow.org/versions/master/?api_docs/python/train.html#AdamOptimizer](https://www.tensorflow.org/versions/master/%C2%A0api_docs/python/train.html#AdamOptimizer)[Accessed: 2/1/2016].
[[43]](https://jorditorres.org/research-teaching/tensorflow/first-contact-with-tensorflow-book/first-contact-with-tensorflow/#_ftnref43)?_Github_, (2016) Source code of this book [on líne]. Availible at:?[https://github.com/jorditorresBCN/TutorialTensorFlow](https://github.com/jorditorresBCN/TutorialTensorFlow)?[Consulted on: 29/12/2015].
[[44]](https://jorditorres.org/research-teaching/tensorflow/first-contact-with-tensorflow-book/first-contact-with-tensorflow/#_ftnref44)? ?TensorFlow, (2016) GPU-related issues. [online]. Available at:?[https://www.tensorflow.org/versions/master/get_started/os_setup.html#gpu-related-issues](https://www.tensorflow.org/versions/master/get_started/os_setup.html#gpu-related-issues)[Accessed: 16/12/2015].
[[45]](https://jorditorres.org/research-teaching/tensorflow/first-contact-with-tensorflow-book/first-contact-with-tensorflow/#_ftnref45)? This output is result of using a server with 4 Tesla K40 GPUs from the[Barcelona Supercomputing Center (BSC-CNS)](http://www.bsc.es/).
[[46]](https://jorditorres.org/research-teaching/tensorflow/first-contact-with-tensorflow-book/first-contact-with-tensorflow/#_ftnref46)_? Github_(2016) AymericDamien. [online]. Available at:?[https://github.com/aymericdamien/TensorFlow-Examples](https://github.com/aymericdamien/TensorFlow-Examples)?[Accessed: 9/1/2015].
[[47]](https://jorditorres.org/research-teaching/tensorflow/first-contact-with-tensorflow-book/first-contact-with-tensorflow/#_ftnref47)? Distributed TensorFlow, (2016) [online]. Available at:?[https://github.com/tensorflow/tensorflow/tree/master/tensorflow/core/distributed_runtime](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/core/distributed_runtime)[Accessed: 16/12/2015].
[[48]](https://jorditorres.org/research-teaching/tensorflow/first-contact-with-tensorflow-book/first-contact-with-tensorflow/#_ftnref48)?_Github_, (2016) Source code of this book [on líne]. Availible at:?[https://github.com/jorditorresBCN/TutorialTensorFlow](https://github.com/jorditorresBCN/TutorialTensorFlow)?[Consulted on: 29/12/2015].
[[49]](https://jorditorres.org/research-teaching/tensorflow/first-contact-with-tensorflow-book/first-contact-with-tensorflow/#_ftnref49)? Twitter (11/11/2015). Kyle McDonald:_2010-2014: new deep learning toolkit is released every 47 days.__2015: every 22 days._[Online]. Available at:[https://twitter.com/kcimc/status/664217437840257024](https://twitter.com/kcimc/status/664217437840257024)[Accessed: 9/01/2016].
[[50]](https://jorditorres.org/research-teaching/tensorflow/first-contact-with-tensorflow-book/first-contact-with-tensorflow/#_ftnref50)??_GitHub,_(2016)_Awesome Deep Learning_. [Online]. Available at:?[https://github.com/ChristosChristofidis/awesome-deep-learning](https://github.com/ChristosChristofidis/awesome-deep-learning)[Accessed: 9/01/2016].
[[51]](https://jorditorres.org/research-teaching/tensorflow/first-contact-with-tensorflow-book/first-contact-with-tensorflow/#_ftnref51)? Explore GitHub, Machine learning: [Online]. Available at:[https://github.com/showcases/machine-learning](https://github.com/showcases/machine-learning)?[Accessed on: 2/01/2016]
[[52]](https://jorditorres.org/research-teaching/tensorflow/first-contact-with-tensorflow-book/first-contact-with-tensorflow/#_ftnref52)? Scikit-Learn GitHub: [Online]. Available at:[https://github.com/scikit-learn/scikit-learn](https://github.com/scikit-learn/scikit-learn)[Accessed: 2/3/2016]
[[53]](https://jorditorres.org/research-teaching/tensorflow/first-contact-with-tensorflow-book/first-contact-with-tensorflow/#_ftnref53)? Tensorflow/skflow GitHub: [Online]. Available at:[https://github.com/tensorflow/skflow](https://github.com/tensorflow/skflow)[Accessed: 2/1/2016]
[[54]](https://jorditorres.org/research-teaching/tensorflow/first-contact-with-tensorflow-book/first-contact-with-tensorflow/#_ftnref54)? Yann LeCun, Yoshua Bengio and Geoffrey Hinton (2015). “Deep Learning”. Nature 521: 436–444 doi:10.1038/nature14539.? Available at:[http://www.nature.com/nature/journal/v521/n7553/full/nature14539.html](http://www.nature.com/nature/journal/v521/n7553/full/nature14539.html)??[Accessed: 16/03/2016].
