This is the second course from my Computer Vision series. Face Detection and Face Recognition is the most used applications of Computer Vision. Using these techniques, the computer will be able to extract one or more faces in an image or video and then compare it with the existing data to identify the people in that image. Face Detection and Face Recognition is widely used by governments and organizations for surveillance and policing. We are also making use of it daily in many applications like face unlocking of cell phones etc.

Welcome to the best Natural Language Processing course on the internet! This course is designed to be your complete online resource for learning how to use Natural Language Processing with the Python programming language. In the course we will cover everything you need to learn in order to become a world class practitioner of NLP with Python. We'll start off with the basics, learning how to open and work with text and PDF files with Python, as well as learning how to use regular expressions to search for custom patterns inside of text files. Afterwards we will begin with the basics of Natural Language Processing, utilizing the Natural Language Toolkit library for Python, as well as the state of the art Spacy library for ultra fast tokenization, parsing, entity recognition, and lemmatization of text.

Computer Vision Masterclass, Learn in practice everything you need to know about Computer Vision! Build projects step by step using Python! Understand the basic intuition about Cascade and HOG classifiers to detect faces Implement face detection using OpenCV and Dlib library Learn how to detect other objects using OpenCV, such as cars, clocks, eyes, and full body of people Compare the results of three face detectors: Haarcascade, HOG (Histogram of Oriented Gradients) and CNN (Convolutional Neural Networks) Detect faces using images and the webcam Understand the basic intuition about LBPH algorithm to recognize faces Implement face recognition using OpenCV and Dlib library Recognize faces using images and the webcam Understand the basic intuition about KCF and CSRT algorithms to perform object tracking Learn how to track objects in videos using OpenCV library Learn everything you need to know about the theory behind neural networks, such as: perceptron, activation functions, weight update, backpropagation, gradient descent and a lot more Implement dense neural networks to classify images Learn how to extract pixels and features from images in order to build neural networks Learn the theory behind convolutional neural networks and implement them using Python and TensorFlow Implement transfer learning and fine tuning to get incredible results when classifying images Use convolutional neural networks to classify the following emotions in images and videos: happy, anger, disgust, fear, surprise and neutral Compress images using linear and convolutional autoencoders Detect objects in images in videos using YOLO, one of the most powerful algorithms today Recognize gestures and actions in videos using OpenCV Learn how to create hallucinogenic images with Deep Dream Learn how to revive famous artists with style transfer Create images that don't exist in the real world with GANs (Generative Adversarial Networks) Implement image segmentation do extract useful information from images and videos Create images that don't exist in the real world with GANs (Generative Adversarial Networks) Computer Vision is a subarea of Artificial Intelligence focused on creating systems that can process, analyze and identify visual data in a similar way to the human eye. There are many commercial applications in various departments, such as: security, marketing, decision making and production. Smartphones use Computer Vision to unlock devices using face recognition, self-driving cars use it to detect pedestrians and keep a safe distance from other cars, as well as security cameras use it to identify whether there are people in the environment for the alarm to be triggered. In this course you will learn everything you need to know in order to get in this world.

AI is a broad and rapidly developing field of technology. Our goal is to make sure all young people have the skills, knowledge, and confidence to use and create AI systems. So what should AI education in schools look like? To hear a range of insights into this, we organised a panel discussion as part of our seminar series on AI and data science education, which we co-host with The Alan Turing Institute. You can also watch the recording below.

Whether you are an algorithm developer in a growing startup company, a data scientist in a university research lab, or a kaggle hobbyist, there may come a point in time when the training resources that you have onsite no longer meet your training demands. In this post we target development teams that are (finally) ready to move their machine learning (ML) workloads to the cloud. We will discuss some of the important decisions that need to made during this big transition. Naturally, any attempt to encompass all of the steps of such an endeavor is doomed to fail. Machine learning projects come in many shapes and forms and as their complexity increases so does the undertaking of making such a significant change as migrating to the cloud. In this post we will highlight what we believe to be some of the most important considerations that are common to most typical deep learning projects.

Hi everyone, this will be the second instalment in my tutorial series for building a chess engine. This lesson will focus on building an AI agent that we can play. This lesson is going to be more technical than part 1, so please bear with me. I try to supply both equations and diagrams to help make things a little easier. Now that we have finished building our chess game, we can begin designing an AI that plays it.

Modern Natural Language Processing course is designed for anyone who wants to grow or start a new career and gain a strong background in NLP. Nowadays, the industry is becoming more and more in need of NLP solutions. Chatbots and online automation, language modeling, event extraction, fraud detection on huge contracts are only a few examples of what is demanded today. Learning NLP is key to bring real solutions to the present and future needs. Throughout this course, we will leverage the huge amount of speech and text data available online, and we will explore the main 3 and most powerful NLP applications, that will give you the power to successfully approach any real-world challenge.

Latest update: Instead of SSD, I show you how to use RetinaNet, which is better and more modern. I show you both how to use a pretrained model and how to train one yourself with a custom dataset on Google Colab. This is one of the most exciting courses I've done and it really shows how fast and how far deep learning has come over the years. When I first started my deep learning series, I didn't ever consider that I'd make two courses on convolutional neural networks. I think what you'll find is that, this course is so entirely different from the previous one, you will be impressed at just how much material we have to cover.

The lectures will examine what Russell will argue is the most profound change in human history as the world becomes increasingly reliant on super-powerful AI. Examining the impact of AI on jobs, military conflict and human behaviour, Russell will argue that our current approach to AI is wrong and that if we continue down this path, we will have less and less control over AI at the same time as it has an increasing impact on our lives. How can we ensure machines do the right thing? The lectures will suggest a way forward based on a new model for AI, one based on machines that learn about and defer to human preferences. The series of lectures will be held in four locations across the UK; Newcastle, Edinburgh, Manchester and London and will be broadcast on Radio 4 and the World Service as well as available on BBC Sounds.

AI is more accessible to young people in the workforce who grew up as'digital natives' with Alexa and self-driving cars as part of the landscape, giving them expectations grounded in their experience of what is possible. That idea set the foundation for a panel discussion at AI World Government on Mindset Needs and Skill Set Myths for AI engineering teams, held this week virtually and in-person in Alexandria, Va. "People feel that AI is within their grasp because the technology is available, but the technology is ahead of our cultural maturity," said panel member Dorothy Aronson, CIO and Chief Data Officer for the National Science Foundation. We might have access to big data, but it might not be the right thing to do," to work with it in all cases. Things are accelerating, which is raising expectations. When panel member Vivek Rao, lecturer and researcher at the University of California at Berkeley, was working on his PhD, a paper on natural language processing might be a master's thesis. "Now we assign it as a homework assignment with a two-day turnaround.