If you are like a measurable amount of programmers out there then you may be interested in Machine Learning(ML). More specifically you might be inspired by hearing or reading about stories of success in the ML industry: from self-driving cars, to robots that can learn to walk and jump, to automations that help advertise better, to trading robots that can make decisions without discretionary input from an investor. The future is certainly an exciting and somewhat scary journey we are going through together. Recently, the advancement of technologies and open source / private projects have brought the ML field into the hands of the people by decentralizing much of the advanced power behind this industry. If you have heard of names like TensorFlow, PyTorch, Sklearn, XGBoost, or Keras, to name a few, you know what I am talking about.

Created by Lazy Programmer Inc., Lazy Programmer Team Students also bought Complete Tensorflow 2 and Keras Deep Learning Bootcamp Complete Guide to TensorFlow for Deep Learning with Python Modern Deep Learning in Python TensorFlow 2.0 Practical Deep Learning with TensorFlow 2.0 [2020] Preview this Udemy Course GET COUPON CODE Description Welcome to Tensorflow 2.0! It's been nearly 4 years since Tensorflow was released, and the library has evolved to its official second version. Tensorflow is Google's library for deep learning and artificial intelligence. Deep Learning has been responsible for some amazing achievements recently, such as: Generating beautiful, photo-realistic images of people and things that never existed (GANs) Beating world champions in the strategy game Go, and complex video games like CS:GO and Dota 2 (Deep Reinforcement Learning) Self-driving cars (Computer Vision) Speech recognition (e.g. Siri) and machine translation (Natural Language Processing) Even creating videos of people doing and saying things they never did (DeepFakes - a potentially nefarious application of deep learning) Tensorflow is the world's most popular library for deep learning, and it's built by Google, whose parent Alphabet recently became the most cash-rich company in the world (just a few days before I wrote this).

The new buzzword shaking the global business arena is machine learning. It's grabbed the public's imagination, conjuring up images of self-learning AI and robots in the future. Machine learning has prepared the path for technical advancements and tools in manufacturing that would have been unthinkable just a few years ago. It drives the breakthrough technologies that sustain our ways of living, from prediction machines to online TV live streaming. If words like deep learning, neural learning, and artificial intelligence spark your interest, we have a great list of free machine learning courses you can begin with right now.

I recently started an AI-focused educational newsletter, that already has over 80,000 subscribers. TheSequence is a no-BS (meaning no hype, no news etc) ML-oriented newsletter that takes 5 minutes to read. The goal is to keep you up to date with machine learning projects, research papers and concepts. In his book "The Master Algorithm", artificial intelligence researcher Pedro Domingos explores the idea of a single algorithm that can combine the major schools of machine learning. The idea is, without a doubt, extremely ambitious but we are already seeing some iterations of it. Last year, Google published a research paper under the catchy title of "One Model to Learn Them All" that combines heterogeneous learning techniques under a single machine learning model.

The term Artificial Intelligence (AI) was used for the first time by John McCarthy during a workshop in 1956 at Dartmouth College. The first AI application programs for playing checker and chess were developed in 1951. After the '50s, AI was on the rise and fall until the 2010s. Over the years, there have been some investments in AI by vendors, universities, institutions. Sometimes, hopes were high and sometimes hopes were low.

We are looking for a highly creative and motivated PhD candidate to join the Data Mining Group at Eindhoven University of Technology. The candidate will be supervised by Dr. Vlado Menkovski, PI of the project and work within the Immuno-engineering research program, which is a collaboration with the department of Biomedical Engineering. Machine Learning models, particularly deep neural networks, have shown outstanding capabilities to deliver accurate prediction in high dimensional settings [1, 2]. However, the goal of the scientific inquiry goes beyond prediction and aims at explanations that can be integrated with the existing knowledge. In the context of data-driven scientific discovery, there are certain unique challenges.

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In September 2018, I compared all the major deep learning frameworks in terms of demand, usage, and popularity in this article. TensorFlow was the undisputed heavyweight champion of deep learning frameworks. PyTorch was the young rookie with lots of buzz. How has the landscape changed for the leading deep learning frameworks in the past six months? To answer that question, I looked at the number of job listings on Indeed, Monster, LinkedIn, and SimplyHired.

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The threats faced by cyber-physical systems (CPSs) in critical infrastructure have motivated research into a multitude of attack detection mechanisms, including anomaly detectors based on neural network models. The effectiveness of anomaly detectors can be assessed by subjecting them to test suites of attacks, but less consideration has been given to adversarial attackers that craft noise specifically designed to deceive them. While successfully applied in domains such as images and audio, adversarial attacks are much harder to implement in CPSs due to the presence of other built-in defence mechanisms such as rule checkers(or invariant checkers). In this work, we present an adversarial attack that simultaneously evades the anomaly detectors and rule checkers of a CPS. Inspired by existing gradient-based approaches, our adversarial attack crafts noise over the sensor and actuator values, then uses a genetic algorithm to optimise the latter, ensuring that the neural network and the rule checking system are both deceived.We implemented our approach for two real-world critical infrastructure testbeds, successfully reducing the classification accuracy of their detectors by over 50% on average, while simultaneously avoiding detection by rule checkers. Finally, we explore whether these attacks can be mitigated by training the detectors on adversarial samples.