Title Page Copyright and Credits Deep Learning By Example Packt Upsell Why subscribe? PacktPub.com Contributors About the author About the reviewers 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 Data Science - A Birds' Eye View Understanding data science by an example Design procedure of data science algorithms Data pre-processing Data cleaning Data pre-processing Feature selection Model selection Learning process Evaluating your model Getting to learn Challenges of learning Feature extraction – feature engineering Noise Overfitting Selection of a machine learning algorithm Prior knowledge Missing values Implementing the fish recognition/detection model Knowledge base/dataset Data analysis pre-processing Model building Model training and testing Fish recognition – all together Different learning types Supervised learning Unsupervised learning Semi-supervised learning Reinforcement learning Data size and industry needs Summary Data Modeling in Action - The Titanic Example Linear models for regression Motivation Advertising – a financial example Dependencies Importing data with pandas Understanding the advertising data Data analysis and visualization Simple regression model Learning model coefficients Interpreting model coefficients Using the model for prediction Linear models for classification Classification and logistic regression Titanic example – model building and training Data handling and visualization Data analysis – supervised machine learning Different types of errors Apparent (training set) error Generalization/true error Summary Feature Engineering and Model Complexity – The Titanic Example Revisited Feature engineering Types of feature engineering Feature selection Dimensionality reduction Feature construction Titanic example revisited Missing values Removing any sample with missing values in it Missing value inputting Assigning an average value Using a regression or another simple model to predict the values of missing variables Feature transformations Dummy features Factorizing Scaling Binning Derived features Name Cabin Ticket Interaction features The curse of dimensionality Avoiding the curse of dimensionality Titanic example revisited – all together Bias-variance decomposition Learning visibility Breaking the rule of thumb Summary Get Up and Running with TensorFlow TensorFlow installation TensorFlow GPU installation for Ubuntu 16.04 Installing NVIDIA drivers and CUDA 8 Installing TensorFlow TensorFlow CPU installation for Ubuntu 16.04 TensorFlow CPU installation for macOS X TensorFlow GPU/CPU installation for Windows The TensorFlow environment Computational graphs TensorFlow data types, variables, and placeholders Variables Placeholders Mathematical operations Getting output from TensorFlow TensorBoard – visualizing learning Summary TensorFlow in Action - Some Basic Examples Capacity of a single neuron Biological motivation and connections Activation functions Sigmoid Tanh ReLU Feed-forward neural network The need for multilayer networks Training our MLP – the backpropagation algorithm Step 1 – forward propagation Step 2 – backpropagation and weight updation TensorFlow terminologies – recap Defining multidimensional arrays using TensorFlow Why tensors? Variables Placeholders Operations Linear regression model – building and training Linear regression with TensorFlow Logistic regression model – building and training Utilizing logistic regression in TensorFlow Why use placeholders? Set model weights and bias Logistic regression model Training Cost function Summary Deep Feed-forward Neural Networks - Implementing Digit Classification Hidden units and architecture design MNIST dataset analysis The MNIST data Digit classification – model building and training Data analysis Building the model Model training Summary Introduction to Convolutional Neural Networks The convolution operation Motivation Applications of CNNs Different layers of CNNs Input layer Convolution step Introducing non-linearity The pooling step Fully connected layer Logits layer CNN basic example – MNIST digit classification Building the model Cost function Performance measures Model training Summary Object Detection – CIFAR-10 Example Object detection CIFAR-10 – modeling, building, and training Used packages Loading the CIFAR-10 dataset Data analysis and preprocessing Building the network Model training Testing the model Summary Object Detection – Transfer Learning with CNNs Transfer learning The intuition behind TL Differences between traditional machine learning and TL CIFAR-10 object detection – revisited Solution outline Loading and exploring CIFAR-10 Inception model transfer values Analysis of transfer values Model building and training Summary Recurrent-Type Neural Networks - Language Modeling The intuition behind RNNs Recurrent neural networks architectures Examples of RNNs Character-level language models Language model using Shakespeare data The vanishing gradient problem The problem of long-term dependencies LSTM networks Why does LSTM work? Implementation of the language model Mini-batch generation for training Building the model Stacked LSTMs Model architecture Inputs Building an LSTM cell RNN output Training loss Optimizer Building the network Model hyperparameters Training the model Saving checkpoints Generating text Summary Representation Learning - Implementing Word Embeddings Introduction to representation learning Word2Vec Building Word2Vec model A practical example of the skip-gram architecture Skip-gram Word2Vec implementation Data analysis and pre-processing Building the model Training Summary Neural Sentiment Analysis General sentiment analysis architecture RNNs – sentiment analysis context Exploding and vanishing gradients - recap Sentiment analysis – model implementation Keras Data analysis and preprocessing Building the model Model training and results analysis Summary Autoencoders – Feature Extraction and Denoising Introduction to autoencoders Examples of autoencoders Autoencoder architectures Compressing the MNIST dataset The MNIST dataset Building the model Model training Convolutional autoencoder Dataset Building the model Model training Denoising autoencoders Building the model Model training Applications of autoencoders Image colorization More applications Summary Generative Adversarial Networks An intuitive introduction Simple implementation of GANs Model inputs Variable scope Leaky ReLU Generator Discriminator Building the GAN network Model hyperparameters Defining the generator and discriminator Discriminator and generator losses Optimizers Model training Generator samples from training Sampling from the generator Summary Face Generation and Handling Missing Labels Face generation Getting the data Exploring the Data Building the model Model inputs Discriminator Generator Model losses Model optimizer Training the model Semi-supervised learning with Generative Adversarial Networks (GANs) Intuition Data analysis and preprocessing Building the model Model inputs Generator Discriminator Model losses Model optimizer Model training Summary Implementing Fish Recognition Code for fish recognition Other Books You May Enjoy Leave a review - let other readers know what you think