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Vasilev, Ivan. Python deep learning: exploring deep learning techniques and neural network architectures with PyTorch, Keras, and TensorFlow / Ivan Vasilev [and four others]. — Second edition. — 1 online resource (1 volume) : illustrations — <URL:http://elib.fa.ru/ebsco/2002295.pdf>.

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2/20/2019

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Python (Computer program language); Machine learning.; Neural networks (Computer science); Artificial intelligence.; Artificial intelligence.; Machine learning.; Neural networks (Computer science); Python (Computer program language)

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EBSCO

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  • Cover
  • Title Page
  • Copyright and Credits
  • About Packt
  • Contributors
  • Table of Contents
  • Preface
  • Chapter 1: 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
  • Chapter 2: 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
  • Chapter 3: 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
  • Chapter 4: 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
  • Chapter 5: 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
  • Chapter 6: 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
  • Chapter 7: 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
  • Chapter 8: 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
  • Chapter 9: 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
  • Chapter 10: 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
  • Index

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