This is the 1st article of series "Coding Deep Learning for Beginners". You will be able to find here links to all articles, agenda, and general information about an estimated release date of next articles on the bottom. They are also available in my open source portfolio -- MyRoadToAI, along with some mini-projects, presentations, tutorials and links. You can also read the article on Medium. If you read this article I assume you want to learn about one of the most promising technologies -- Deep Learning. Statement "AI is a new electricity" becomes more and more popular lately.

I've discussed parts of what I'm going to mention here in other articles, but now I want to give a few directions on what's not data science and how not to learn it. So let's start with the basics. Data science not just knowing some programming languages, math, statistics and have "domain knowledge". We've created a new field, or something like that. There's a lot of things to say and study in this field.

Bayesian methods promise to fix many shortcomings of deep learning, but they are impractical and rarely match the performance of standard methods, let alone improve them. In this paper, we demonstrate practical training of deep networks with natural-gradient variational inference. By applying techniques such as batch normalisation, data augmentation, and distributed training, we achieve similar performance in about the same number of epochs as the Adam optimiser, even on large datasets such as ImageNet. Importantly, the benefits of Bayesian principles are preserved: predictive probabilities are well-calibrated and uncertainties on out-of-distribution data are improved. This work enables practical deep learning while preserving benefits of Bayesian principles. A PyTorch implementation will be available as a plug-and-play optimiser.

A gentle guide to deep learning object detection Today's blog post is inspired by PyImageSearch reader Ezekiel, who emailed me last week and asked: Hey Adrian, with the followup tutorial for real-time deep learning object detection . I've been using your source code in my example projects but I'm having two issues: How do I filter/ignore classes that I am uninterested in? How can I add new classes to my object detector? I would really appreciate it if you could cover this in a blog post. In fact, if you go through the comments section of my two most recent posts on deep learning object detection (linked above), you'll find that one of the most common questions is typically (paraphrased): How do I modify your source code to include my own object classes? Since this appears to be such a common question, and ultimately a misunderstanding on how neural networks/deep learning object detectors actually work, I decided to revisit the topic of deep learning object detection in today's blog post. Specifically, in this post you will learn: The differences between image classification and object detection The components of a deep learning object detector including the differences between an object detection framework and the base model itself How to perform deep learning object detection with a pre-trained model How you can filter and ignore predicted classes from a deep learning model Common misconceptions and misunderstandings when adding or removing classes from a deep neural network To learn more about deep learning object detections, and perhaps even debunk a few misconceptions or misunderstandings you may have with deep learning-based object detection, just keep reading. A gentle guide to deep learning object detection Today's blog post is meant to be a gentle introduction to deep learning-based object detection. I've done my best to provide a review of the components of deep learning object detectors, including OpenCV Python source code to perform deep learning using a pre-trained object detector.

Before the meeting, the head of state was told about the academic process at School 21 and had a brief conversation with students. The President was informed about the school by Head of Sberbank German Gref and school Principal Svetlana Infimovskaya. The students of the school can study the following areas: Algorithms, Graphics, Mobile Development, Computer Security, Robot Technology, and Artificial Intelligence to name a few. The school has 940 students today. On average, students are expected to study for 2–3.5 years. The course includes two practical training sessions in relevant companies for six months or more. Today I suggest that we discuss concrete steps that will form the foundation for our National Strategy on the development of artificial intelligence technologies. We have repeatedly spoken about the need for such a comprehensive document. I also mentioned it in this year's Address to the Federal Assembly. This is indeed one of the key areas of technological development that determines and will continue to determine the future of the entire world. The artificial intelligence mechanisms will allow for quick real-time decision-making based on analysing vast amounts of information known as big data, which provides tremendous advantages in terms of quality and performance. In addition, such mechanisms are unparalleled in history in terms of their impact on the economy and productivity, the effectiveness of management, education, healthcare and daily life. However, vying for technological leadership, primarily, in the sphere of artificial intelligence – and you are all very well aware of this, colleagues – has already lead to global competition. New products and solutions are being created at an exponential growth rate. I have said it before and I will say it now: he who can establish a monopoly in artificial intelligence – we are aware of the consequences – will rule the world. It is no accident that many developed countries of the world have already adopted action plans to develop such technologies. Of course, we must ensure technological sovereignty in the realm of artificial intelligence. This is the most important prerequisite for the viability of our businesses and the economy, the quality of life for Russian citizens, security and, finally, our defence capability. Here, we are not just talking about algorithms for addressing individual and highly specialised problems; what we need are universal solutions, the use of which gives the optimum effect in any industry. In order to achieve such an ambitious goal in AI technology, we are objectively positioned to have a good start and we have a serious competitive edge. Today, Russia boasts one of the world's highest penetration rates for mobile communications and internet access, as well as for the development of electronic services.

