Lectures are in Playlists and are alphabetically sorted with thumbnail pictures. No fee, no registration required - learn at your own pace. Certificates can be arranged with presenting universities.
A list of Udemy’s 250 most popular free online courses. Combined, they have close to 40M enrollments.
Udemy is one of the largest online learning platforms for professional learning. According to the company, they have 35 million registered users. An analysis by Class Central shows they have launched over 157,000 courses since 2010.
The pandemic has boosted the fortune of many online providers. Udemy is no exception. In 2020 alone, the company raised $123 million and increased its valuation by over $1 billion. Overall, the company has raised ~$300 million at a valuation over $3 billion.
For this year's course edition, we created a series of Jupyter notebooks that are designed to help you understanding the "theory" from the lectures by seeing corresponding implementations. We will visit various topics such as optimization techniques, transformers, graph neural networks, and more (for a full list, see below). The notebooks are there to help you understand the material and teach you details of the PyTorch framework, including PyTorch Lightning. Further, we provide one-to-one translations of the notebooks to JAX+Flax as alternative framework.
Course edition: Fall 2022 (Nov. 01 - Dec. 24) - Being kept up to date Recordings: YouTube Playlist Author: Phillip Lippe
The notebooks are presented in the first hour of every group tutorial session. During the tutorial sessions, we will present the content and explain the implementation of the notebooks. You can decide yourself whether you just want to look at the filled notebook, want to try it yourself, or code along during the practical session. The notebooks are not directly part of any mandatory assignments on which you would be graded or similarly. However, we encourage you to get familiar with the notebooks and experiment or extend them yourself. Further, the content presented will be relevant for the graded assignment and exam.
The tutorials have been integrated as official tutorials of PyTorch Lightning. Thus, you can also view them in their documentation.
How to run the notebooks
On this website, you will find the notebooks exported into a HTML format so that you can read them from whatever device you prefer. However, we suggest that you also give them a try and run them yourself. There are three main ways of running the notebooks we recommend:
Locally on CPU: All notebooks are stored on the github repository that also builds this website. You can find them here: https://github.com/phlippe/uvadlc_notebooks/tree/master/docs/tutorial_notebooks. The notebooks are designed so that you can execute them on common laptops without the necessity of a GPU. We provide pretrained models that are automatically downloaded when running the notebooks, or can manually be downloaded from this Google Drive. The required disk space for the pretrained models and datasets is less than 1GB. To ensure that you have all the right python packages installed, we provide a conda environment in the same repository (choose the CPU or GPU version depending on your system).
Google Colab: If you prefer to run the notebooks on a different platform than your own computer, or want to experiment with GPU support, we recommend using Google Colab. Each notebook on this documentation website has a badge with a link to open it on Google Colab. Remember to enable GPU support before running the notebook (Runtime -> Change runtime type). Each notebook can be executed independently, and doesn't require you to connect your Google Drive or similar. However, when closing the session, changes might be lost if you don't save it to your local computer or have copied the notebook to your Google Drive beforehand.
Lisa cluster: If you want to train your own (larger) neural networks based on the notebooks, you can make use of the Lisa cluster. However, this is only suggested if you really want to train a new model, and use the other two options to go through the discussion and analysis of the models. Lisa might not allow you with your student account to run Jupyter notebooks directly on the gpu_shared partition. Instead, you can first convert the notebooks to a script using jupyter nbconvert --to script ...ipynb, and then start a job on Lisa for running the script. A few advices when running on Lisa:
Disable the tqdm statements in the notebook. Otherwise your slurm output file might overflow and be several MB large. In PyTorch Lightning, you can do this by setting progress_bar_refresh_rate=0 in the trainer.
Comment out the matplotlib plotting statements, or change :code:plt.show() to plt.savefig(...).
Tutorial-Lecture alignment
We will discuss 7 of the tutorials in the course, spread across lectures to cover something from every area. You can align the tutorials with the lectures based on their topics. The list of tutorials is:
Guide 1: Working with the Lisa cluster
Tutorial 2: Introduction to PyTorch
Tutorial 3: Activation functions
Tutorial 4: Optimization and Initialization
Tutorial 5: Inception, ResNet and DenseNet
Tutorial 6: Transformers and Multi-Head Attention
Tutorial 7: Graph Neural Networks
Tutorial 8: Deep Energy Models
Tutorial 9: Autoencoders
Tutorial 10: Adversarial attacks
Tutorial 11: Normalizing Flows on image modeling
Tutorial 12: Autoregressive Image Modeling
Tutorial 15: Vision Transformers
Tutorial 16: Meta Learning - Learning to Learn
Tutorial 17: Self-Supervised Contrastive Learning with SimCLR
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Via Oliver Durrer swissleap.com, Dennis Swender
Introduction to machine learning for pattern classification, regression analysis, clustering, and dimensionality reduction. For each category, fundamental algorithms, as well as selections of contemporary, current state-of-the-art algorithms, are being discussed. The evaluation of machine learning models using statistical methods is a particular focus of this course. Statistical pattern classification approaches, including maximum likelihood estimation and Bayesian decision theory, are compared and contrasted to algorithmic and nonparametric approaches. While fundamental mathematical concepts underlying machine learning and pattern classification algorithms are being taught, the practical use of machine learning algorithms using open source libraries from the Python programming ecosystem will be of equal focus in this course.
