Alan Sokal writes:

• Begin with N of 10, increase by 10 until p < 0.05 or max N reached.

Single Shot Multibox Detetor (SSD) We present a method for detecting objects in images using a single deep neural network. Our approach, named SSD, discretizes the output space of bounding boxes into a set of default boxes over different aspect ratios and scales per feature map location. At prediction time, the network generates scores for the presence of each object category in each default box and produces adjustments to the box to better match the object shape. Additionally, the network combines predictions from multiple feature maps with different resolutions to naturally handle objects of various sizes. Our SSD model is simple relative to methods that require object proposals because it completely eliminates proposal generation and subsequent pixel or feature resampling stage and encapsulates all computation in a single network. This makes SSD easy to train and straightforward to integrate into systems that require a detection component. Experimental results on the PASCAL VOC, MS COCO, and ILSVRC datasets confirm that SSD has comparable accuracy to methods that utilize an additional object proposal step and is much faster, while providing a unified framework for both training and inference. Compared to other single stage methods, SSD has much better accuracy, even with a smaller input image size. For input, SSD achieves 72.1% mAP on VOC2007 test at 58 FPS on a Nvidia Titan X and for input, SSD achieves 75.1% mAP, outperforming a comparable state of the art Faster R-CNN model. Code is available at this https URL . …

Keras Hyperparameter Tuning in Google Colab using Hyperas

We at STATWORX work a lot with R and we often use the same little helper functions within our projects. These functions ease our daily work life by reducing repetitive code parts or by creating overviews of our projects. At first, there was no plan to make a package, but soon I realised, that it will be much easier to share and improve those functions, if they are within a package. Up till the 24th December I will present one function each day from helfRlein. So, on the 7th day of Christmas my true love gave to me…

I find the activity of codifying and comparing various interpretations of a particular process in the pursuit of strengthening one’s own interpretation of said process to be a worthy one. I have previously done so with alternate interpretations of what we could call the machine learning process (and which could reasonably be closely aligned with the data science or data mining processes, at least to some degree), of which you can find examples here and here and here.

Overview

Title: Take a Look at Looker Demo Date: Thursday, December 13, 2018 Time: 1pm PT, 4pm ET Duration: 1 hour