Collaborating Authors


4 Pre-Trained CNN Models to Use for Computer Vision with Transfer Learning


ResNet50 is a convolutional neural network which has a depth of 50 layers. It was build and trained by Microsoft in 2015 and you can access the model performance results on their paper, titled Deep Residual Learning for Image Recognition. This model is also trained on more than 1 million images from the ImageNet database. Just like VGG-19, it can classify up to 1000 objects and the network was trained on 224x224 pixels colored images.

Train Neural Networks Using a Genetic Algorithm in Python with PyGAD


The genetic algorithm (GA) is a biologically-inspired optimization algorithm. It has in recent years gained importance, as it's simple while also solving complex problems like travel route optimization, training machine learning algorithms, working with single and multi-objective problems, game playing, and more. Deep neural networks are inspired by the idea of how the biological brain works. It's a universal function approximator, which is capable of simulating any function, and is now used to solve the most complex problems in machine learning. What's more, they're able to work with all types of data (images, audio, video, and text).

100% OFF Python OOP : Object Oriented Programming in Python


This "Python OOP: Object Oriented Programming in Python" course provides good understanding of object oriented concepts and implementation in Python programming. Design and development of a product requires great understanding of implementation language. The complexity of real world application requires the use of strength of language to provide robust, flexible and efficient solutions. Python provides the Object Oriented capability and lot of rich features to stand with changing demand of current world application requirement. This "Python OOP: Object Oriented Programming in Python" tutorial explains the Object Oriented features of Python programming in step-wise manner.

Learning Data Science Has Never Been Easier


In this article, I will discuss several resources that can help you master the foundations of data science. In the modern age of information technology, there is an enormous amount of free resources for data science self-study. As a matter of fact, you can design your own data science curriculum from the innumerable amount of available resources. The rising demand for data science practitioners has given rise to a proliferation of massive open online courses (MOOC). If you are going to be taking one of these courses, keep in mind that some MOOCs are 100% free, while some do require you to pay a subscription fee (it could range anywhere from $50 to $200 per course or more, varies from platforms to platforms).

OpenCV Sudoku Solver and OCR - PyImageSearch


In this tutorial, you will create an automatic Sudoku puzzle solver using OpenCV, Deep Learning, and Optical Character Recognition (OCR). My wife is a huge Sudoku nerd. Every time we travel, whether it be a 45-minute flight from Philadelphia to Albany or a 6-hour transcontinental flight to California, she always has a Sudoku puzzle with her. The funny thing is, she prefers the printed Sudoku puzzle books. She hates the digital/smartphone app versions and refuses to play them. I'm not a big puzzle person myself, but one time, we were sitting on a flight, and I asked: How do you know if you solved the puzzle correctly?

Natural Language Processing (NLP) with Python -- Tutorial


Author(s): Andreea Bodnari Artificial Intelligence, Neuroscience Can we invent artificial intelligence ex nihilo, without peeking at human intelligence for inspiration? Intelligence is a determining factor for success and prosperity.

OCR a document, form, or invoice with Tesseract, OpenCV, and Python - PyImageSearch


In this tutorial, you will learn how to OCR a document, form, or invoice using Tesseract, OpenCV, and Python. On the left, we have our template image (i.e., a form from the United States Internal Revenue Service). The middle figure is our input image that we wish to align to the template (thereby allowing us to match fields from the two images together). And finally, the right shows the output of aligning the two images together. At this point, we can associate text fields in the form with each corresponding field in the template, meaning that we know which locations of the input image map to the name, address, EIN, etc. fields of the template: Knowing where and what the fields are allows us to then OCR each individual field and keep track of them for further processing, such as automated database entry.

Deepfake Fiascos Of 2020 That Made Headlines


Deepfakes are indeed scary and have managed to strike a nerve for many, especially the ones being victimised for this sophisticated technology. Not only has it become a worldwide concern for many due to its influential impact on election campaigns but also made people anxious due to the criminal activity associated with it. With easily accessible deepfake making tools available for anybody to use and advancements in GANs has made it relatively easy for notorious minds to create these eerie-looking unreal AI-generated videos and images. Such improvement and accessibility has in turn increased the number of deepfake incidents in recent times. Some of them are so incredibly convincing that they manage to surpass the original videos. This news showcased one of the weirder applications of deep fakes, that used artificial intelligence to manipulate an audio-visual content -- a less heard usage, termed as audio deepfake scam.

Free Online Resources To Get Hands-On Deep Learning


With deep learning gaining its momentum in fields like self-driving cars, object detection, voice assistants and text generation, to name a few, the demand for deep learning experts in organisations has also significantly increased. As a matter of fact, big tech companies like Facebook, Google, Apple as well as Microsoft have started investing heavily on deep learning projects which, in turn, increase the number of deep learning open jobs in the market. Having said that, deep learning is one of the complex subsets of machine learning and envelops several layers of components which cannot be grasped in a day. Hence, despite the high demand, there is indeed a gap in deep learning talent for organisations. Not only does it come with prerequisites of linear algebra and calculus knowledge but also enough interest to pursue a complicated subject like deep learning.