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Deep Learning Essentials

Wei Di Anurag Bhardwaj Jianing Wei 著

更新時(shí)間：2021-06-30 19:18:45

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Aspiringdatascientistsandmachinelearningexpertswhohavelimitedornoexposuretodeeplearningwillfindthisbooktobeveryuseful.Ifyouarelookingforaresourcethatgetsyouupandrunningwiththefundamentalsofdeeplearningandneuralnetworks,thisbookisforyou.AsthemodelsinthebookaretrainedusingthepopularPython-basedlibrariessuchasTensorflowandKeras,itwouldbeusefultohavesoundprogrammingknowledgeofPython.

目錄(274章)

倒序

coverpage
Title Page
Packt Upsell
Why subscribe?
PacktPub.com
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
Why Deep Learning?
What is AI and deep learning?
The history and rise of deep learning
Why deep learning?
Advantages over traditional shallow methods
Impact of deep learning
The motivation of deep architecture
The neural viewpoint
The representation viewpoint
Distributed feature representation
Hierarchical feature representation
Applications
Lucrative applications
Success stories
Deep learning for business
Future potential and challenges
Summary
Getting Yourself Ready for Deep Learning
Basics of linear algebra
Data representation
Data operations
Matrix properties
Deep learning with GPU
Deep learning hardware guide
CPU cores
CPU cache size
RAM size
Hard drive
Cooling systems
Deep learning software frameworks
TensorFlow – a deep learning library
Caffe
MXNet
Torch
Theano
Microsoft Cognitive Toolkit
Keras
Framework comparison
Setting up deep learning on AWS
Setup from scratch
Setup using Docker
Summary
Getting Started with Neural Networks
Multilayer perceptrons
The input layer
The output layer
Hidden layers
Activation functions
Sigmoid or logistic function
Tanh or hyperbolic tangent function
ReLU
Leaky ReLU and maxout
Softmax
Choosing the right activation function
How a network learns
Weight initialization
Forward propagation
Backpropagation
Calculating errors
Backpropagation
Updating the network
Automatic differentiation
Vanishing and exploding gradients
Optimization algorithms
Regularization
Deep learning models
Convolutional Neural Networks
Convolution
Pooling/subsampling
Fully connected layer
Overall
Restricted Boltzmann Machines
Energy function
Encoding and decoding
Contrastive divergence (CD-k)
Stacked/continuous RBM
RBM versus Boltzmann Machines
Recurrent neural networks (RNN/LSTM)
Cells in RNN and unrolling
Backpropagation through time
Vanishing gradient and LTSM
Cells and gates in LTSM
Step 1 – The forget gate
Step 2 – Updating memory/cell state
Step 3 – The output gate
Practical examples
TensorFlow setup and key concepts
Handwritten digits recognition
Summary
Deep Learning in Computer Vision
Origins of CNNs
Convolutional Neural Networks
Data transformations
Input preprocessing
Data augmentation
Network layers
Convolution layer
Pooling or subsampling layer
Fully connected or dense layer
Network initialization
Regularization
Loss functions
Model visualization
Handwritten digit classification example
Fine-tuning CNNs
Popular CNN architectures
AlexNet
Visual Geometry Group
GoogLeNet
ResNet
Summary
NLP - Vector Representation
Traditional NLP
Bag of words
Weighting the terms tf-idf
Deep learning NLP
Motivation and distributed representation
Word embeddings
Idea of word embeddings
Advantages of distributed representation
Problems of distributed representation
Commonly used pre-trained word embeddings
Word2Vec
Basic idea of Word2Vec
The word windows
Generating training data
Negative sampling
Hierarchical softmax
Other hyperparameters
Skip-Gram model
The input layer
The hidden layer
The output layer
The loss function
Continuous Bag-of-Words model
Training a Word2Vec using TensorFlow
Using existing pre-trained Word2Vec embeddings
Word2Vec from Google News
Using the pre-trained Word2Vec embeddings
Understanding GloVe
FastText
Applications
Example use cases
Fine-tuning
Summary
Advanced Natural Language Processing
Deep learning for text
Limitations of neural networks
Recurrent neural networks
RNN architectures
Basic RNN model
Training RNN is tough
Long short-term memory network
LSTM implementation with tensorflow
Applications
Language modeling
Sequence tagging
Machine translation
Seq2Seq inference
Chatbots
Summary
Multimodality
What is multimodality learning?
Challenges of multimodality learning
Representation
Translation
Alignment
Fusion
Co-learning
Image captioning
Show and tell
Encoder
Decoder
Training
Testing/inference
Beam Search
Other types of approaches
Datasets
Evaluation
BLEU
ROUGE
METEOR
CIDEr
SPICE
Rank position
Attention models
Attention in NLP
Attention in computer vision
The difference between hard attention and soft attention
Visual question answering
Multi-source based self-driving
Summary
Deep Reinforcement Learning
What is reinforcement learning (RL)?
Problem setup
Value learning-based algorithms
Policy search-based algorithms
Actor-critic-based algorithms
Deep reinforcement learning
Deep Q-network (DQN)
Experience replay
Target network
Reward clipping
Double-DQN
Prioritized experience delay
Dueling DQN
Implementing reinforcement learning
Simple reinforcement learning example
Reinforcement learning with Q-learning example
Summary
Deep Learning Hacks
Massaging your data
Data cleaning
Data augmentation
Data normalization
Tricks in training
Weight initialization
All-zero
Random initialization
ReLU initialization
Xavier initialization
Optimization
Learning rate
Mini-batch
Clip gradients
Choosing the loss function
Multi-class classification
Multi-class multi-label classification
Regression
Others
Preventing overfitting
Batch normalization
Dropout
Early stopping
Fine-tuning
Fine-tuning
When to use fine-tuning
When not to use fine-tuning
Tricks and techniques
List of pre-trained models
Model compression
Summary
Deep Learning Trends
Recent models for deep learning
Generative Adversarial Networks
Capsule networks
Novel applications
Genomics
Predictive medicine
Clinical imaging
Lip reading
Visual reasoning
Code synthesis
Summary
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Deep Learning Essentials