I do really appreciate these helpful on-hand labs and lectures. Passed the DP-100 in Dec 2020.

This tutorial aims to introduce the fundamentals of adversarial robustness of deep learning, presenting a well-structured review of up-to-date techniques to assess the vulnerability of various types of deep learning models to adversarial examples. This tutorial will particularly highlight state-of-the-art techniques in adversarial attacks and robustness verification of deep neural networks (DNNs). We will also introduce some effective countermeasures to improve the robustness of deep learning models, with a particular focus on adversarial training. We aim to provide a comprehensive overall picture about this emerging direction and enable the community to be aware of the urgency and importance of designing robust deep learning models in safety-critical data analytical applications, ultimately enabling the end-users to trust deep learning classifiers. We will also summarize potential research directions concerning the adversarial robustness of deep learning, and its potential benefits to enable accountable and trustworthy deep learning-based data analytical systems and applications.

If you're here, you already know the truth: Machine Learning is the future of everything. In the coming years, there won't be a single industry in the world untouched by Machine Learning. A transformative force, you can either choose to understand it now, or lose out on a wave of incredible change. You probably already use apps many times each day that rely upon Machine Learning techniques. So why stay in the dark any longer?

WIRED has challenged computer scientist and Hidden Door cofounder and CEO Hilary Mason to explain machine learning to 5 different people; a child, teen, a college student, a grad student and an expert.

This game-changing course introduces you to new-age technologies in Artificial Intelligence music creation to help you become a music star in no time. Why learn this music course and how is this a differentiator for music creators? This course can change your life if you are a music composer. Because, we will tell you the most popular Artificial Intelligence Music Creation tools that can help you compose music tracks without you – having any music knowledge whatsoever. We will also detail about the latest discovery tools in Music Mashups and also we will go through the complete tutorial of Adobe Amper and Jukedeck – great AI music assistants.

Calculus is one of the core mathematical concepts in machine learning that permits us to understand the internal workings of different machine learning algorithms. One of the important applications of calculus in machine learning is the gradient descent algorithm, which, in tandem with backpropagation, allows us to train a neural network model. In this tutorial, you will discover the integral role of calculus in machine learning. Calculus in Machine Learning: Why it Works Photo by Hasmik Ghazaryan Olson, some rights reserved. A neural network model, whether shallow or deep, implements a function that maps a set of inputs to expected outputs.

Image by the author (made using Canva). Data science is one of the rapidly growing fields that demands that a data scientist grows up daily, and I can't see this demand slowing down anytime soon. It is an interdisciplinary field that can help us analyze the data around us to make our life better and our future brighter. Luckily, becoming a data scientist does not require a degree. As long as you are open to learning new things and willing to put in the effort and time, you can become a data scientist.

TensorFlow is a state-of-the-art, open source machine learning framework created by Google to design, build, and train Machine Learning and Deep learning models. TensorFlow has a comprehensive and flexible ecosystem of tools and community resources that make it easy to develop and train ML and Deep Learning models. I know the options out there; prerequisites and the skills you need to acquire to overcome the learning blocks. So, Please refer to the Closing Notes section at the tail end of this piece, where you will find helpful resources for bootstrapping your intellectual abilities. My goal in this piece is to help you find some interactive courses from the Notable Educators that will edify you with a solid understanding of TensorFlow.

If there are any problems, you should be able to just reload the page. The code and notebooks are in a GitHub repo. The video is here YouTube.

Machine learning is a branch of artificial intelligence (AI) focused on building applications that learn from data and improve their accuracy over time without being programmed to do so. In data science, an algorithm is a sequence of statistical processing steps. In machine learning, algorithms are'trained' to find patterns and features in massive amounts of data in order to make decisions and predictions based on new data. The better the algorithm, the more accurate the decisions and predictions will become as it processes more data. Machine learning has led to some amazing results, like being able to analyze medical images and predict diseases on-par with human experts.