Whether you are a complete beginner who would like to begin your first machine learning project or a machine learning expert who wants to expand your boundary, anyone who wishes to learn machine learning is welcome. Feel free to share any educational resources of machine learning. An educational resource could be anything from a professional blog article to tips you would like to share to the fellow redditors. Also, we are a beginner-friendly subreddit, so don't be afraid to ask questions! This can include questions that are non-technical such as a systematic approach to a machine learning problem. If you have any questions or suggestions, please don't hesitate to comment on this thread or the welcoming page at /r/LearnMachineLearning

Artificial intelligence continues to have its moment in the spotlight, with a surge of interest in startups and efforts from huge tech companies to push the boundaries of how we might best use machine learning, computer vision and other areas of AI in the future. The latest development on that front comes from China's Huawei, which today announced that it would form a research partnership with UC Berkeley focused on AI, and fund it to the initial tune of 1 million. The alliance, between Huawei's Noah's Ark Laboratory and Berkeley Artificial Intelligence Research (BAIR), is being billed as a "strategic partnership into basic research", and it will cover areas like deep learning, reinforcement learning, machine learning, natural language processing and computer vision. "The two parties believe that this strategic partnership will fuel the advancement of AI technology and create completely new experiences for people, thus contributing greatly to society at large," Huawei notes. Some of these areas of AI you will have heard a lot about already.

How do most referral processes begin again? "Hey, Fred, may I ask you a question? Did you find value in our conversation today? If so, I strive to bring value to others in the same way, so would you be willing to give me the names of 3 people you feel could benefit as well?" That script is in every referral textbook.

Science fiction is slowly becoming science fact, claims science and technology committee. The UK is not well prepared for how artificial intelligence (AI) and robots will fundamentally reshape the way people live and work, lawmakers have warned in a report. The Science and Technology Committee urged the government to establish a commission for examining the social, ethical and legal implications of recent and potential developments in AI and robots. The committee's interim chairwoman Tania Mathias said: "At present, 'AI machines' have narrow and specific roles, such as in voice-recognition or playing the board game'Go'. "But science fiction is slowly becoming science fact, and robotics and AI look destined to play an increasing role in our lives over the coming decades." The report cited driverless cars, supercomputers that help with medical diagnoses, and intelligent tutoring systems as examples of areas where AI is transforming day to day life, raising questions on the transparency of AI decision-making and privacy. It also noted that the government's leadership in AI has been lacking even though UK-startups and universities have made huge contributions to the field from a technological point of view. Mathias said: "Some major technology companies – including Google and Amazon – have recently come together to form the'Partnership on AI'.

"Artificial intelligence has some way to go before we see systems and robots as portrayed in the creative arts such as Star Wars. At present, 'AI machines' have narrow and specific roles, such as in voice-recognition or playing the board game'Go'. But science fiction is slowly becoming science fact, and robotics and AI look destined to play an increasing role in our lives over the coming decades. It is too soon to set down sector-wide regulations for this nascent field but it is vital that careful scrutiny of the ethical, legal and societal ramifications of artificially intelligent systems begins now." AI systems are starting to have transformational impacts on everyday life: from driverless cars and supercomputers that can assist doctors with medical diagnoses, to intelligent tutoring systems that can tailor lessons to meet a student's individual cognitive needs.

Did you noticed anything interesting the last time you uploaded a picture to Facebook? Perhaps you picked up on the fact that, sometimes, Facebook tries to tag your friends and family for you. Welcome to DeepFace, Facebook's facial recognition system. If you're wondering why it's called DeepFace, it's because at its core, the system is based on a type of Artificial Intelligence (AI) called Deep Learning. AI is here, and it's changing the way that we interact with technology on a daily basis.

Schools are not preparing children to succeed in a world where intelligent robots have transformed the workforce, MPs have warned. A report by the cross-party Science and Technology Committee suggests that the education system should be adapted to "focus on things that machines will be less good at for longer," rather than skills that are rapidly becoming obsolete. The committee also warned that while "robots as portrayed in films like Star Wars" remain some way off, the government's role in preparing for major social change is lacking. Dr Tania Mathias, acting chairwoman of the committee and Conservative MP for Twickenham, said: "Science fiction is slowly becoming science fact, and robotics and AI look destined to play an increasing role in our lives over the coming decades." Mathias told ttold the Guardian that the school curriculum, particularly in secondary schools, did not reflect the "fourth industrial revolution" in robotics and AI that is underway.

A BOT is the most basic example of a weak AI that can do automated tasks on your behalf. Chat bots were one of the first automated programs to be called as'bots'. Web crawlers used by Search Engines like Google are a perfect example of sophisticated and advanced BOT.

When a lot of us think about Data Science and Machine Learning, we might shy away and think - 'that's only for the clever boffins' ... 'I didn't do advanced maths in school, I'll avoid that...', or'I only do basic coding and SQL, it's no use to me...'. Like a lot of areas, the thing is that, if we don't at least take a look at it, we are doing ourselves a disservice. Sure, I will be the first to admit that Data Science and Machine Learning can be hard, but guess what, that's only a small part of the domain. The majority of it is quite accessible to most developers and is not that difficult to either learn, or use. In addition, the Data Science and Machine Learning tools given to use by Microsoft in Azure, make it super easy to get started.

The article examines in some detail the convergence rate and mean-square-error performance of momentum stochastic gradient methods in the constant step-size and slow adaptation regime. The results establish that momentum methods are equivalent to the standard stochastic gradient method with a re-scaled (larger) step-size value. The size of the re-scaling is determined by the value of the momentum parameter. The equivalence result is established for all time instants and not only in steady-state. The analysis is carried out for general strongly convex and smooth risk functions, and is not limited to quadratic risks. One notable conclusion is that the well-known bene ts of momentum constructions for deterministic optimization problems do not necessarily carry over to the adaptive online setting when small constant step-sizes are used to enable continuous adaptation and learn- ing in the presence of persistent gradient noise. From simulations, the equivalence between momentum and standard stochastic gradient methods is also observed for non-differentiable and non-convex problems.