What you will learn in this course? In this course, you'll work with more complex environments, specifically provided by the OpenAI Gym: CartPole Mountain Car Atari games to train effective learning agents so you'll need new techniques. We've seen that reinforcement learning is an entirely different kind of machine learning than supervised and unsupervised learning.Supervised and unsupervised machine learning algorithms are for making predictions about data and analyzing, while reinforcement learning is about training an agent to interact with an environment and maximize its reward. Deep reinforcement learning and AI has a lot of potentials also carries huge risk. One main principle of training reinforcement learning agents is that there are unintended consequences when training an AI.

Batteryless or so called passive wearables are providing new and innovative methods for human activity recognition (HAR), especially in healthcare applications for older people. Passive sensors are low cost, lightweight, unobtrusive and desirably disposable; attractive attributes for healthcare applications in hospitals and nursing homes. Despite the compelling propositions for sensing applications, the data streams from these sensors are characterised by high sparsity---the time intervals between sensor readings are irregular while the number of readings per unit time are often limited. In this paper, we rigorously explore the problem of learning activity recognition models from temporally sparse data. We describe how to learn directly from sparse data using a deep learning paradigm in an end-to-end manner. We demonstrate significant classification performance improvements on real-world passive sensor datasets from older people over the state-of-the-art deep learning human activity recognition models. Further, we provide insights into the model's behaviour through complementary experiments on a benchmark dataset and visualisation of the learned activity feature spaces.

What's the earliest we can predict cancer survival rates, and what schools do the best job of educating children? You can only answer these questions with very rare access to private and personal data, but access to this personal data requires that you master methods for the principled protection of user privacy. While not all privacy use cases have been solved, the last few years have seen great strides in privacy-preserving technologies. This free course will introduce you to three cutting-edge technologies for privacy-preserving AI: Federated Learning, Differential Privacy, and Encrypted Computation. You will learn how to use the newest privacy-preserving technologies, such as OpenMined's PySyft.

The Python Mega Course is one of the top online Python courses with over 100,000 enrolled students and is targeted toward people with little or no previous programming experience. The course follows a modern-teaching approach where students learn by doing. You will start from scratch and master Python by building 10 real-world applications in Python 3, guided and supported by the course instructor. What you'll learn Go from a total beginner to an advanced-Python programmer Create 10 real-world Python programs (no Tic-Tac-Toe games) Solidify your skills with bonus practice activities throughout the course Create an app that translates English words Create a web-mapping app Create a portfolio website Create a desktop app for storing book information Create a webcam video app that detects objects Create a web scraper Create a data visualization app Create a database app Create a geocoding web app Create a website blocker Send automated emails Analyze and visualize data Use Python to schedule programs based on computer events. Go from a total beginner to an advanced-Python programmer Create 10 real-world Python programs (no Tic-Tac-Toe games) Solidify your skills with bonus practice activities throughout the course Create an app that translates English words Create a web-mapping app Create a portfolio website Create a desktop app for storing book information Create a webcam video app that detects objects Create a web scraper Create a data visualization app Create a database app Create a geocoding web app Create a website blocker Send automated emails Analyze and visualize data Use Python to schedule programs based on computer events.

In many real-world sequential decision making problems, the number of available actions (decisions) can vary over time. While problems like catastrophic forgetting, changing transition dynamics, changing rewards functions, etc. have been well-studied in the lifelong learning literature, the setting where the action set changes remains unaddressed. In this paper, we present an algorithm that autonomously adapts to an action set whose size changes over time. To tackle this open problem, we break it into two problems that can be solved iteratively: inferring the underlying, unknown, structure in the space of actions and optimizing a policy that leverages this structure. We demonstrate the efficiency of this approach on large-scale real-world lifelong learning problems.