AI News, 30 Free Resources for Machine Learning, Deep Learning, NLP & AI
30 Free Resources for Machine Learning, Deep Learning, NLP & AI
This is a collection of free resources beyond the regularly shared books, MOOCs, and courses, mostly from over the past year.
They start from zero and progress accordingly, and are suitable for individuals looking to pick up some of the basic ideas, before hopefully branching out further (see the final 2 resources listed below for more on that).
For many good reasons, much of the highest quality machine learning educational resources tend to have a very strong focus on theory, especially at the beginning.
Covering machine learning right from basics, as well as coding algorithms from scratch and using particular deep learning frameworks, these resources cover quite a bit of ground.
But what if you've completed these, have already gained a foundation in NLP and want to move to some practical resources, or simply have an interest in other approaches, which may not necessarily be dependent on neural networks?
With more and more institutes of higher learning today making the decision to allow course materials to be openly accessible to non-students via the magic of the web, all of a sudden a pseudo-university course experience can be had by almost anyone, anywhere.
Have a look at the following free course materials, all of which are appropriate for an introductory level of AI understanding, some of which also cover niche application concepts and material.
What are the best resources to learn about deep learning?
One of the reasons for this is that deep learning is a field with a huge amount of design choices -- what deep learning architecture to use, what training algorithm to use, etc.
was first active in neural nets back in the early 1990s and remember that the field was still struggling with many basic concepts such as good generalisation and overfitting.
Looking back I think the field was perhaps mesmerised by these incredibly powerful neural network tools that could in principle do anything -- everthing was a neural network, and everything could be solved by a neural network.
There were certainly some enlighted researchers from outside the neural net community looking in, but I think we got a poor reputation for not being outward looking enough.
In the 2000s we went through a tremendous maturation period in which the connections to a range of different communities (information theory, optimisation, statistics) became much better understood and I think the ML field benefitted enormously from that.
We're in a much better position now to understand what the benefits of deep learning might be compared to alternative approaches since we know much better how non deep learning approaches behave on AI tasks.
Even today there are deep learning tutorials online that are deeply misleading, largely because the authors don't understand the basics of machine learning and statistics.
Yoshua Bengio's book Deep Learning looks very nice since it contains, in addition to a nice intro to deep learning, some material on the basics of machine learning as well.
12 of the best free Natural Language Processing and Machine Learning educational resources
Advances in of Natural Language Processing and Machine Learning are broadening the scope of what technology can do in people’s everyday lives, and because of this, there is an unprecedented number of people developing a curiosity in the fields.
We’ve split these resources into two categories: The resources on this post are 12 of the best, not the 12 best, and as such should be taken as suggestions on where to start learning without spending a cent, nothing more!
Top Resources to Learn Electrical Engineering Online (for Free): The 50 Best Courses, eBooks, Programs, Tutorials and More
Whether you are a high school student looking forward to graduating and moving on to a career in electrical engineering or someone looking to go back and study a different field than the one in which you currently work, it can be difficult to find programs and courses suited to your needs that also are affordable.
Well, thanks to the internet, partnerships between higher education institutions and open courseware providers, and new licensing guidelines, it is possible to learn electrical engineering online.
And, in this case, we decided to sweeten the deal and scour the internet for the most reputable, reliable sources of information on electrical engineering that just so happen to be free.
Several of the resources we’ve chosen received rave reviews from previous and current students who highly recommend them to people just beginning to study electrical engineering.
The best part about Arduino is that they provide materials for purchase as well as free online video tutorials so that beginners in electrical engineering can gain hands-on experience and practice.
With more than 70 electrical engineering courses to choose from, the National Programme on Technology Enhanced Learning (NPTEL) is one of the most useful online resources for anyone interested in studying electrical engineering.
Available through edX, a nonprofit dedicated to giving everyone access to education through a Massive Open Online Courses (MOOC) platform, Circuits and Electronics has everything to offer someone just beginning to learn electrical engineering.
With course offerings from some of the top universities, including Rice University, Coursera is a great resource to browse and select just the right learning opportunity for your electrical engineering studies.
Electrical Knowhow offers courses, a download library, a quiz and answer section, and more to help people on their quest to learn electrical engineering online.
An e-learning site, UStudy.in aims to provide educational materials for polytechnic college students in India, but they strive to provide content to the learning community at large.
The Fourier Transform and Its Applications strives to provide students of electrical engineering with “a facility with using the Fourier transform, both specific techniques and general principles, and learning to recognize when, why, and how it is used.”
With over 600 free, interactive certification and diploma courses, ALISON is a fantastic resource for anyone looking to educate themselves, but it is a great choice for people looking to learn electrical engineering.
What makes SkilledUp so attractive to students is that it compiles dozens of courses from reputable universities and services so that prospective students can choose the course that’s right for them.