[[55]](https://jorditorres.org/research-teaching/tensorflow/first-contact-with-tensorflow-book/first-contact-with-tensorflow/#_ftnref55)? TensorFlow, (2016) Tutorial – Recurrent Neural Networks [Online]. Available at:[https://www.tensorflow.org/versions/r0.7/tutorials/recurrent/index.html](https://www.tensorflow.org/versions/r0.7/tutorials/recurrent/index.html)[Accessed: 16/03/2016].
[[56]](https://jorditorres.org/research-teaching/tensorflow/first-contact-with-tensorflow-book/first-contact-with-tensorflow/#_ftnref56)? Hello World en TensorFlow. Spanish version of this book [Online]. Available at:[https://jorditorres.org/libro-hello-world-en-tensorflow/](https://jorditorres.org/libro-hello-world-en-tensorflow/)[Accessed: 16/03/2016].
- TensorFlow 1.x 深度學習秘籍
- 零、前言
- 一、TensorFlow 簡介
- 二、回歸
- 三、神經網絡:感知器
- 四、卷積神經網絡
- 五、高級卷積神經網絡
- 六、循環神經網絡
- 七、無監督學習
- 八、自編碼器
- 九、強化學習
- 十、移動計算
- 十一、生成模型和 CapsNet
- 十二、分布式 TensorFlow 和云深度學習
- 十三、AutoML 和學習如何學習(元學習)
- 十四、TensorFlow 處理單元
- 使用 TensorFlow 構建機器學習項目中文版
- 一、探索和轉換數據
- 二、聚類
- 三、線性回歸
- 四、邏輯回歸
- 五、簡單的前饋神經網絡
- 六、卷積神經網絡
- 七、循環神經網絡和 LSTM
- 八、深度神經網絡
- 九、大規模運行模型 -- GPU 和服務
- 十、庫安裝和其他提示
- TensorFlow 深度學習中文第二版
- 一、人工神經網絡
- 二、TensorFlow v1.6 的新功能是什么?
- 三、實現前饋神經網絡
- 四、CNN 實戰
- 五、使用 TensorFlow 實現自編碼器
- 六、RNN 和梯度消失或爆炸問題
- 七、TensorFlow GPU 配置
- 八、TFLearn
- 九、使用協同過濾的電影推薦
- 十、OpenAI Gym
- TensorFlow 深度學習實戰指南中文版
- 一、入門
- 二、深度神經網絡
- 三、卷積神經網絡
- 四、循環神經網絡介紹
- 五、總結
- 精通 TensorFlow 1.x
- 一、TensorFlow 101
- 二、TensorFlow 的高級庫
- 三、Keras 101
- 四、TensorFlow 中的經典機器學習
- 五、TensorFlow 和 Keras 中的神經網絡和 MLP
- 六、TensorFlow 和 Keras 中的 RNN
- 七、TensorFlow 和 Keras 中的用于時間序列數據的 RNN
- 八、TensorFlow 和 Keras 中的用于文本數據的 RNN
- 九、TensorFlow 和 Keras 中的 CNN
- 十、TensorFlow 和 Keras 中的自編碼器
- 十一、TF 服務:生產中的 TensorFlow 模型
- 十二、遷移學習和預訓練模型
- 十三、深度強化學習
- 十四、生成對抗網絡
- 十五、TensorFlow 集群的分布式模型
- 十六、移動和嵌入式平臺上的 TensorFlow 模型
- 十七、R 中的 TensorFlow 和 Keras
- 十八、調試 TensorFlow 模型
- 十九、張量處理單元
- TensorFlow 機器學習秘籍中文第二版
- 一、TensorFlow 入門
- 二、TensorFlow 的方式
- 三、線性回歸
- 四、支持向量機
- 五、最近鄰方法
- 六、神經網絡
- 七、自然語言處理
- 八、卷積神經網絡
- 九、循環神經網絡
- 十、將 TensorFlow 投入生產
- 十一、更多 TensorFlow
