Deep Learning with Theano

Develop deep neural networks in Theano with practical code examples for image classification, machine translation, reinforcement agents, or generative models.

About This Book

• Learn Theano basics and evaluate your mathematical expressions faster and in an efficient manner
• Learn the design patterns of deep neural architectures to build efficient and powerful networks on your datasets
• Apply your knowledge to concrete fields such as image classification, object detection, chatbots, machine translation, reinforcement agents, or generative models.

Who This Book Is For

This book is indented to provide a full overview of deep learning. From the beginner in deep learning and artificial intelligence, to the data scientist who wants to become familiar with Theano and its supporting libraries, or have an extended understanding of deep neural nets.

Some basic skills in Python programming and computer science will help, as well as skills in elementary algebra and calculus.

What You Will Learn

• Get familiar with Theano and deep learning
• Provide examples in supervised, unsupervised, generative, or reinforcement learning.
• Discover the main principles for designing efficient deep learning nets: convolutions, residual connections, and recurrent connections.
• Use Theano on real-world computer vision datasets, such as for digit classification and image classification.
• Extend the use of Theano to natural language processing tasks, for chatbots or machine translation
• Cover artificial intelligence-driven strategies to enable a robot to solve games or learn from an environment
• Generate synthetic data that looks real with generative modeling
• Become familiar with Lasagne and Keras, two frameworks built on top of Theano

In Detail

This book offers a complete overview of Deep Learning with Theano, a Python-based library that makes optimizing numerical expressions and deep learning models easy on CPU or GPU.

The book provides some practical code examples that help the beginner understand how easy it is to build complex neural networks, while more experimented data scientists will appreciate the reach of the book, addressing supervised and unsupervised learning, generative models, reinforcement learning in the fields of image recognition, natural language processing, or game strategy.

The book also discusses image recognition tasks that range from simple digit recognition, image classification, object localization, image segmentation, to image captioning. Natural language processing examples include text generation, chatbots, machine translation, and question answering. The last example deals with generating random data that looks real and solving games such as in the Open-AI gym.

At the end, this book sums up the best -performing nets for each task. While early research results were based on deep stacks of neural layers, in particular, convolutional layers, the book presents the principles that improved the efficiency of these architectures, in order to help the reader build new custom nets.

Style and approach

It is an easy-to-follow example book that teaches you how to perform fast, efficient computations in Python. Starting 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.

使用Theano在實際的程式碼範例中開發深度神經網絡，包括圖像分類、機器翻譯、強化學習代理或生成模型。

關於本書
- 學習Theano的基礎知識，以更快速和高效的方式評估數學表達式
- 學習深度神經網絡的設計模式，以在您的數據集上構建高效且強大的網絡
- 將您的知識應用於圖像分類、物體檢測、聊天機器人、機器翻譯、強化學習代理或生成模型等具體領域

本書適合對深度學習和人工智能有初步了解的初學者，以及希望熟悉Theano及其支援庫，或對深度神經網絡有更深入了解的數據科學家。

一些基本的Python編程和計算機科學技能，以及初等代數和微積分技能將有所幫助。

您將學到什麼
- 熟悉Theano和深度學習
- 提供監督、非監督、生成或強化學習的示例
- 探索設計高效深度學習網絡的主要原則：卷積、殘差連接和循環連接
- 在真實的計算機視覺數據集上使用Theano，例如數字分類和圖像分類
- 將Theano擴展到自然語言處理任務，例如聊天機器人或機器翻譯
- 涵蓋基於Theano的兩個框架Lasagne和Keras
- 生成看起來真實的合成數據
- 熟悉Theano的最佳性能網絡

詳細內容
本書提供了Theano深度學習的完整概述，Theano是一個基於Python的庫，可在CPU或GPU上輕鬆優化數值表達式和深度學習模型。

本書提供了一些實用的代碼示例，幫助初學者了解構建複雜神經網絡的簡單性，同時更有經驗的數據科學家將欣賞到本書的廣度，涵蓋監督和非監督學習、生成模型、圖像識別、自然語言處理或遊戲策略的強化學習。

本書還討論了從簡單的數字識別、圖像分類、物體定位、圖像分割到圖像標題的圖像識別任務。自然語言處理的示例包括文本生成、聊天機器人、機器翻譯和問答。最後一個示例涉及生成看起來真實的隨機數據和解決Open-AI gym中的遊戲。

最後，本書總結了每個任務的最佳性能網絡。早期的研究結果基於深度堆疊的神經層，特別是卷積層，本書介紹了改進這些架構效率的原則，以幫助讀者構建新的自定義網絡。

風格和方法
這是一本易於理解的示例書，教您如何在Python中執行快速、高效的計算。從最基礎的NumPy和Theano安裝開始，本書將帶您順利實現Theano進行機器學習和深度學習的高級計算。