When Stanford undergrad Ananya Karthik was a high school freshman, she was curious about technology, but didn't know much about AI before she attended the 2016 Stanford AI4ALL summer program. Six months later, along with two AI4ALL classmates, she co-founded CreAIte, a neural art program targeting students from groups underrepresented in tech fields. Since then, CreAIte has introduced more than 500 girls around the country to coding basics, interdisciplinary technology, and peers who share their interests. Harvard computer science undergrad Catherine Yeo attended Stanford AI4ALL's inaugural program in 2015 as a high school sophomore. She went on to co-found PixelHacks, a hackathon that each year introduces hundreds of girls to tech and AI.

Perceptron is a section of machine learning which is used to understand the concept of binary classifiers. It is a part of the neural grid system. In fact, it can be said that perceptron and neural networks are interconnected. Perceptron forms the basic foundation of the neural network which is the part of Deep Learning. It is viewed as building blocks within a single layer of the neural network. A neural network which is made up of perceptron can be defined as a complex statement with a very deep understanding of logical equations. A neural statement following perceptron is either true or false but can never be both at the same time.

Learn to build decision trees for applied machine learning from scratch in Python. Decision trees are one of the hottest topics in Machine Learning. They dominate many Kaggle competitions nowadays. This course covers both fundamentals of decision tree algorithms such as CHAID, ID3, C4.5, CART, Regression Trees and its hands-on practical applications. Besides, we will mention some bagging and boosting methods such as Random Forest or Gradient Boosting to increase decision tree accuracy.

I have done an introduction to Artificial Intelligence (AI) course and I want to share my learning experience. This post covers my notes and summaries of the content of the course. On the early sixties, there was a gathering between several investigators interested on artificial intelligence, neural networks and automats theory as a consequence of the first works made on the field. The problem resolution was based on a general purpose search engine with high cost. To lower that cost, the first search algorithms were developed, like Heuristic Search and Alpha Beta Search.

Learn to capture videos, manipulate images, and track objects with Python using the OpenCV Library Overview Set up OpenCV, its Python bindings, and optional Kinect drivers on Windows, Mac or Ubuntu Create an application that tracks and manipulates faces Identify face regions using normal color images and depth images In Detail Computer Vision can reach consumers in various contexts via webcams, camera phones and gaming sensors like Kinect. OpenCV's Python bindings can help developers meet these consumer demands for applications that capture images, change their appearance and extract information from them, in a high-level language and in a standardized data format that is interoperable with scientific libraries such as NumPy and SciPy. "OpenCV Computer Vision with Python" is a practical, hands-on guide that covers the fundamental tasks of computer vision--capturing, filtering and analyzing images--with step-by-step instructions for writing both an application and reusable library classes. "OpenCV Computer Vision with Python" shows you how to use the Python bindings for OpenCV. By following clear and concise examples you will develop a computer vision application that tracks faces in live video and applies special effects to them.

Amazon SageMaker is a fully managed service that provides every developer and data scientist with the ability to build, train, and deploy machine learning (ML) models quickly. Amazon SageMaker removes the heavy lifting from each step of the ML process to make it easier to develop high-quality models. As datasets continue to increase in size, additional compute is required to reduce the amount of time it takes to train. One method to scale horizontally and add these additional resources on Amazon SageMaker is through the use of Horovod and Apache MXNet. In this post, we show how you can reduce training time with MXNet and Horovod on Amazon SageMaker.

Become a professional Python Developer and get hired Master modern Python 3 fundamentals as well as advanced topics Learn Object Oriented Programming Learn Function Programming Build 12 real world Python projects you can show off Learn how to use Python in Web Development Learn Machine Learning with Python Build a Machine Learning Model Learn Data Science - Analyze and Visualize Data Build a professional Portfolio Website Use Python to process: Images, CSVs, PDFs, and other Files Build a Web Scraper with Python and BeautifulSoup Use Python to send Emails and SMS Use Python to build a Twitter bot Learn to Test, Debug and Handle Errors in your Python programs Learn best practices to write clean, performant, and bug free code Learn to use Selenium and Python in Automation Set up a professional workspace with Jupyter Notebooks, PyCharm, VS Code more Become a complete Python developer! Join a live online community of over 150,000 developers and a course taught by an industry expert that has actually worked both in Silicon Valley and Toronto. This is a brand new Python course just launched September 2019! Graduates of Andrei's courses are now working at Google, Amazon, Apple, IBM, JP Morgan, Facebook, other top tech companies.Learn Python from scratch, get hired, and have fun along the way with the most modern, up-to-date Python course on Udemy (we use the latest version of Python). This course is focused on efficiency: never spend time on confusing, out of date, incomplete Python tutorials anymore.

One cannot, in all their seriousness, comprehend what went into writing the "Requiem for a dream" or painting the frescoes on the ceiling of the Sistine Chapel. Art is a consequence; of intelligence and experience. But, what happens when this intelligence is augmented with an external entity, an algorithm? Artificial intelligence has intruded into the space of creativity, the final frontier of the human intellect, through algorithms such as Generative Adversarial Networks (GANs). GANs have become fertile tools for artistic exploration.

Strategic recommendations (SR) refer to the problem where an intelligent agent observes the sequential behaviors and activities of users and decides when and how to interact with them to optimize some long-term objectives, both for the user and the business. These systems are in their infancy in the industry and in need of practical solutions to some fundamental research challenges. At Adobe research, we have been implementing such systems for various use-cases, including points of interest recommendations, tutorial recommendations, next step guidance in multi-media editing software, and ad recommendation for optimizing lifetime value. There are many research challenges when building these systems, such as modeling the sequential behavior of users, deciding when to intervene and offer recommendations without annoying the user, evaluating policies offline with high confidence, safe deployment, non-stationarity, building systems from passive data that do not contain past recommendations, resource constraint optimization in multi-user systems, scaling to large and dynamic actions spaces, and handling and incorporating human cognitive biases. In this paper we cover various use-cases and research challenges we solved to make these systems practical.

This paper takes a first step towards a critical thinking curriculum for neural auto-regressive language models. We introduce a synthetic text corpus of deductively valid arguments, and use this artificial argument corpus to train and evaluate GPT-2. Significant transfer learning effects can be observed: Training a model on a few simple core schemes allows it to accurately complete conclusions of different, and more complex types of arguments, too. The language models seem to connect and generalize the core argument schemes in a correct way. Moreover, we obtain consistent and promising results for the GLUE and SNLI benchmarks. The findings suggest that there might exist a representative sample of paradigmatic instances of good reasoning that will suffice to acquire general reasoning skills and that might form the core of a critical thinking curriculum for language models.