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Copyright and Credits
by Tamoghna Ghosh, Raghav Bali, Dipanjan Sarkar
Hands-On Transfer Learning with Python
Title Page
Copyright and Credits
Hands-On Transfer Learning with Python
Dedication
Packt Upsell
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Foreword
Contributors
About the authors
About the reviewer
Packt is searching for authors like you
Preface
Who this book is for
What this book covers
To get the most out of this book
Download the example code files
Download the color images
Conventions used
Get in touch
Reviews
Machine Learning Fundamentals
Why ML?
Formal definition
Shallow and deep learning
ML techniques
Supervised learning
Classification
Regression
Unsupervised learning
Clustering
Dimensionality reduction
Association rule mining
Anomaly detection
CRISP-DM
Business understanding
Data understanding
Data preparation
Modeling
Evaluation
Deployment
Standard ML workflow
Data retrieval
Data preparation
Exploratory data analysis
Data processing and wrangling
Feature engineering and extraction
Feature scaling and selection
Modeling
Model evaluation and tuning
Model evaluation
Bias variance trade-off
Bias
Variance
Trade-off
Underfitting
Overfitting
Generalization
Model tuning
Deployment and monitoring
Exploratory data analysis
Feature extraction and engineering
Feature engineering strategies
Working with numerical data
Working with categorical data
Working with image data
Deep learning based automated feature extraction
Working with text data
Text preprocessing
Feature engineering
Feature selection
Summary
Deep Learning Essentials
What is deep learning?
Deep learning frameworks
Setting up a cloud-based deep learning environment with GPU support
Choosing a cloud provider
Setting up your virtual server
Configuring your virtual server
Installing and updating deep learning dependencies 
Accessing your deep learning cloud environment
Validating GPU-enablement on your deep learning environment
Setting up a robust, on-premise deep learning environment with GPU support
Neural network basics
A simple linear neuron
Gradient-based optimization
The Jacobian and Hessian matrices
Chain rule of derivatives
Stochastic Gradient Descent
Non-linear neural units
Learning a simple non-linear unit – logistic unit
Loss functions
Data representations
Tensor examples
Tensor operations
Multilayered neural networks
Backprop – training deep neural networks
Challenges in neural network learning
Ill-conditioning
Local minima and saddle points 
Cliffs and exploding gradients
Initialization – bad correspondence between the local and global structure of the objective
Inexact gradients
Initialization of model parameters
Initialization heuristics
Improvements of SGD
The momentum method
Nesterov momentum
Adaptive learning rate – separate for each connection
AdaGrad
RMSprop
Adam
Overfitting and underfitting in neural networks
Model capacity
How to avoid overfitting – regularization
Weight-sharing
Weight-decay 
Early stopping
Dropout
Batch normalization
Do we need more data?
Hyperparameters of the neural network
Automatic hyperparameter tuning
Grid search
Summary
Understanding Deep Learning Architectures
Neural network architecture
Why different architectures are needed
Various architectures
MLPs and deep neural networks
Autoencoder neural networks
Variational autoencoders 
Generative Adversarial Networks
Text-to-image synthesis using the GAN architecture
CNNs 
The convolution operator
Stride and padding mode in convolution
The convolution layer
LeNet architecture
AlexNet
ZFNet
GoogLeNet (inception network)
VGG
Residual Neural Networks
Capsule networks 
Recurrent neural networks 
LSTMs
Stacked LSTMs
Encoder-decoder – Neural Machine Translation
Gated Recurrent Units
Memory Neural Networks
MemN2Ns
Neural Turing Machine 
Selective attention
Read operation
Write operation
The attention-based neural network model
Summary
Transfer Learning Fundamentals
Introduction to transfer learning
Advantages of transfer learning
Transfer learning strategies
Transfer learning and deep learning
Transfer learning methodologies
Feature-extraction
Fine-tuning
Pretrained models
Applications
Deep transfer learning types
Domain adaptation
Domain confusion
Multitask learning
One-shot learning
Zero-shot learning
Challenges of transfer learning
Negative transfer
Transfer bounds
Summary
Unleashing the Power of Transfer Learning
The need for transfer learning
Formulating our real-world problem
Building our dataset
Formulating our approach
Building CNN models from scratch
Basic CNN model
CNN model with regularization
CNN model with image augmentation
Leveraging transfer learning with pretrained CNN models
Understanding the VGG-16 model
Pretrained CNN model as a feature extractor
Pretrained CNN model as a feature extractor with image augmentation
Pretrained CNN model with fine-tuning and image augmentation
Evaluating our deep learning models
Model predictions on a sample test image
Visualizing what a CNN model perceives
Evaluation model performance on test data
Summary
Image Recognition and Classification
Deep learning-based image classification
Benchmarking datasets
State-of-the-art deep image classification models
Image classification and transfer learning
CIFAR-10
Building an image classifier
Transferring knowledge
Dog Breed Identification dataset
Exploratory analysis
Data preparation
Dog classifier using transfer learning
Summary
Text Document Categorization
Text categorization
Traditional text categorization
Shortcomings of BoW models
Benchmark datasets
Word representations
Word2vec model
Word2vec using gensim
GloVe model
CNN document model
Building a review sentiment classifier
What has embedding changed most?
Transfer learning – application to the IMDB dataset
Training on the full IMDB dataset with Word2vec embeddings
Creating document summaries with CNN model
Multiclass classification with the CNN model
Visualizing document embeddings
Summary
Audio Event Identification and Classification
Understanding audio event classification
Formulating our real-world problem
Exploratory analysis of audio events
Feature engineering and representation of audio events
Audio event classification with transfer learning
Building datasets from base features
Transfer learning for feature extraction
Building the classification model
Evaluating the classifier performance
Building a deep learning audio event identifier
Summary
DeepDream
Introduction
Algorithmic pareidolia in computer vision
Visualizing feature maps
DeepDream
Examples
Summary
Style Transfer
Understanding neural style transfer
Image preprocessing methodology
Building loss functions
Content loss
Style loss
Total variation loss
Overall loss function
Constructing a custom optimizer
Style transfer in action
Summary
Automated Image Caption Generator
Understanding image captioning
Formulating our objective
Understanding the data
Approach to automated image captioning
Conceptual approach
Practical hands-on approach
Image feature extractor – DCNN model with transfer learning
Text caption generator – sequence-based language model with LSTM
Encoder-decoder model
Image feature extraction with transfer learning
Building a vocabulary for our captions
Building an image caption dataset generator
Building our image language encoder-decoder deep learning model
Training our image captioning deep learning model
Evaluating our image captioning deep learning model
Loading up data and models
Understanding greedy and beam search
Implementing a beam search-based caption generator
Understanding and implementing BLEU scoring
Evaluating model performance on test data
Automated image captioning in action!
Captioning sample images from outdoor scenes
Captioning sample images from popular sports
Future scope for improvement
Summary
Image Colorization
Problem statement
Color images
Color theory
Color models and color spaces
RGB
YUV
LAB
Problem statement revisited
Building a coloring deep neural network
Preprocessing
Standardization
Loss function
Encoder
Transfer learning – feature extraction
Fusion layer
Decoder
Postprocessing
Training and results
Challenges
Further improvements
Summary
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