- 與 TensorFlow 的初次接觸
- 前言
- 1.?TensorFlow 基礎知識
- 2. TensorFlow 中的線性回歸
- 3. TensorFlow 中的聚類
- 4. TensorFlow 中的單層神經網絡
- 5. TensorFlow 中的多層神經網絡
- 6. 并行
- 后記
- TensorFlow 學習指南
- 一、基礎
- 二、線性模型
- 三、學習
- 四、分布式
- TensorFlow Rager 教程
- 一、如何使用 TensorFlow Eager 構建簡單的神經網絡
- 二、在 Eager 模式中使用指標
- 三、如何保存和恢復訓練模型
- 四、文本序列到 TFRecords
- 五、如何將原始圖片數據轉換為 TFRecords
- 六、如何使用 TensorFlow Eager 從 TFRecords 批量讀取數據
- 七、使用 TensorFlow Eager 構建用于情感識別的卷積神經網絡(CNN)
- 八、用于 TensorFlow Eager 序列分類的動態循壞神經網絡
- 九、用于 TensorFlow Eager 時間序列回歸的遞歸神經網絡
- TensorFlow 高效編程
- 圖嵌入綜述:問題,技術與應用
- 一、引言
- 三、圖嵌入的問題設定
- 四、圖嵌入技術
- 基于邊重構的優化問題
- 應用
- 基于深度學習的推薦系統:綜述和新視角
- 引言
- 基于深度學習的推薦:最先進的技術
- 基于卷積神經網絡的推薦
- 關于卷積神經網絡我們理解了什么
- 第1章概論
- 第2章多層網絡
- 2.1.4生成對抗網絡
- 2.2.1最近ConvNets演變中的關鍵架構
- 2.2.2走向ConvNet不變性
- 2.3時空卷積網絡
- 第3章了解ConvNets構建塊
- 3.2整改
- 3.3規范化
- 3.4匯集
- 第四章現狀
- 4.2打開問題
- 參考
- 機器學習超級復習筆記
- Python 遷移學習實用指南
- 零、前言
- 一、機器學習基礎
- 二、深度學習基礎
- 三、了解深度學習架構
- 四、遷移學習基礎
- 五、釋放遷移學習的力量
- 六、圖像識別與分類
- 七、文本文件分類
- 八、音頻事件識別與分類
- 九、DeepDream
- 十、自動圖像字幕生成器
- 十一、圖像著色
- 面向計算機視覺的深度學習
- 零、前言
- 一、入門
- 二、圖像分類
- 三、圖像檢索
- 四、對象檢測
- 五、語義分割
- 六、相似性學習
- 七、圖像字幕
- 八、生成模型
- 九、視頻分類
- 十、部署
- 深度學習快速參考
- 零、前言
- 一、深度學習的基礎
- 二、使用深度學習解決回歸問題
- 三、使用 TensorBoard 監控網絡訓練
- 四、使用深度學習解決二分類問題
- 五、使用 Keras 解決多分類問題
- 六、超參數優化
- 七、從頭開始訓練 CNN
- 八、將預訓練的 CNN 用于遷移學習
- 九、從頭開始訓練 RNN
- 十、使用詞嵌入從頭開始訓練 LSTM
- 十一、訓練 Seq2Seq 模型
- 十二、深度強化學習
- 十三、生成對抗網絡
- TensorFlow 2.0 快速入門指南
- 零、前言
- 第 1 部分:TensorFlow 2.00 Alpha 簡介
- 一、TensorFlow 2 簡介
- 二、Keras:TensorFlow 2 的高級 API
- 三、TensorFlow 2 和 ANN 技術
- 第 2 部分:TensorFlow 2.00 Alpha 中的監督和無監督學習
- 四、TensorFlow 2 和監督機器學習
- 五、TensorFlow 2 和無監督學習
- 第 3 部分:TensorFlow 2.00 Alpha 的神經網絡應用
- 六、使用 TensorFlow 2 識別圖像
- 七、TensorFlow 2 和神經風格遷移
- 八、TensorFlow 2 和循環神經網絡
- 九、TensorFlow 估計器和 TensorFlow HUB
- 十、從 tf1.12 轉換為 tf2
- TensorFlow 入門
- 零、前言
- 一、TensorFlow 基本概念
- 二、TensorFlow 數學運算
- 三、機器學習入門
- 四、神經網絡簡介
- 五、深度學習
- 六、TensorFlow GPU 編程和服務
- TensorFlow 卷積神經網絡實用指南
- 零、前言
- 一、TensorFlow 的設置和介紹
- 二、深度學習和卷積神經網絡
- 三、TensorFlow 中的圖像分類
- 四、目標檢測與分割
- 五、VGG,Inception,ResNet 和 MobileNets
- 六、自編碼器,變分自編碼器和生成對抗網絡
- 七、遷移學習
- 八、機器學習最佳實踐和故障排除
- 九、大規模訓練
- 十、參考文獻