Applied Neural Networks with Tensorflow 2: API Oriented Deep Learning with Python

Yalçın, Orhan Gazi

  • 出版商: Apress
  • 出版日期: 2020-11-30
  • 定價: $1,900
  • 售價: 9.5$1,805
  • 貴賓價: 9.0$1,710
  • 語言: 英文
  • 頁數: 295
  • 裝訂: Quality Paper - also called trade paper
  • ISBN: 1484265122
  • ISBN-13: 9781484265123
  • 相關分類: Python程式語言DeepLearningTensorFlow
  • 立即出貨 (庫存=1)



Implement deep learning applications using TensorFlow while learning the "why" through in-depth conceptual explanations.
You'll start by learning what deep learning offers over other machine learning models. Then familiarize yourself with several technologies used to create deep learning models. While some of these technologies are complementary, such as Pandas, Scikit-Learn, and Numpy--others are competitors, such as PyTorch, Caffe, and Theano. This book clarifies the positions of deep learning and Tensorflow among their peers.
You'll then work on supervised deep learning models to gain applied experience with the technology. A single-layer of multiple perceptrons will be used to build a shallow neural network before turning it into a deep neural network. After showing the structure of the ANNs, a real-life application will be created with Tensorflow 2.0 Keras API. Next, you'll work on data augmentation and batch normalization methods. Then, the Fashion MNIST dataset will be used to train a CNN. CIFAR10 and Imagenet pre-trained models will be loaded to create already advanced CNNs.
Finally, move into theoretical applications and unsupervised learning with auto-encoders and reinforcement learning with tf-agent models. With this book, you'll delve into applied deep learning practical functions and build a wealth of knowledge about how to use TensorFlow effectively.
What You'll Learn

  • Compare competing technologies and see why TensorFlow is more popular
  • Generate text, image, or sound with GANs
  • Predict the rating or preference a user will give to an item
  • Sequence data with recurrent neural networks

Who This Book Is For
Data scientists and programmers new to the fields of deep learning and machine learning APIs.



接下來,您將使用監督式深度學習模型來獲得應用經驗。首先使用多個感知器的單層來構建一個淺層神經網絡,然後將其轉化為深度神經網絡。在展示了人工神經網絡的結構之後,將使用TensorFlow 2.0 Keras API創建一個真實應用。接下來,您將學習數據擴增和批量正規化方法。然後,將使用Fashion MNIST數據集來訓練一個卷積神經網絡。還將加載CIFAR10和Imagenet預訓練模型來創建先進的卷積神經網絡。


- 比較競爭技術,了解為什麼TensorFlow更受歡迎
- 使用生成對抗網絡(GANs)生成文本、圖像或聲音
- 預測用戶對項目的評分或偏好
- 使用循環神經網絡處理序列數據



Orhan Gazi Yalçın is a joint Ph.D. candidate at the University of Bologna & the Polytechnic University of Madrid. After completing his double major in business and law, he began his career in Istanbul, working for a city law firm, Allen & Overy, and a global entrepreneurship network, Endeavor. During his academic and professional career, he taught himself programming and excelled in machine learning. He currently conducts research on hotly debated law & AI topics such as explainable artificial intelligence and the right to explanation by combining his technical and legal skills. In his spare time, he enjoys free-diving, swimming, exercising as well as discovering new countries, cultures, and cuisines.


Orhan Gazi Yalçın是博洛尼亞大學和馬德里理工大學的聯合博士候選人。在完成商業和法律雙學位後,他在伊斯坦布爾開始了他的職業生涯,曾在Allen & Overy律師事務所和全球創業網絡Endeavor工作。在學術和職業生涯中,他自學編程並在機器學習方面表現出色。他目前通過結合技術和法律技能,進行有關熱門爭議的法律和人工智能主題的研究,如可解釋人工智能和解釋權。在閒暇時間,他喜歡自由潛水、游泳、運動,並探索新的國家、文化和美食。