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Hands-On Artificial Intelligence for Beginners : an Introduction to AI Concepts, Algorithms, and Their Implementation.
This book will empower you to apply Artificial Intelligence techniques to design applications for natural language processing, robotics, and other real-world use-cases. You will be able to develop, debug, deploy and optimize intelligent AI systems for self-driving cars, game playing, and much more.
| Clasificación: | Libro Electrónico |
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| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Birmingham :
Packt Publishing Ltd,
2018.
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| Temas: | |
| Acceso en línea: | Texto completo |
Tabla de Contenidos:
- Cover; Title Page; Copyright and Credits; Packt Upsell; Contributors; Table of Contents; Preface; Chapter 1: The History of AI; The beginnings of AI -1950-1974; Rebirth -1980-1987; The modern era takes hold
- 1997-2005; Deep learning and the future
- 2012-Present; Summary; Chapter 2: Machine Learning Basics; Technical requirements; Applied math basics; The building blocks
- scalars, vectors, matrices, and tensors; Scalars; Vectors; Matrices; Tensors; Matrix math; Scalar operations; Element-wise operations; Basic statistics and probability theory; The probability space and general theory
- Probability distributionsProbability mass functions ; Probability density functions ; Conditional and joint probability; Chain rule for joint probability; Bayes' rule for conditional probability; Constructing basic machine learning algorithms; Supervised learning algorithms; Random forests; Unsupervised learning algorithms; Basic tuning; Overfitting and underfitting; K-fold cross-validation; Hyperparameter optimization; Summary; Chapter 3: Platforms and Other Essentials; Technical requirements; TensorFlow, PyTorch, and Keras; TensorFlow; Basic building blocks; The TensorFlow graph; PyTorch
- Basic building blocksThe PyTorch graph; Keras; Basic building blocks; Wrapping up; Cloud computing essentials; AWS basics; EC2 and virtual machines; S3 Storage ; AWS Sagemaker; Google Cloud Platform basics; GCP cloud storage; GCP Cloud ML Engine; CPUs, GPUs, and other compute frameworks; Installing GPU libraries and drivers; With Linux (Ubuntu); With Windows; Basic GPU operations; The future
- TPUs and more; Summary; Chapter 4: Your First Artificial Neural Networks; Technical requirements; Network building blocks; Network layers; Naming and sizing neural networks
- Setting up network parameters in our MNIST exampleActivation functions; Historically popular activation functions; Modern approaches to activation functions; Weights and bias factors; Utilizing weights and biases in our MNIST example; Loss functions; Using a loss function for simple regression; Using cross-entropy for binary classification problems; Defining a loss function in our MNIST example; Stochastic gradient descent; Learning rates; Utilizing the Adam optimizer in our MNIST example; Regularization; The training process; Putting it all together; Forward propagation; Backpropagation
- Forwardprop and backprop with MNISTManaging a TensorFlow model; Saving model checkpoints; Summary; Chapter 5: Convolutional Neural Networks; Overview of CNNs; Convolutional layers; Layer parameters and structure; Pooling layers; Fully connected layers; The training process; CNNs for image tagging; Summary; Chapter 6: Recurrent Neural Networks; Technical requirements; The building blocks of RNNs; Basic structure; Vanilla recurrent neural networks; One-to-many; Many-to-one; Many-to-many; Backpropagation through time; Memory units
- LSTMs and GRUs; LSTM; GRUs; Sequence processing with RNNs