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Python Deep Learning
Withthesurgeinartificialintelligenceinapplicationscateringtobothbusinessandconsumerneeds,deeplearningismoreimportantthaneverformeetingcurrentandfuturemarketdemands.Withthisbook,you’llexploredeeplearning,andlearnhowtoputmachinelearningtouseinyourprojects.ThissecondeditionofPythonDeepLearningwillgetyouuptospeedwithdeeplearning,deepneuralnetworks,andhowtotrainthemwithhigh-performancealgorithmsandpopularPythonframeworks.You’lluncoverdifferentneuralnetworkarchitectures,suchasconvolutionalnetworks,recurrentneuralnetworks,longshort-termmemory(LSTM)networks,andcapsulenetworks.You’llalsolearnhowtosolveproblemsinthefieldsofcomputervision,naturallanguageprocessing(NLP),andspeechrecognition.You'llstudygenerativemodelapproachessuchasvariationalautoencodersandGenerativeAdversarialNetworks(GANs)togenerateimages.Asyoudelveintonewlyevolvedareasofreinforcementlearning,you’llgainanunderstandingofstate-of-the-artalgorithmsthatarethemaincomponentsbehindpopulargamesGo,Atari,andDota.Bytheendofthebook,youwillbewell-versedwiththetheoryofdeeplearningalongwithitsreal-worldapplications.
目錄(221章)
倒序
- coverpage
- Title Page
- Copyright and Credits
- Python Deep Learning Second Edition
- About Packt
- 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
- Machine Learning - an Introduction
- Introduction to machine learning
- Different machine learning approaches
- Supervised learning
- Linear and logistic regression
- Support vector machines
- Decision Trees
- Naive Bayes
- Unsupervised learning
- K-means
- Reinforcement learning
- Q-learning
- Components of an ML solution
- Neural networks
- Introduction to PyTorch
- Summary
- Neural Networks
- The need for neural networks
- An introduction to neural networks
- An introduction to neurons
- An introduction to layers
- Multi-layer neural networks
- Different types of activation function
- Putting it all together with an example
- Training neural networks
- Linear regression
- Logistic regression
- Backpropagation
- Code example of a neural network for the XOR function
- Summary
- Deep Learning Fundamentals
- Introduction to deep learning
- Fundamental deep learning concepts
- Feature learning
- Deep learning algorithms
- Deep networks
- A brief history of contemporary deep learning
- Training deep networks
- Applications of deep learning
- The reasons for deep learning's popularity
- Introducing popular open source libraries
- TensorFlow
- Keras
- PyTorch
- Using Keras to classify handwritten digits
- Using Keras to classify images of objects
- Summary
- Computer Vision with Convolutional Networks
- Intuition and justification for CNN
- Convolutional layers
- A coding example of convolution operation
- Stride and padding in convolutional layers
- 1D 2D and 3D convolutions
- 1x1 convolutions
- Backpropagation in convolutional layers
- Convolutional layers in deep learning libraries
- Pooling layers
- The structure of a convolutional network
- Classifying handwritten digits with a convolutional network
- Improving the performance of CNNs
- Data pre-processing
- Regularization
- Weight decay
- Dropout
- Data augmentation
- Batch normalization
- A CNN example with Keras and CIFAR-10
- Summary
- Advanced Computer Vision
- Transfer learning
- Transfer learning example with PyTorch
- Advanced network architectures
- VGG
- VGG with Keras PyTorch and TensorFlow
- Residual networks
- Inception networks
- Inception v1
- Inception v2 and v3
- Inception v4 and Inception-ResNet
- Xception and MobileNets
- DenseNets
- Capsule networks
- Limitations of convolutional networks
- Capsules
- Dynamic routing
- Structure of the capsule network
- Advanced computer vision tasks
- Object detection
- Approaches to object detection
- Object detection with YOLOv3
- A code example of YOLOv3 with OpenCV
- Semantic segmentation
- Artistic style transfer
- Summary
- Generating Images with GANs and VAEs
- Intuition and justification of generative models
- Variational autoencoders
- Generating new MNIST digits with VAE
- Generative Adversarial networks
- Training GANs
- Training the discriminator
- Training the generator
- Putting it all together
- Types of GANs
- DCGAN
- The generator in DCGAN
- Conditional GANs
- Generating new MNIST images with GANs and Keras
- Summary
- Recurrent Neural Networks and Language Models
- Recurrent neural networks
- RNN implementation and training
- Backpropagation through time
- Vanishing and exploding gradients
- Long short-term memory
- Gated recurrent units
- Language modeling
- Word-based models
- N-grams
- Neural language models
- Neural probabilistic language model
- word2vec
- Visualizing word embedding vectors
- Character-based models for generating new text
- Preprocessing and reading data
- LSTM network
- Training
- Sampling
- Example training
- Sequence to sequence learning
- Sequence to sequence with attention
- Speech recognition
- Speech recognition pipeline
- Speech as input data
- Preprocessing
- Acoustic model
- Recurrent neural networks
- CTC
- Decoding
- End-to-end models
- Summary
- Reinforcement Learning Theory
- RL paradigms
- Differences between RL and other ML approaches
- Types of RL algorithms
- Types of RL agents
- RL as a Markov decision process
- Bellman equations
- Optimal policies and value functions
- Finding optimal policies with Dynamic Programming
- Policy evaluation
- Policy evaluation example
- Policy improvements
- Policy and value iterations
- Monte Carlo methods
- Policy evaluation
- Exploring starts policy improvement
- Epsilon-greedy policy improvement
- Temporal difference methods
- Policy evaluation
- Control with Sarsa
- Control with Q-learning
- Double Q-learning
- Value function approximations
- Value approximation for Sarsa and Q-learning
- Improving the performance of Q-learning
- Fixed target Q-network
- Experience replay
- Q-learning in action
- Summary
- Deep Reinforcement Learning for Games
- Introduction to genetic algorithms playing games
- Deep Q-learning
- Playing Atari Breakout with Deep Q-learning
- Policy gradient methods
- Monte Carlo policy gradients with REINFORCE
- Policy gradients with actor–critic
- Actor-Critic with advantage
- Playing cart pole with A2C
- Model-based methods
- Monte Carlo Tree Search
- Playing board games with AlphaZero
- Summary
- Deep Learning in Autonomous Vehicles
- Brief history of AV research
- AV introduction
- Components of an AV system
- Sensors
- Deep learning and sensors
- Vehicle localization
- Planning
- Imitiation driving policy
- Behavioral cloning with PyTorch
- Driving policy with ChauffeurNet
- Model inputs and outputs
- Model architecture
- Training
- DL in the Cloud
- Summary
- Other Books You May Enjoy
- Leave a review - let other readers know what you think 更新時(shí)間：2021-07-02 14:31:45
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