Colorizing Images in an iOS App Using DeOldify and a Flask API

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Computer Vision — iOS

Finding old black and white photos and imagining what people, landscapes, and objects looked like in color has probably already crossed the minds of many.

While it is possible to manually colorize black and white photos using image editing and retouching software such as Photoshop, the manipulation, which is only available to insiders and professional image editors, can be time-consuming and tedious.

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Predicting Depression from Routine Survey Data using Keras

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Depression is among the most prominent mental illnesses, affecting more than 300 million people globally, according to the World Health Organization (WHO).

Beyond the potentially devastating personal and social effects of depression, the economic costs related to this mental health issue have substantially increased in recent years. In fact, it’s currently estimated to cost the global economy over $1 trillion USD each year.

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Fritz AI Named a 2020 Boston “Startup to Watch” by Built In Boston

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Since we founded Fritz AI in late 2017, we’ve seen tremendous growth in the Boston-area tech scene. What was once a city whose tech economy centered on academia, biotech, and robotics, Boston has flourished into one of the most attractive locations for all kinds of tech start-ups, early- and late-stage alike.

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Training an Image Classification Convolutional Neural Net to Detect Plant Disease Using fast.ai

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Image Classification and Convolutional Neural Networks

Over the past few years, deep learning techniques have dominated computer vision. One of the computer vision application areas where deep learning excels is image classification with Convolutional Neural Networks (CNNs).

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Using Transfer Learning and Pre-trained Language Models to Classify Spam

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Transfer learning, an approach where a model developed for a task is reused as the starting point for a model on a second task, is an important approach in machine learning. Prior knowledge from one domain and task is leveraged into a different domain and task.

Transfer learning, therefore, draws inspiration from human beings, who are capable of transferring and leveraging knowledge from what they have learned in the past for tackling a wide variety of tasks.

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The 5 Computer Vision Techniques That Will Change How You See The World

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Computer Vision is one of the hottest research fields within Deep Learning at the moment.

It sits at the intersection of many academic subjects, such as Computer Science (Graphics, Algorithms, Theory, Systems, Architecture), Mathematics (Information Retrieval, Machine Learning), Engineering (Robotics, Speech, NLP, Image Processing), Physics (Optics), Biology (Neuroscience), and Psychology (Cognitive Science).

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Quickly Build a Snapchat Lens By Leveraging Fritz AI Studio’s Style Transfer Model

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Computer Vision — SnapML

The style transfer is one of the most creative applications of Convolutional Neural Networks. It allows you to retrieve the style of an image and use it to transform any given image.

It is an interesting technique that highlights the capacities and internal representations of neural networks. It can also be useful in certain scientific fields for augmenting or simulating image data.

The almost endless combinations of content and styles possible bring out unique and ever more creative results from neural network enthusiasts.

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Audio in Lens Studio

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Lens Studio allows you to import and add audio to your lenses. Adding audio to your lenses enhances the nature of the lenses by enabling the viewer more interaction. It can be added in behavior scripts as a response and even in animations.

Behavior scripts in lens studio enable you to make interactions in your lenses by defining a trigger type and a response for that trigger.

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