The course includes 12 weeks of study, with an estimated 8-10 hours of work/week, the course syllabus, suggested readings, lecture videos, quizzes, and more.
An online electrical engineering study site, Electrical4u provides information and study notes, questions, engineering animation, video presentations, and more.
The site is created and run by a team of experienced electrical engineers in various fields of electrical technology, so individuals looking to learn electrical engineering can trust the expertise available from Electrical4u.
For those looking to study electrical engineering, FreeVideoLectures is a great resource for lectures and tutorials covering a broad range of topics related to the electrical engineering field.
Their engineering courses are specifically geared to people who are serious about learning electrical engineering online, and they are provided by some of the world’s top universities and professors and available for download to your computer or mp3 player.
As for electrical engineering, Academic Earth offers more than 15 courses, plus has links to electrical engineering journals and trade magazines, plus links to grants and scholarships, internships, and student and professional organizations related to the field.
Courses begin at dates throughout the year, and participants can browse the extensive list of offerings to choose those that best suit their educational and scheduling needs.
This database of videos contains dozens of lectures related to electrical engineering, so people wishing to learn more about the industry should browse the topics and choose those that are of most interest to them.
Featuring measurements, lesson notes, study guides, and other resources, Exploring Electrical Engineering would be a great place to start for people interested in starting electrical engineering studies.
Electrical engineering students will appreciate the ability to search videos by latest courses or most viewed courses, plus the wide variety of topics covered by the more than 30 offerings provided by Dnatube.
course offering from Tokyo Tech OpenCourseWare, Guided Wave Circuit Theory focuses on guided wave theory and “its application to the design of guided wave circuit in microwave, millimeter-wave and optical regime.”
The courses from Energy University are geared toward energy experts, students, or anyone who wants to advance their career in the electrical industry, so they are perfect for people who want to learn about electrical engineering online.
Its electrical engineering offerings are from some of the leading experts at MIT, Stanford, and other top universities, so anyone interested in learning more about the topic can rest assured that the video lectures from Minerva+ are informative and educational.
Using the search term electrical engineering, site visitors will find tutorials, articles, lectures, presentations, and more to help them learn about electrical engineering online.
However, their online resources are available to anyone, and they especially useful to people looking to learn electrical engineering online because they include a list of free online courses in computer science and electrical engineering.
resource for courses in a number of fields, MyFreeCoursesOnline features many useful links to people looking to pursue a new career or further their education.
Other features of the site include links to other classes related to the electrical and engineering fields, projects and kits, and more that will be of interest to people seeking to learn about electrical engineering.
This series covers a multitude of topics related to equations, circuits, and other information that those aiming to learn about electrical engineering will find useful and informative.
In terms of electrical engineering, dozens of lectures are available from Course Hero from experts in the field from reputable higher learning institutions, including Brad Osgood of Stanford, David Forney of MIT, David Culler of UC Berkeley, and others.
How to Learn Advanced Mathematics Without Heading to University - Part 1
I am often asked in emails how to go about learning the necessary mathematics for getting a job in quantitative finance or data science if it isn't possible to head to university.
While it is far from easy to sustain the necessary effort to achieve such a task outside of a formal setting, it is possible with the resources (both paid and free) that are now available.
Finally, I will describe a mathematical syllabus that takes you all the way through a modern four-year Masters-level UK-style undergraduate course in mathematics, as applicable mainly to quantitative finance, data science or scientific software development.
It is an extremely serious undertaking and requires substantial long-term commitment over a number of years, so it is absolutely imperative that there is a strong underlying motivation, otherwise it is unlikely that you will stick with self-study over the long term.
For the majority of you on this site, it is because you wish to gain employment and/or further formal study in the field of quantitative finance, data science or scientific software development.
You might have worked in a technical industry for 10-15 years, but seek a new role and wish to understand the necessary prerequisite material for the career change.
However, if your sole reason for wanting to learn these topics is to get a job in the sector, particularly in an investment bank or quantitative hedge fund, I would strongly advise you to carry out mathematics in a formal setting (i.e.
If you are heavily interested in learning more about deeper areas of mathematics, but lack the ability to carry it out in a formal setting, this article series will help you gain the necessary mathematical maturity, if you are willing to put in the effort.
want to emphasise that studying mathematics from the level of a junior highschooler to postgraduate level (if desired) will require a huge commitment in time, likely on the order of 10-15 years.
However, chances are if you are considering studying advanced mathematics you will already have formal qualifications in the basics, particularly the mathematics learnt in junior and senior highschool (GCSE and A-Level for those of us in the UK!).
I estimate that it will take approximately 3-4 years of full-time study or 6-8 years of part-time study, in order to have an equivalent knowledge base gained by an individual who has carried out formal study in a UK undergraduate mathematics program to masters level.
