Deep Learning for Beginners A Beginner's Guide to Getting up and Running with Deep Learning from Scratch Using Python

• Main Author: Rivas, Pablo
• Format: eBook
• Language: English
• Published: Birmingham Packt Publishing, Limited 2020; Packt Publishing Limited; Packt Publishing
• Edition: 1
• Subjects: COM004000 COMPUTERS / Intelligence (AI) & Semantics; COMPUTERS / Data Modeling & Design; COMPUTERS / Neural Networks; Machine learning
• ISBN: 1838640851; 9781838640859
• DOI: 10.0000/9781838647582

This book is for beginners who are looking for a strong foundation to build deep learning models from scratch. You will test your understanding of the concepts and measure your progress at the end of each chapter. You will have a firm understanding of deep learning and will be able to identify which...

Table of Contents:

• Introducing deep belief networks -- Making deep autoencoders -- Batch normalization -- Dropout -- Exploring latent spaces with deep autoencoders -- CIFAR-10 -- MNIST -- Summary -- Questions and answers -- References -- Chapter 9: Variational Autoencoders -- Introducing deep generative models -- Examining the VAE model -- The heart disease dataset revisited -- The re-parametrization trick and sampling -- Learning the posterior's parameters in the encoder -- Modeling the decoder -- Minimizing the loss -- Training a VAE -- Generating data from the VAE -- Comparing a deep and shallow VAE on MNIST -- Shallow VAE -- Deep VAE -- Encoder -- Decoder -- Denoising VAEs -- Thinking about the ethical implications of generative models -- Summary -- Questions and answers -- References -- Chapter 10: Restricted Boltzmann Machines -- Introduction to RBMs -- BMs -- RBMs -- Bernoulli RBMs -- Learning data representations with RBMs -- Comparing RBMs and AEs -- Summary -- Questions and answers -- References -- Section 3: Supervised Deep Learning -- Chapter 11: Deep and Wide Neural Networks -- Wide neural networks -- Deep learning revisited -- Wide layers -- Summaries -- Names -- The CIFAR-10 dataset -- New training tools -- Saving or loading models -- Reducing the learning rate on the fly -- Stopping the learning process early -- Results -- Dense deep neural networks -- Building and training the model -- Results -- Sparse deep neural networks -- Building a sparse network and training it -- Results -- Hyperparameter optimization -- Libraries and parameters -- Implementation and results -- Summary -- Questions and answers -- References -- Chapter 12: Convolutional Neural Networks -- Introduction to convolutional neural networks -- Convolution in n-dimensions -- 1-dimension -- 2-dimensions -- n-dimensions -- Convolutional layers -- Conv2D -- The layer+activation combo
• Cover -- Title Page -- Copyright and Credits -- About Packt -- Foreword -- Contributors -- Table of Contents -- Preface -- Section 1: Getting Up to Speed -- Chapter 1: Introduction to Machine Learning -- Diving into the ML ecosystem -- Training ML algorithms from data -- Introducing deep learning -- The model of a neuron -- The perceptron learning algorithm -- Shallow networks -- The input-to-hidden layer -- The hidden-to-hidden layer -- The hidden-to-output layer -- Deep networks -- Why is deep learning important today? -- Summary -- Questions and answers -- References -- Chapter 2: Setup and Introduction to Deep Learning Frameworks -- Introduction to Colaboratory -- Introduction and setup of TensorFlow -- Setup -- TensorFlow with GPU support -- Principles behind TensorFlow -- Introduction and setup of Keras -- Setup -- Principles behind Keras -- Introduction to PyTorch -- Introduction to Dopamine -- Other deep learning libraries -- Caffe -- Theano -- Honorable mentions -- Summary -- Questions and answers -- References -- Chapter 3: Preparing Data -- Binary data and binary classification -- Binary targets on the Cleveland Heart Disease dataset -- Binarizing the MNIST dataset -- Binarizing the images -- Binarizing the targets -- Categorical data and multiple classes -- Converting string labels to numbers -- Converting categories to one-hot encoding -- Real-valued data and univariate regression -- Scaling to a specific range of values -- Standardizing to zero mean and unit variance -- Altering the distribution of data -- Data augmentation -- Rescaling -- Adding noise -- Rotating -- Other augmentation techniques -- Data dimensionality reduction -- Supervised algorithms -- Linear discriminant analysis -- Unsupervised techniques -- Kernel PCA -- Large datasets -- Sparse PCA -- Dictionary Learning -- Regarding the number of dimensions
• Ethical implications of manipulating data -- Summary -- Questions and answers -- References -- Chapter 4: Learning from Data -- Learning for a purpose -- Classification -- Binary classification -- Multi-class classification -- Regression -- Measuring success and error -- Binary classification -- Multiple classes -- Regression -- Identifying overfitting and generalization -- If we have test data -- No test data? No problem - cross-validate -- The art behind learning -- Ethical implications of training deep learning algorithms -- Reporting using the appropriate performance measures -- Being careful with outliers and verifying them -- Weight classes with undersampled groups -- Summary -- Questions and answers -- References -- Chapter 5: Training a Single Neuron -- The perceptron model -- The visual concept -- Tensor operations -- The perceptron learning algorithm -- PLA in Python -- A perceptron over non-linearly separable data -- Convergence on linearly separable data -- Convergence on non-linearly separable data -- Summary -- Questions and answers -- References -- Chapter 6: Training Multiple Layers of Neurons -- The MLP model -- Minimizing the error -- Step 1 - Initialization -- Step 2 - The forward pass -- Step 3 - Calculating loss -- Step 4 - The backward pass -- Finding the best hyperparameters -- Summary -- Questions and answers -- References -- Section 2: Unsupervised Deep Learning -- Chapter 7: Autoencoders -- Introduction to unsupervised learning -- Encoding and decoding layers -- Encoding layers -- Decoding layers -- Loss function -- Learning and testing -- Applications in dimensionality reduction and visualization -- MNIST data preparation -- Autoencoders for MNIST -- Training and visualization -- Ethical implications of unsupervised learning -- Summary -- Questions and answers -- References -- Chapter 8: Deep Autoencoders
• Pooling strategies -- Convolutional neural network for CIFAR-10 -- Implementation -- Loading data -- Compiling the model -- Training the CNN -- Results -- Visualization of filters -- Summary -- Questions and answers -- References -- Chapter 13: Recurrent Neural Networks -- Introduction to recurrent neural networks -- Simple RNNs -- Embedding layers -- Word embedding and RNNs on IMDb -- Long short-term memory models -- Sequence-to-vector models -- Unsupervised model -- Results -- Vector-to-sequence models -- Bi-directional LSTM -- Implementation and results -- Sequence-to-sequence models -- Ethical implications -- Summary -- Questions and answers -- References -- Chapter 14: Generative Adversarial Networks -- Introducing adversarial learning -- Learning using an adversary -- GANs -- Training a GAN -- An MLP model -- A convolutional model -- Comparing GANs and VAEs -- Thinking about the ethical implications of GANs -- Summary -- Questions and answers -- References -- Chapter 15: Final Remarks on the Future of Deep Learning -- Looking for advanced topics in deep learning -- Deep reinforcement learning -- Self-supervised learning -- System 2 algorithms -- Learning with more resources from Packt -- Reinforcement learning -- Self-supervised learning -- Summary -- References -- Other Books You May Enjoy -- Index
• Deep Learning for Beginners: A beginner's guide to getting up and running with deep learning from scratch using Python