Objects in JavaScript, Part 1
Objects in jаvascript, Part 1
.MP4, AVC, 577 kbps, 1920x1080 | English, AAC, 235 kbps, 2 Ch | 52 mins | 306 MB
Instructor: Eric Greene

Outside of primitive types, everything in jаvascript is an object, including functions and arrays. Learn the basics of jаvascript objects and lay the foundation for a more advanced treatment of jаvascript objects in the next session.

Data Analysis and Dashboards with Google Data Studio
Data Analysis and Dashboards with Google Data Studio
MP4 | Video: AVC 1280x720 | Audio: AAC 44KHz 2ch | Duration: 3 Hours | 583 MB
Genre: eLearning | Language: English

In this course we are going to show you how to supercharge your Google Sheets into Interactive Dashboards and incredible reports. Google Data Studio works seamlessly with your Google Sheets data to create amazing data analysis in minutes.

Herbs & Spices - Isolated Food Items

Herbs & Spices - Isolated Food Items
English | Size: 1.9GB
Category: Tutorial

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.
Creating Your Workflow in Capture One David Grover

Creating Your Workflow in Capture One David Grover
English | Size: 895.6MB
Category: Tutorial

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.
CS231n: Convolutional Neural Networks (Deep Learning)

CS231n: Convolutional Neural Networks (Deep Learning)
English | Size: 4.75 GB
Category: tUTORIAL

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.