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Python Deep Learning Cookbook
最新章節：
How to do it...
Thisbookisintendedformachinelearningprofessionalswhoarelookingtousedeeplearningalgorithmstocreatereal-worldapplicationsusingPython.ThoroughunderstandingofthemachinelearningconceptsandPythonlibrariessuchasNumPy,SciPyandscikit-learnisexpected.Additionally,basicknowledgeinlinearalgebraandcalculusisdesired.
最新章節
- How to do it...
- Fine-tuning with Xception
- How to do it...
- Leveraging pretrained VGG models for new classes
- How to do it...
- Extracting bottleneck features with ResNet
品牌：中圖公司
上架時間：2021-07-02 12:45:26
出版社：Packt Publishing
本書數字版權由中圖公司提供，并由其授權上海閱文信息技術有限公司制作發行
- How to do it... 更新時間：2021-07-02 15:43:43
- Fine-tuning with Xception
- How to do it...
- Leveraging pretrained VGG models for new classes
- How to do it...
- Extracting bottleneck features with ResNet
- How to do it...
- Large-scale visual recognition with GoogLeNet/Inception
- Introduction
- Pretrained Models
- How to do it...
- Storing the network topology and trained weights
- How to do it...
- Freezing layers
- How to do it...
- Analyzing network weights and more
- Visualizing the network architecture with TensorBoard
- How to do it..
- Visualizing training with TensorBoard
- Introduction
- Network Internals
- Leveraging test-time augmentation (TTA) to boost accuracy
- How to do it...
- Making a model more robust with data augmentation
- How to do it...
- Adding dropouts to prevent overfitting
- Determining the depth of the network
- How to do it...
- Comparing optimizers
- How to do it...
- Learning rates and learning rate schedulers
- How to do it...
- Using grid search for parameter tuning
- How to do it...
- Working with batches and mini-batches
- How to do it...
- Visualizing training with TensorBoard and Keras
- Introduction
- Hyperparameter Selection Tuning and Neural Network Learning
- How to do it..
- Genetic Algorithm (GA) to optimize hyperparameters
- How to do it...
- Learning to play games with deep reinforcement learning
- How to do it...
- Getting started
- Learning to drive a car with end-to-end learning
- Introduction
- Game Playing Agents and Robotics
- How to do it...
- Using a shallow neural network for binary classification
- How to do it...
- Predicting bike sharing demand
- How to do it...
- Predicting stock prices with neural networks
- Introduction
- Time Series and Structured Data
- How to do it...
- Understanding videos with deep learning
- How to do it...
- Identifying speakers with voice recognition
- How to do it...
- Implementing a speech recognition pipeline from scratch
- Introduction
- Speech Recognition and Video Analysis
- How to do it...
- Summarizing text
- How to do it...
- Translating sentences
- How to do it...
- Analyzing sentiment
- Introduction
- Natural Language Processing
- How to do it...
- Transferring styles to images
- How to do it...
- Recognizing faces
- How to do it...
- Finding facial key points
- How to do it...
- Scene understanding (semantic segmentation)
- How to do it...
- Segmenting classes in images with U-net
- Real-time detection frameworks
- How to do it...
- Localizing an object in images
- How to do it...
- Classifying objects in images
- How to do it...
- Augmenting images with computer vision techniques
- Introduction
- Computer Vision
- How to do it...
- Upscaling the resolution of images with Super-Resolution GANs (SRGANs)
- How to do it...
- Implementing Deep Convolutional GANs (DCGANs)
- How to do it...
- Understanding GANs
- Introduction
- Generative Adversarial Networks
- How to do it...
- Getting ready
- Implementing a deep Q-learning algorithm
- How to do it...
- Getting ready
- Implementing policy gradients
- Introduction
- Reinforcement Learning
- How to do it...
- Character-level text generation
- How to do it...
- Implementing bidirectional RNNs
- How to do it...
- Using gated recurrent units (GRUs)
- How to do it...
- Adding Long Short-Term Memory (LSTM)
- How to do it...
