Computer systems that emulate key aspects of human problem solving are commonly referred to as artificial intelligence (AI). This field has seen massive progress over the last years. Most notably, deep learning enabled groundbreaking progress in areas such as self-driving cars, computers beating the best human players in strategy games (Go, chess), computer games, and in poker, and initial applications in diagnostic medicine. Deep learning is based on artificial neural networks--networks of mathematical functions that are iteratively reorganized until they accurately map the data describing a given problem to its solution. In biology, deep learning has established itself as a powerful method to predict phenotypes (i.e., observable characteristics of cells or individuals) from genome data (for example gene expression profiles).

CNN's have been extensively used to classify images. But to detect an object in an image and to draw bounding boxes around them is a tough problem to solve. To solve this problem, R-CNN algorithm was published in 2014. After R-CNN, many of its variants like Fast-R-CNN, Faster-R-CNN and Mask-R-CNN came which improvised the task of object detection. To understand the latest R-CNN variants, it is important to have a clear understanding of R-CNN.

The curiosity in artificial intelligence (AI) is taken to a whole new level these past years. Every day new startups, new tools, new innovations are growing. This term is now always mentioned when we talk about AI. Nowadays, though, people who interested in learning more about this technology won't have time to go back to college or spend a whole year on a training course. For this reason, we decided to created this article.

Core ML, Apple's primary model framework, got a big boost with the inclusion of on-device model training last year. While the hopes for the introduction of on-device training for recurrent neural networks (RNNs) this year were dashed, there were still some pretty interesting announcements. As you can see, we have a new metadata tab that shows the layer distribution for the Core ML model. Most notably, the utility section consists of two new features -- Model Encryption and Model Deployment. Until now, updating models in production apps has been a headache.

Bitcoin is a very particular asset. Bitcoin is a very particular asset. Its price is sensible to demand and supply rather than external factors, so it may highly depend on perceived trends rather than perceived information. For this category of problems, pattern recognition may prove incredibly useful. Because this problem is very big, from beginning to end, I will begin with the first part of the article by Mining Bitcoin Data.

A bot that writes letters on behalf of nature. Those are just some of the recent stories written about GPT-3, the latest contraption of artificial intelligence research lab OpenAI. GPT-3 is the largest language model ever made, and it has triggered many discussions over how AI will soon transform many industries. But what has been less discussed is how GPT-3 has transformed OpenAI itself. In the process of creating the most successful natural language processing system ever created, OpenAI has gradually morphed from a nonprofit AI lab to a company that sells AI services. And hanging in the balance is the very mission for which OpenAI was founded.

In this tutorial, you will learn how to train an Optical Character Recognition (OCR) model using Keras, TensorFlow, and Deep Learning. For now, we'll primarily be focusing on how to train a custom Keras/TensorFlow model to recognize alphanumeric characters (i.e., the digits 0-9 and the letters A-Z). Building on today's post, next week we'll learn how we can use this model to correctly classify handwritten characters in custom input images. We'll be starting with the fundamentals of using well-known handwriting datasets and training a ResNet deep learning model on these data. To learn how to train an OCR model with Keras, TensorFlow, and deep learning, just keep reading. In the first part of this tutorial, we'll discuss the steps required to implement and train a custom OCR model with Keras and TensorFlow.

Convolution is a mathematical operation on two functions (f and g) that produces a third function (f*g) expressing how the shape of one is modified by the other. The term convolution refers to both the result function and to the process of computing it. It is defined as the integral of the product of the two functions after one is reversed and shifted. And the integral is evaluated for all values of shift, producing the convolution function. Hyperparameter is a parameter whose value is used to control the learning process. It can be classified as model hyperparameters, that cannot be inferred while fitting the machine to the training set because they refer to the model selection task, or algorithm hyperparameters, that in principle have no influence on the performance of the model but affect the speed and quality of the learning process.

Every problem worth solving needs great tools for support. Deep learning is no exception. If anything, it is a realm in which good tooling will become ever more important over the coming years. We are still in the relatively early days of the deep learning supernova, with many deep learning engineers and enthusiasts hacking their own way into efficient processes. However, we are also observing an increasing number of great tools that help facilitate the intricate process that is deep learning, making it both more accessible and more efficient.

It's the Swiss Army Knife of AI from OpenAI, a San Francisco R&D shop set up to guide a path to safe artificial general intelligence and funded by Microsoft, Reid Hoffman, and Vinod Khosla. Generative Pre-trained Transformer 3 (GPT-3) is a deep learning language model that produces human-like text. The third-generation model "is the most powerful language model ever," MIT Technology Review. The API for GPT-3 represents a sector advance more than a breakthrough. It performs like a clever student trying to fake their way through a course.