The first programming exercise "Twitter Sentiment Analysis in Python" is both fun and challenging, where you analyze tons of twitter message to find out the sentiments e.g.

Computer Vision is the interdisciplinary field of artificial intelligence and computer science, is basically the transition of data from either a still or a video camera into an accurate representation. Just like human vision, a computer vision also works on validating the computers to visualise, recognise and process images. One of the most buzzing fields under artificial intelligence, computer vision has found plenty of use cases in the industry. There are many resources available to come up to speed with computer vision. In this article, we list down 5 best free resources that will come handy in learning computer vision. The list is in no particular order.

Jaana Partanen is not your typical AI programming geek. Until a year ago, the 59-year-old dentist from the Finnish town of Mikkeli had no idea what to make of terms like "machine learning" or "neural networks." Now, Partanen spends her evenings learning the basics of coding and she is thinking about how to apply artificial intelligence to her job, either to help write up medical summaries or perform orthodontics. "I can see it [artificial intelligence] is already here, and it serves us -- very much actually," she said, adding that following the latest developments in the field has become a hobby. She's one of tens of thousands of non-technology experts who are taking part in a grand experiment aimed at repurposing the country's economy toward high-end applications of artificial intelligence. The idea has a simple, Nordic ring to it: Start by teaching 1 percent of the country's population, or about 55,000 people, the basic concepts at the root of artificial technology, and gradually build on the number over the next few years.

In this tutorial, you will learn how the Keras .fit To help you gain hands-on experience, I've included a full example showing you how to implement a Keras data generator from scratch. Today's blog post is inspired by PyImageSearch reader, Shey. I've been methodically going through every one. They've really helped me learn deep learning.

This is Part 5 of the tutorial on implementing a YOLO v3 detector from scratch. In the last part, we implemented a function to transform the output of the network into detection predictions. With a working detector at hand, all that's left is to create input and output pipelines. The code for this tutorial is designed to run on Python 3.5, and PyTorch 0.4. It can be found in it's entirety at this Github repo.

My course does exactly what the title describes in a simple, relatable way. I help you to grasp the complete start to end concepts of fundamental deep learning. On your own it can be quite confusing, difficult and frustrating. I've been through the process myself, and with the help of lifelong ... I want to share this with my fellow beginners, developers, AI aspirers, with you. I will give you straightforward examples, instructions, advice, insights and resources for you to take simple steps to create your own neural networks from scratch.

Artificial Intelligence has moved from the big wide screens from films such as Terminator. It has, in fact, moved into our daily life. AI has been a success with improvements in various fields such as mechanical engineering, agriculture and many more. Even healthcare, which stayed a little far away from the digital revolution has embraced AI in tasks that involved low level manual labour. In this article, let us discuss on the advantages of Artificial Intelligence in Education sector.

This is Part 4 of the tutorial on implementing a YOLO v3 detector from scratch. In the last part, we implemented the forward pass of our network. In this part, we threshold our detections by an object confidence followed by non-maximum suppression. The code for this tutorial is designed to run on Python 3.5, and PyTorch 0.4. It can be found in it's entirety at this Github repo.

Swarm control is a difficult problem due to the need to guide a large number of agents simultaneously. We cast the problem as a shepherding problem, similar to biological dogs guiding a group of sheep towards a goal. The shepherd needs to deal with complex and dynamic environments and make decisions in order to direct the swarm from one location to another. In this paper, we design a novel curriculum to teach an artificial intelligence empowered agent to shepherd in the presence of the large state space associated with the shepherding problem and in a transparent manner. The results show that a properly designed curriculum could indeed enhance the speed of learning and the complexity of learnt behaviours.

The demand for skills in artificial intelligence (AI) and specifically machine learning has been growing exponentially over the past five years, as businesses from online entertainment to eCommerce scramble for new ways to utilize data to improve customer experience and realize new features. Simplilearn recently appointed Mike Tamir, Ph.D., as the Advisor for Simplilearn's Artificial Intelligence and Machine Learning curricula. He has been instrumental in developing the course structure and incorporating advanced programs on AI Engineering, Machine Learning and Deep Learning with TensorFlow. Dr. Tamir is ranked number one globally as an influencer for Machine Learning and AI by Onalytica and currently serves as Head of Data Science at Uber ATG (self-driving vehicles) and is a lecturer for the University of California, Berkeley - iSchool Data Science Master's Program. Recently, Simplilearn spoke with Mike Tamir about his insights, predictions, and recommendations about machine learning and how both businesses and career-seekers could prepare themselves for the future.