Python deep learning next generation techniques to revolutionize computer vision, AI, speech and data analysis
Take your machine learning skills to the next level by mastering Deep Learning concepts and algorithms using Python. About This Book Explore and create intelligent systems using cutting-edge deep learning techniques Implement deep learning algorithms and work with revolutionary libraries in Python G...
| Otros Autores: | |
|---|---|
| Formato: | Libro electrónico |
| Idioma: | Inglés |
| Publicado: |
Birmingham, England ; Mumbai, [India] :
Packt
2017.
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| Edición: | 1st edition |
| Materias: | |
| Ver en Biblioteca Universitat Ramon Llull: | https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009630136006719 |
Tabla de Contenidos:
- Cover
- Copyright
- Credits
- About the Authors
- About the Reviewer
- www.PacktPub.com
- Customer Feedback
- Table of Contents
- Preface
- Chapter 1: Machine Learning - An Introduction
- What is machine learning?
- Different machine learning approaches
- Supervised learning
- Unsupervised learning
- Reinforcement learning
- Steps Involved in machine learning systems
- Brief description of popular techniques/algorithms
- Linear regression
- Decision trees
- K-means
- Naïve Bayes
- Support vector machines
- The cross-entropy method
- Neural networks
- Deep learning
- Applications in real life
- A popular open source package
- Summary
- Chapter 2: Neural Networks
- Why neural networks?
- Fundamentals
- Neurons and layers
- Different types of activation function
- The back-propagation algorithm
- Linear regression
- Logistic regression
- Back-propagation
- Applications in industry
- Signal processing
- Medical
- Autonomous car driving
- Business
- Pattern recognition
- Speech production
- Code example of a neural network for the function xor
- Summary
- Chapter 3: Deep Learning Fundamentals
- What is deep learning?
- Fundamental concepts
- Feature learning
- Deep learning algorithms
- Deep learning applications
- Speech recognition
- Object recognition and classification
- GPU versus CPU
- Popular open source libraries - an introduction
- Theano
- TensorFlow
- Keras
- Sample deep neural net code using Keras
- Summary
- Chapter 4: Unsupervised Feature Learning
- Autoencoders
- Network design
- Regularization techniques for autoencoders
- Denoising autoencoders
- Contractive autoencoders
- Sparse autoencoders
- Summary of autoencoders
- Restricted Boltzmann machines
- Hopfield networks and Boltzmann machines
- Boltzmann machine
- Restricted Boltzmann machine.
- Implementation in TensorFlow
- Deep belief networks
- Summary
- Chapter 5: Image Recognition
- Similarities between artificial and biological models
- Intuition and justification
- Convolutional layers
- Stride and padding in convolutional layers
- Pooling layers
- Dropout
- Convolutional layers in deep learning
- Convolutional layers in Theano
- A convolutional layer example with Keras to recognize digits
- A convolutional layer example with Keras for cifar10
- Pre-training
- Summary
- Chapter 6: Recurrent Neural Networks and Language Models
- Recurrent neural networks
- RNN - how to implement and train
- Backpropagation through time
- Vanishing and exploding gradients
- Long short term memory
- Language modeling
- Word-based models
- N-grams
- Neural language models
- Character-based model
- Preprocessing and reading data
- LSTM network
- Training
- Sampling
- Example training
- Speech recognition
- Speech recognition pipeline
- Speech as input data
- Preprocessing
- Acoustic model
- Deep belief networks
- Recurrent neural networks
- CTC
- Attention-based models
- Decoding
- End-to-end models
- Summary
- Bibliography
- Chapter 7: Deep Learning for Board Games
- Early game playing AI
- Using the min-max algorithm to value game states
- Implementing a Python Tic-Tac-Toe game
- Learning a value function
- Training AI to master Go
- Upper confidence bounds applied to trees
- Deep learning in Monte Carlo Tree Search
- Quick recap on reinforcement learning
- Policy gradients for learning policy functions
- Policy gradients in AlphaGo
- Summary
- Chapter 8: Deep Learning for Computer Games
- A supervised learning approach to games
- Applying genetic algorithms to playing games
- Q-Learning
- Q-function
- Q-learning in action
- Dynamic games
- Experience replay
- Epsilon greedy.
- Atari Breakout
- Atari Breakout random benchmark
- Preprocessing the screen
- Creating a deep convolutional network
- Convergence issues in Q-learning
- Policy gradients versus Q-learning
- Actor-critic methods
- Baseline for variance reduction
- Generalized advantage estimator
- Asynchronous methods
- Model-based approaches
- Summary
- Chapter 9: Anomaly Detection
- What is anomaly and outlier detection?
- Real-world applications of anomaly detection
- Popular shallow machine learning techniques
- Data modeling
- Detection modeling
- Anomaly detection using deep auto-encoders
- H2O
- Getting started with H2O
- Examples
- MNIST digit anomaly recognition
- Electrocardiogram pulse detection
- Summary
- Chapter 10: Building a Production-ready Intrusion Detection System
- What is a data product?
- Training
- Weights initialization
- Parallel SGD using HOGWILD!
- Adaptive learning
- Rate annealing
- Momentum
- Nesterov's acceleration
- Newton's method
- Adagrad
- Adadelta
- Distributed learning via Map/Reduce
- Sparkling Water
- Testing
- Model validation
- Labeled Data
- Unlabeled Data
- Summary of validation
- Hyper-parameters tuning
- End-to-end evaluation
- A/B Testing
- A summary of testing
- Deployment
- POJO model export
- Anomaly score APIs
- A summary of deployment
- Summary
- Index.
