Hands-On Transfer Learning with Python: Implement advanced deep learning and neural network models using TensorFlow and Keras

Dipanjan Sarkar, Raghav Bali, Tamoghna Ghosh



Deep learning simplified by taking supervised, unsupervised, and reinforcement learning to the next level using the Python ecosystem

Key Features

  • Build deep learning models with transfer learning principles in Python
  • implement transfer learning to solve real-world research problems
  • Perform complex operations such as image captioning neural style transfer

Book Description

Transfer learning is a machine learning (ML) technique where knowledge gained during training a set of problems can be used to solve other similar problems.

The purpose of this book is two-fold; firstly, we focus on detailed coverage of deep learning (DL) and transfer learning, comparing and contrasting the two with easy-to-follow concepts and examples. The second area of focus is real-world examples and research problems using TensorFlow, Keras, and the Python ecosystem with hands-on examples.

The book starts with the key essential concepts of ML and DL, followed by depiction and coverage of important DL architectures such as convolutional neural networks (CNNs), deep neural networks (DNNs), recurrent neural networks (RNNs), long short-term memory (LSTM), and capsule networks. Our focus then shifts to transfer learning concepts, such as model freezing, fine-tuning, pre-trained models including VGG, inception, ResNet, and how these systems perform better than DL models with practical examples. In the concluding chapters, we will focus on a multitude of real-world case studies and problems associated with areas such as computer vision, audio analysis and natural language processing (NLP).

By the end of this book, you will be able to implement both DL and transfer learning principles in your own systems.

What you will learn

  • Set up your own DL environment with graphics processing unit (GPU) and Cloud support
  • Delve into transfer learning principles with ML and DL models
  • Explore various DL architectures, including CNN, LSTM, and capsule networks
  • Learn about data and network representation and loss functions
  • Get to grips with models and strategies in transfer learning
  • Walk through potential challenges in building complex transfer learning models from scratch
  • Explore real-world research problems related to computer vision and audio analysis
  • Understand how transfer learning can be leveraged in NLP

Who this book is for

Hands-On Transfer Learning with Python is for data scientists, machine learning engineers, analysts and developers with an interest in data and applying state-of-the-art transfer learning methodologies to solve tough real-world problems. Basic proficiency in machine learning and Python is required.

Table of Contents

  1. Machine Learning Fundamentals
  2. Deep Learning Essentials
  3. Understanding Deep Learning Architectures
  4. Transfer Learning Fundamentals
  5. Unleash the Power of Transfer Learning
  6. Image Recognition and Classification
  7. Text Document Categorization
  8. Audio Identification and Categorization
  9. Deep Dream
  10. Neural Style Transfer
  11. Automated Image Caption Generator
  12. Image Colorization




  • 使用Python建立具有轉移學習原則的深度學習模型

  • 實施轉移學習以解決真實世界的研究問題

  • 執行複雜操作,如圖像標題生成和神經風格轉移







  • 建立具有圖形處理單元(GPU)和雲支持的DL環境

  • 深入研究ML和DL模型的轉移學習原則

  • 探索各種DL架構,包括CNN、LSTM和膠囊網絡

  • 了解數據和網絡表示以及損失函數

  • 掌握轉移學習中的模型和策略

  • 解決從頭開始構建複雜轉移學習模型時可能遇到的挑戰

  • 探索與計算機視覺和音頻分析相關的真實研究問題

  • 了解如何在NLP中利用轉移學習




  1. 機器學習基礎

  2. 深度學習基本概念

  3. 理解深度學習架構

  4. 轉移學習基礎

  5. 發揮轉移學習的威力

  6. 圖像識別和分類

  7. 文本文檔分類

  8. 音頻識別和分類

  9. 深度夢境

  10. 神經風格轉移

  11. 自動圖像標題生成

  12. 圖像上色