If you are happy with this overall level of commitment, then the broad path that you will follow should look something like this: As you can see, a mathematics education to a high level can take anywhere from 3 years to approximately 15 years (or more!) depending on your chosen path.
They provide the added benefits of being able to pause videos, rewind them, interaction with lecturers on online portals as well as easy access to supplementary materials.
At this stage of your mathematical career you will be familiar with the basics of differential and integral calculus, trigonometric identities, perhaps some elementary linear algebra and possibly some elementary group theory, gained from highschool or through self-study.
This means that ones thinking is shifted from mechanical solution of problems, utilising a 'toolbox' of techniques, towards deep thought about disparate areas of mathematics that can be linked in order to prove results.
For those of you who are unable or unwilling to carry out formal study in a university setting and wish to tackle a full syllabus of undergraduate mathematics, I have created a comprehensive study plan below to take you from high school level mathematics to the equivalent of a four-year Masters in Mathematics undergraduate course.
Since a degree course is often tailored to the desires of the individual in the latter two years, I have created a syllabus which broadly reflects the topics that a prospective quant should know.
The first year in an undergraduate mathematics education is primarily about shifting your mindset from the 'mechanical' approach taught at highschool/A-Level into the 'formal systems' approach that is studied at university.
The courses found in a first year largely reflect this transition, whereby the following core topics are emphasised: Here is the course list for Year 1: Most top-tier UK undergraduate courses have a 'Foundations' module of some description.
The goal of the course is to provide you with a detailed overview of the nature of university mathematics, including the notions of proof (such as proof by induction and proof by contradiction), the concept of a map or function, as well as the differing types such as the injection, surjection and bijection.
In this way, real analysis courses teach not only the 'mindset' of forming proofs, but also introduce more abstract concepts such as 'proper' definitions of infinity, axioms (such as the axiom of completeness) and some good experience manipulating continuous functions and their derivatives.
The majority of statistical machine learning methods are based on the principles of linear algebra and calculus, as are many quantitative finance theories, such as the covariance matrix and the capital asset pricing model.
Since the more complicated partial differential equations (PDE) and stochastic differential, equations (SDE) are widely found in quantitative analysis and trading, understanding the solution of the more simpler ODEs helps with understanding solutions of these problems.
In highschool (or at GCSE!) students are often taught about triangular geometry, and an introductory university module in Geometry will formalise these concepts, ultimately with the idea of gaining practice understanding and writing geometric proofs.
In addition, and perhaps more relevant to the quant, having a good understanding of trigonometry is essential for later courses such as Fourier Analysis, which plays a substantial role in signals analysis and time series analysis.
While it isn't clear how a direct study of groups and symmetry might be applied on a day-to-day basis in the world of a quant, the study of groups does form the basis of many more advanced mathematical topics, particularly advanced Linear Algebra.
For the autodidact who is short on time, I would state that it is worth studying them at an introductory level in order to 'be aware of their existence', as many advanced quantitative techniques will indirectly refer to them.
Note however that one of the most successful quant hedge funds in history, Renaissance Technologies, was founded by Jim Simons, a notable mathematician who carried out a substantial amount of work on manifolds (which requires a solid understanding of group theory).
Undergraduate introductory probability courses usually begin by discussing the laws of probability, including Bayes' Theorem, probability distributions, discrete random variables, expectation, covariance and continuous random variables.
One of the key benefits for a quant of carrying out a PhD in a scientific computing discipline is that it teaches you how to take complex algorithms, found in papers that often leave out the essential details, and write them into fully working pieces of software in a reasonable time frame.
In the next article, covering Year 2, we will look at more advanced topics in the subject areas outlined above, including the Riemann Integral in Real Analysis, more complicated topics in Group Theory, an introduction to Metric Spaces (a precursor to Topology), Vector Calculus and Statistics (an absolutely essential subject for the practising quant trader or risk manager).
Center for Teaching
Print Version Intrinsic motivators include fascination with the subject, a sense of its relevance to life and the world, a sense of accomplishment in mastering it, and a sense of calling to it.
Advantages: Intrinsic motivation can be long-lasting and self-sustaining. Efforts to build this kind of motivation are also typically efforts at promoting student learning. Such efforts often focus on the subject rather than rewards or punishments.
Disadvantages: On the other hand, efforts at fostering intrinsic motivation can be slow to affect behavior and can require special and lengthy preparation. Students are individuals, so a variety of approaches may be needed to motivate different students.
Extrinsic motivators include parental expectations, expectations of other trusted role models, earning potential of a course of study, and grades (which keep scholarships coming).
Deci argued that the group that had been paid to solve puzzles might have found the puzzles intrinsically interesting as well, but the extrinsic, monetary reward had reduced their intrinsic interest.
Sources: When encouraging students to find your subject matter interesting, use cues to show students the appeal of the subject matter.
- On Monday, January 20, 2020
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