- Implementing a simple RNN
- Introduction
- Recurrent Neural Networks
- How to do it...
- Applying a 1D CNN to text
- How to do it...
- Implementing a convolutional autoencoder
- How to do it...
- Experimenting with different types of initialization
- How to do it...
- Understanding padding and strides
- How to do it...
- Optimizing with batch normalization
- How to do it...
- Applying pooling layers
- How to do it...
- Getting started with filters and parameter sharing
- Introduction
- Convolutional Neural Networks
- How to do it...
- Adding dropout to prevent overfitting
- How to do it...
- Improving generalization with regularization
- How to do it...
- Experimenting with different optimizers
- How to do it...
- Tuning the loss function
- How to do it...
- Implementing an autoencoder
- There's more...
- How to do it...
- Experiment with hidden layers and hidden units
- How to do it...
- Getting started with activation functions
- How to do it...
- Building a multi-layer neural network
- How to do it...
- Implementing a single-layer neural network
- How to do it...
- Understanding the perceptron
- Introduction
- Feed-Forward Neural Networks
- How to do it...
- Defining networks using simple and efficient code with Gluon
- How to do it...
- Building efficient models with MXNet
- How to do it...
- Implementing high-performance models with CNTK
- How to do it...
- Using PyTorch’s dynamic computation graphs for RNNs
- How to do it...
- Intuitively building networks with Keras
- How to do it...
- Building state-of-the-art production-ready models with TensorFlow
- How to do it...
- Connecting with Jupyter Notebooks on a server
- How to do it...
- Installing Anaconda and libraries
- How to do it...
- Getting ready
- Installing CUDA and cuDNN
- How to do it...
- Getting ready
- Launching an instance on Google Cloud Platform (GCP)
- How to do it...
- Getting ready
- Launching an instance on Amazon Web Services (AWS)
- How to do it...
- Setting up a deep learning environment
- Introduction
- Programming Environments GPU Computing Cloud Solutions and Deep Learning Frameworks
- Questions
- Piracy
- Errata
- Downloading the example code
- Customer support
- Reader feedback
- Conventions
- Who this book is for
- What you need for this book
- What this book covers
- Preface
- Customer Feedback
- Why subscribe?
- www.PacktPub.com
- About the Reviewer
- About the Author
- Credits
- Python Deep Learning Cookbook
- Copyright
- Title Page
- coverpage
- coverpage
- Title Page
- Copyright
- Python Deep Learning Cookbook
- Credits
- About the Author
- About the Reviewer
- www.PacktPub.com
- Why subscribe?
- Customer Feedback
- Preface
- What this book covers
- What you need for this book
- Who this book is for
- Conventions
- Reader feedback
- Customer support
- Downloading the example code
- Errata
- Piracy
- Questions
- Programming Environments GPU Computing Cloud Solutions and Deep Learning Frameworks
- Introduction
- Setting up a deep learning environment
- How to do it...
- Launching an instance on Amazon Web Services (AWS)
- Getting ready
- How to do it...
- Launching an instance on Google Cloud Platform (GCP)
- Getting ready
- How to do it...
- Installing CUDA and cuDNN
- Getting ready
- How to do it...
- Installing Anaconda and libraries
- How to do it...
- Connecting with Jupyter Notebooks on a server
- How to do it...
- Building state-of-the-art production-ready models with TensorFlow
- How to do it...
- Intuitively building networks with Keras
- How to do it...
- Using PyTorch’s dynamic computation graphs for RNNs
- How to do it...
- Implementing high-performance models with CNTK
- How to do it...
- Building efficient models with MXNet
- How to do it...
- Defining networks using simple and efficient code with Gluon
- How to do it...
- Feed-Forward Neural Networks
- Introduction
- Understanding the perceptron
- How to do it...
- Implementing a single-layer neural network
- How to do it...
- Building a multi-layer neural network
- How to do it...
- Getting started with activation functions
- How to do it...
