“…Although the contributions synthetic biology can make in these and other areas hold great promise, it is also possible to imagine malicious uses that could threaten U.S. citizens and military personnel. …”

Tabla de Contenidos: “…The main training loop -- Model Initialization -- Do not initialize all weights with zeros -- Initializing with a mean zero distribution -- Xavier-Bengio and the Initializer -- Improving generalization by regularizing -- L2 and L1 regularization -- Dropout -- The batch norm layer -- Summary -- Chapter 4: Object Detection and Segmentation -- Image classification with localization -- Localization as regression -- TensorFlow implementation -- Other applications of localization -- Object detection as classification - Sliding window -- Using heuristics to guide us (R-CNN) -- Problems -- Fast R-CNN -- Faster R-CNN -- Region Proposal Network -- RoI Pooling layer -- Conversion from traditional CNN to Fully Convnets -- Single Shot Detectors - You Only Look Once -- Creating training set for Yolo object detection -- Evaluating detection (Intersection Over Union) -- Filtering output -- Anchor Box -- Testing/Predicting in Yolo -- Detector Loss function (YOLO loss) -- Loss Part 1 -- Loss Part 2 -- Loss Part 3 -- Semantic segmentation -- Max Unpooling -- Deconvolution layer (Transposed convolution) -- The loss function -- Labels -- Improving results -- Instance segmentation -- Mask R-CNN -- Summary -- Chapter 5: VGG, Inception Modules, Residuals, and MobileNets -- Substituting big convolutions -- Substituting the 3x3 convolution -- VGGNet -- Architecture -- Parameters and memory calculation -- Code -- More about VGG -- GoogLeNet -- Inception module -- More about GoogLeNet -- Residual Networks -- MobileNets -- Depthwise separable convolution -- Control parameters -- More about MobileNets -- Summary -- Chapter 6: Autoencoders, Variational Autoencoders, and Generative Adversarial Networks -- Why generative models -- Autoencoders -- Convolutional autoencoder example -- Uses and limitations of autoencoders -- Variational autoencoders…”

“…Fueling this transformation is the insights we gain from data in service of one and in service of all. We are holding the future in our hands and therefore share both the excitement and the responsibility of making our innovations useful and trustworthy for each other. …”

“…The InsurTech Book offers essential updates, critical thinking and actionable insight — globally — from start-ups, incumbents, investors, tech companies, advisors and other partners in this evolving ecosystem, in one volume. …”

Tabla de Contenidos: “…LTSS Are Expensive, and Financing Mechanisms Are Inadequate for Many FamiliesRAND's Blueprint Builds on Current U.S. Dementia or LTSS Strategies; Organization of the Report; Chapter Two: Stakeholder-Engaged Policy Identification; Overview; Methods for Identifying and Evaluating Policy Options; Stakeholder-Identified Policy Options; Comparison of Policy Options to Recommendations in Other Reports; Research Directions; Summary; Chapter Three: RAND Policy Evaluation and Blueprint Recommendations; Overview; Impact and Feasibility Ratings of Policy Options…”