Artificial intelligence (AI) has already transformed our lives, and it keeps altering different aspects of daily life. From driving autonomous vehicles to translating languages in real time, AI technologies can considerably improve the efficiency of every process in our diverse workplaces. That is why the Data Science Program at the College of Charleston is offering a four-day workshop for students to learn how AI can be integrated into different fields of science and technology. Sponsored by the South Carolina Council on Competitiveness, the Applications of Artificial Intelligence workshop was designed to teach about artificial intelligence through instructional and hands-on training activities. Workshop components include AI in Music and Art, AI in Natural Language Processing, AI in Smart Cities and Autonomous Vehicles, AI in Environmental Informatics, AI in Education and AI at Home.

In this step-by-step tutorial, you'll build a neural network from scratch as an introduction to the world of artificial intelligence (AI) in Python. You'll learn how to train your neural network and make accurate predictions based on a given dataset.

Did you know that computers can generate pictorial art? Have you ever wondered how can they do that? Did you know that the art generated by Artificial Intelligence (AI) has been sold on auctions for thousands of $$$? Here you will learn how to become Artificial Intelligence (AI) Artisan by building, training and applying Generative Artificial Networks (GANs) - deep learning networks behind AI generated art. To get a taster on the kind of images you'll be able to generate by completing this course, have a look at the logo of this course, which has 6 examples of images generated with GANs:) The course uses Python as a programming language upon which you'll be able to build, train and use your own GANs.

Do you want to learn Tensorflow and looking for the Best Resources to Learn Tensorflow?… If yes, you are in the right place. In this article, I have listed all the best resources to learn Tensorflow including Online Courses, Tutorials, Books, and YouTube Videos. So, give your few minutes and find out the best resources to learn Tensorflow. You can bookmark this article so that you can refer to this article later.

In this course, you will learn the basics of deep learning and how to build your first deep learning model using Keras. This course will teach supervised deep learning models, such as convolutional neural networks and recurrent neural networks, and how to build a convolutional neural network using the Keras library. The course material of this course is available free, but for a certificate, you have to pay. In this course, you will also learn how do neural networks learn and what are activation functions.

Inductive logic programming (ILP) is a form of machine learning. The goal of ILP is to induce a hypothesis (a set of logical rules) that generalises training examples. As ILP turns 30, we provide a new introduction to the field. We introduce the necessary logical notation and the main learning settings; describe the building blocks of an ILP system; compare several systems on several dimensions; describe four systems (Aleph, TILDE, ASPAL, and Metagol); highlight key application areas; and, finally, summarise current limitations and directions for future research.

Editor's note: In the interest of full transparency, Machine Learning Mastery is KDnuggets' sister site. The author was presented a copy of the book in question and granted full autonomy over their review. Most of the people I know can build, validate, and deploy machine learning models, but they don't know the basics of Python language. Their primary focus is on model architects instead of learning production-ready coding practices. These software engineering practices are necessary that will make you productive.

Deep Learning Basics, NLP, Computer Vision, Time Series Forecasting, Audio Data. “Beginner-Friendly Kaggle Notebooks to Learn Deep Learning” is published by Ebrahim Haque Bhatti.

If you are a software developer who wants to build scalable AI-powered algorithms, you need to understand how to use the tools to build them. This course is part of the upcoming Machine Learning in Tensorflow Specialization and will teach you best practices for using TensorFlow, a popular open-source framework for machine learning. In Course 2 of the deeplearning.ai TensorFlow Specialization, you will learn advanced techniques to improve the computer vision model you built in Course 1. You will explore how to work with real-world images in different shapes and sizes, visualize the journey of an image through convolutions to understand how a computer "sees" information, plot loss and accuracy, and explore strategies to prevent overfitting, including augmentation and dropout.

This intermediate-level course introduces the mathematical foundations to derive Principal Component Analysis (PCA), a fundamental dimensionality reduction technique. We'll cover some basic statistics of data sets, such as mean values and variances, we'll compute distances and angles between vectors using inner products and derive orthogonal projections of data onto lower-dimensional subspaces. Using all these tools, we'll then derive PCA as a method that minimizes the average squared reconstruction error between data points and their reconstruction. At the end of this course, you'll be familiar with important mathematical concepts and you can implement PCA all by yourself. If you're struggling, you'll find a set of jupyter notebooks that will allow you to explore properties of the techniques and walk you through what you need to do to get on track.