- Experiment with hidden layers and hidden units
- How to do it...
- There's more...
- Implementing an autoencoder
- How to do it...
- Tuning the loss function
- How to do it...
- Experimenting with different optimizers
- How to do it...
- Improving generalization with regularization
- How to do it...
- Adding dropout to prevent overfitting
- How to do it...
- Convolutional Neural Networks
- Introduction
- Getting started with filters and parameter sharing
- How to do it...
- Applying pooling layers
- How to do it...
- Optimizing with batch normalization
- How to do it...
- Understanding padding and strides
- How to do it...
- Experimenting with different types of initialization
- How to do it...
- Implementing a convolutional autoencoder
- How to do it...
- Applying a 1D CNN to text
- How to do it...
- Recurrent Neural Networks
- Introduction
- Implementing a simple RNN
- How to do it...
- Adding Long Short-Term Memory (LSTM)
- How to do it...
- Using gated recurrent units (GRUs)
- How to do it...
- Implementing bidirectional RNNs
- How to do it...
- Character-level text generation
- How to do it...
- Reinforcement Learning
- Introduction
- Implementing policy gradients
- Getting ready
- How to do it...
- Implementing a deep Q-learning algorithm
- Getting ready
- How to do it...
- Generative Adversarial Networks
- Introduction
- Understanding GANs
- How to do it...
- Implementing Deep Convolutional GANs (DCGANs)
- How to do it...
- Upscaling the resolution of images with Super-Resolution GANs (SRGANs)
- How to do it...
- Computer Vision
- Introduction
- Augmenting images with computer vision techniques
- How to do it...
- Classifying objects in images
- How to do it...
- Localizing an object in images
- How to do it...
- Real-time detection frameworks
- Segmenting classes in images with U-net
- How to do it...
- Scene understanding (semantic segmentation)
- How to do it...
- Finding facial key points
- How to do it...
- Recognizing faces
- How to do it...
- Transferring styles to images
- How to do it...
- Natural Language Processing
- Introduction
- Analyzing sentiment
- How to do it...
- Translating sentences
- How to do it...
- Summarizing text
- How to do it...
- Speech Recognition and Video Analysis
- Introduction
- Implementing a speech recognition pipeline from scratch
- How to do it...
- Identifying speakers with voice recognition
- How to do it...
- Understanding videos with deep learning
- How to do it...
- Time Series and Structured Data
- Introduction
- Predicting stock prices with neural networks
- How to do it...
- Predicting bike sharing demand
- How to do it...
- Using a shallow neural network for binary classification
- How to do it...
- Game Playing Agents and Robotics
- Introduction
- Learning to drive a car with end-to-end learning
- Getting started
- How to do it...
- Learning to play games with deep reinforcement learning
- How to do it...
- Genetic Algorithm (GA) to optimize hyperparameters
- How to do it..
- Hyperparameter Selection Tuning and Neural Network Learning
- Introduction
- Visualizing training with TensorBoard and Keras
- How to do it...
- Working with batches and mini-batches
- How to do it...
- Using grid search for parameter tuning
- How to do it...
- Learning rates and learning rate schedulers
- How to do it...
- Comparing optimizers
- How to do it...
- Determining the depth of the network
- Adding dropouts to prevent overfitting
- How to do it...
- Making a model more robust with data augmentation
- How to do it...
- Leveraging test-time augmentation (TTA) to boost accuracy
- Network Internals
- Introduction
- Visualizing training with TensorBoard
- How to do it..
- Visualizing the network architecture with TensorBoard
- Analyzing network weights and more
- How to do it...
- Freezing layers
- How to do it...
- Storing the network topology and trained weights
- How to do it...
- Pretrained Models
- Introduction
- Large-scale visual recognition with GoogLeNet/Inception
- How to do it...
- Extracting bottleneck features with ResNet
- How to do it...
- Leveraging pretrained VGG models for new classes
- How to do it...
- Fine-tuning with Xception
- How to do it... 更新時間：2021-07-02 15:43:43
