Python · Advanced Guide to Artificial Intelligence by Bonaccorso, Giuseppe -- Read -- Imperial Library of Trantor

Autoencoder types Stacked autoencoder in TensorFlow Stacked autoencoder in Keras Denoising autoencoder in TensorFlow Denoising autoencoder in Keras Variational autoencoder in TensorFlow Variational autoencoder in Keras Summary

TensorFlow Models in Production with TF Serving

Saving and Restoring models in TensorFlow

Saving and restoring all graph variables with the saver class Saving and restoring selected variables with the saver class

Saving and restoring Keras models TensorFlow Serving

Installing TF Serving Saving models for TF Serving Serving models with TF Serving

TF Serving in the Docker containers

Installing Docker Building a Docker image for TF serving Serving the model in the Docker container

TensorFlow Serving on Kubernetes

Installing Kubernetes Uploading the Docker image to the dockerhub Deploying in Kubernetes

Summary

Deep Reinforcement Learning

OpenAI Gym 101 Applying simple policies to a cartpole game Reinforcement learning 101

Q function (learning to optimize when the model is not available) Exploration and exploitation in the RL algorithms V function (learning to optimize when the model is available) Reinforcement learning techniques

Naive Neural Network policy for Reinforcement Learning Implementing Q-Learning

Initializing and discretizing for Q-Learning Q-Learning with Q-Table Q-Learning with Q-Network or Deep Q Network (DQN)

Summary

Generative Adversarial Networks

Generative Adversarial Networks 101 Best practices for building and training GANs Simple GAN with TensorFlow Simple GAN with Keras Deep Convolutional GAN with TensorFlow and Keras Summary

Distributed Models with TensorFlow Clusters

Strategies for distributed execution TensorFlow clusters

Defining cluster specification Create the server instances Define the parameter and operations across servers and devices Define and train the graph for asynchronous updates Define and train the graph for synchronous updates

Summary

Debugging TensorFlow Models

Fetching tensor values with tf.Session.run() Printing tensor values with tf.Print() Asserting on conditions with tf.Assert() Debugging with the TensorFlow debugger (tfdbg) Summary

Tensor Processing Units Getting Started

Understanding deep learning

Perceptron Activation functions

Sigmoid The hyperbolic tangent function The Rectified Linear Unit (ReLU)

Artificial neural network (ANN)

One-hot encoding Softmax Cross-entropy Dropout Batch normalization L1 and L2 regularization

Training neural networks

Backpropagation Gradient descent Stochastic gradient descent

Playing with TensorFlow playground Convolutional neural network

Kernel Max pooling

Recurrent neural networks (RNN) Long short-term memory (LSTM)

Deep learning for computer vision

Classification Detection or localization and segmentation Similarity learning Image captioning Generative models Video analysis

Development environment setup

Hardware and Operating Systems - OS

General Purpose - Graphics Processing Unit (GP-GPU)

Computer Unified Device Architecture - CUDA CUDA Deep Neural Network - CUDNN

Installing software packages

Python Open Computer Vision - OpenCV The TensorFlow library

Installing TensorFlow TensorFlow example to print Hello, TensorFlow TensorFlow example for adding two numbers TensorBoard The TensorFlow Serving tool

The Keras library

Summary

Image Classification

The bigger deep learning models

The AlexNet model The VGG-16 model The Google Inception-V3 model The Microsoft ResNet-50 model The SqueezeNet model Spatial transformer networks The DenseNet model

Training a model for cats versus dogs

Preparing the data Benchmarking with simple CNN Augmenting the dataset

Augmentation techniques

Transfer learning or fine-tuning of a model

Training on bottleneck features

Fine-tuning several layers in deep learning

Developing real-world applications

Choosing the right model Tackling the underfitting and overfitting scenarios Gender and age detection from face Fine-tuning apparel models Brand safety

Summary

Image Retrieval

Understanding visual features

Visualizing activation of deep learning models Embedding visualization

Guided backpropagation

The DeepDream Adversarial examples

Model inference

Exporting a model Serving the trained model

Content-based image retrieval

Building the retrieval pipeline

Extracting bottleneck features for an image Computing similarity between query image and target database

Efficient retrieval

Matching faster using approximate nearest neighbour

Advantages of ANNOY

Autoencoders of raw images

Denoising using autoencoders

Summary

Object Detection

Detecting objects in an image Exploring the datasets

ImageNet dataset PASCAL VOC challenge COCO object detection challenge Evaluating datasets using metrics

Intersection over Union The mean average precision

Localizing algorithms

Localizing objects using sliding windows

The scale-space concept Training a fully connected layer as a convolution layer Convolution implementation of sliding window

Thinking about localization as a regression problem

Applying regression to other problems Combining regression with the sliding window

Detecting objects

Regions of the convolutional neural network (R-CNN) Fast R-CNN Faster R-CNN Single shot multi-box detector

Object detection API

Installation and setup Pre-trained models Re-training object detection models

Data preparation for the Pet dataset Object detection training pipeline Training the model Monitoring loss and accuracy using TensorBoard

Training a pedestrian detection for a self-driving car

The YOLO object detection algorithm Summary

Semantic Segmentation

Predicting pixels

Diagnosing medical images Understanding the earth from satellite imagery Enabling robots to see

Datasets Algorithms for semantic segmentation

The Fully Convolutional Network The SegNet architecture

Upsampling the layers by pooling Sampling the layers by convolution Skipping connections for better training

Dilated convolutions DeepLab RefiNet PSPnet Large kernel matters DeepLab v3

Ultra-nerve segmentation Segmenting satellite images

Modeling FCN for segmentation

Segmenting instances Summary

Similarity Learning

Algorithms for similarity learning

Siamese networks

Contrastive loss

FaceNet

Triplet loss

The DeepNet model DeepRank Visual recommendation systems

Human face analysis

Face detection Face landmarks and attributes

The Multi-Task Facial Landmark (MTFL) dataset The Kaggle keypoint dataset The Multi-Attribute Facial Landmark (MAFL) dataset Learning the facial key points

Face recognition

The labeled faces in the wild (LFW) dataset The YouTube faces dataset The CelebFaces Attributes dataset (CelebA) CASIA web face database The VGGFace2 dataset Computing the similarity between faces Finding the optimum threshold

Face clustering

Summary

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