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A revolution in the world of newsletters is there. Its called MailStyler. The only drag-and-drop editor that lets you create a newsletter dynamically, interactively, and incredibly quickly.

By choosing from over 110 fresh & high resolution items and placing them per drag&drop you can create awesome images in seconds ! Theres a PSD with all items having prescaled proportions so you can get started quickly without wasting time on scaling items to fit to each other. Of course you can also use it for web purposes or without photoshop, since every item also comes as transparent PNG.

David Grover
Creating Your Workflow in Capture One 10
Work Smarter, Not Harder, In Capture One
What can you do in Capture One to make your workflow faster and more efficient? David Grover, Capture One educator and expert will show you how to set up the best workspace for post-processing to exporting batches of images to multiple locations.

Computer Vision has become ubiquitous in our society, with applications in search, image understanding, apps, mapping, medicine, drones, and self-driving cars. Core to many of these applications are visual recognition tasks such as image classification, localization and detection. Recent developments in neural network (aka “deep learning”) approaches have greatly advanced the performance of these state-of-the-art visual recognition systems. This course is a deep dive into details of the deep learning architectures with a focus on learning end-to-end models for these tasks, particularly image classification. During the 10-week course, students will learn to implement, train and debug their own neural networks and gain a detailed understanding of cutting-edge research in computer vision. The final assignment will involve training a multi-million parameter convolutional neural network and applying it on the largest image classification dataset (ImageNet). We will focus on teaching how to set up the problem of image recognition, the learning algorithms (e.g. backpropagation), practical engineering tricks for training and fine-tuning the networks and guide the students through hands-on assignments and a final course project. Much of the background and materials of this course will be drawn from the ImageNet Challenge.