Here are 10 free physics courses you can take online now with top instructors. Their credits range from winning Nobel Prizes to field-defining work. Topics cover introductory concepts, Einstein's theory of relativity, particle physics, dark energy, quantum mechanics, and much more.
You can find numerous physics courses currently available online for free.
Courses are taught by instructors with amazing credits like Nobel Prizes and field-defining work.
Topics range from introductory to Einstein's theory of relativity, particle physics, dark energy, quantum mechanics, and more.
The internet has in many ways fulfilled its educational promise and can be an amazing resource to learn pretty much anything. This is especially true if you have an interest in physics, the study of matter, energy and the fundamental interactions and forces of our universe. There are hundreds of great free courses available, with field-leading and even Nobel Prize-winning instructors.
This is an intermediate algorithms course with an emphasis on teaching techniques for the design and analysis of efficient algorithms, emphasizing methods of application.
Topics include divide-and-conquer, randomization, dynamic programming, greedy algorithms, incremental improvement, complexity, and cryptography.
Courses from 166 universities, including Stanford, Georgia Tech, Yale, Duke, and Michigan, are part of the list.
Are Coursera courses still free? At Class Central, we get that question so often that I wrote a guide to answer it. Generally speaking, Coursera courses are free to audit but if you want to access graded assignments or earn a Course Certificate, you will need to pay. This change was first announced in October 2015, and went live in January 2016.
But there are always a few courses that were still completely free. When you signed up for these courses, you would see an option of “Full Course, No Certificate.” For courses that are part of the new model, you need to select the “audit” option.
It can be tricky to figure out which courses have this option. This is particularly because Coursera is a single page app, and the information is only available once you are logged in. So instead of going through each course one by one and then clicking on enroll, Selenium was used to figure it out. The infrastructure developer Bob Brady set up an AWS instance along with a Cloud 9 IDE for this automation. Below is a screenshot of the setup. The result was, more than 1,150 Coursera MOOCs (1,174, to be exact) are still completely free (including the graded assignments, minus the certificate). Even Coursera’s own help pages claim that for “all courses” you can only access the non-graded materials and lectures for free.
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Subjects include Physics, Chemistry, Computer Science, History, Math, Biology, Economics, Finance, Web Design, Web Technologies,Astronomy. More videos and subjects will be added in future.
Open Culture brings together high-quality cultural & educational media for the worldwide lifelong learning community. Web 2.0 has given us great amounts of intelligent audio and video. It's all free. It's all enriching. But it's also scattered across the web, and not easy to find. Our whole mission is to centralize this content, curate it, and give you access to this high quality content whenever and wherever you want it. Some of our major resource collections include:
A series of public lectures describing frontier discoveries in science and technology that reveal awe-inspiring possibilities and exploring the futures they could lead to.
The Futures public series by Noble and Templeton Laureate Frank Wilczek is now live on YouTube.
Below you'll find lectures from the past few years sorted by year and then by category. Click any one to watch it for free, right now. For older lectures, visit the PIRSA website.
This playlist contains 32.5 hours across 52 video lectures from my Differential Equations course (Math 2080) at Clemson University.
He explains: " After teaching this course 9 times during the academic year, I offered it online and made these videos. Every time I taught it, I would discover a better way to present the material until it finally "converged" to its present form, which I am very happy with. It's remarkable how much one can learn from teaching a course, even if one knows the material very well. For many, if not most undergraduate STEM majors, Differential Equations is the most important math class they will take. Unfortunately, the way that it is usually taught lacks some of the key intution, and presents the material as a "cook book" of how to solve a variety of ODEs. As a result, students usually miss the "big picture", which is quite beautiful. In this course, I make a special effort to explain the simple intuition behind the scenes, because understanding that makes so much of the content much more straightforward and natural. My presentation of the material does really not follow any textbook. Despite this, most textbooks (at least those "with boundary value problems") would serve as a decent reference, as they contain most of this content. That said, in this day and age, so does the internet. I have a course webpage, where I make the content freely available. With each lecture, I post the slides and a worksheet containing what I consider to be the fundamental practice problems for that topic. It should be very conducive to self study."
Learning is a life-long process. Yet if you're someone who always loves learning or wants to pick up a new, specific skill, then cost might be a problem. However, there are lots of free website out there that can teach you a range of skills for free.
Here are 25 websites to learn new skills...
Whatever you want to learn, there's a good chance you can get up to speed online (without paying top-dollar).
Free stuff is always exciting. So is the case with online courses. We know how much you guys enjoyed the 45 free online computer science courses and the 55 PHP tutorials. So here’s another thoroughly worked through list of 40 top notch courses that fall into the Electrical Engineering category. The courses range from Power Electronics, Power Systems to Nano Electronics basics and advance courses. Enjoy the list!
After a review of the axiomatic formulation of quantum theory, the generalized operational structure of the theory will be introduced (including POVM measurements, sequential measurements, and CP maps). There will be an introduction to the orthodox (sometimes called Copenhagen) interpretation of quantum mechanics and the historical problems/issues/debates regarding that interpretation, in particular, the measurement problem and the EPR paradox, and a discussion of contemporary views on these topics. The majority of the course lectures will consist of guest lectures from international experts covering the various approaches to the interpretation of quantum theory (in particular, many-worlds, de Broglie-Bohm, consistent/decoherent histories, and statistical/epistemic interpretations, as time permits) and fundamental properties and tests of quantum theory (such as entanglement and experimental tests of Bell inequalities, contextuality, macroscopic quantum phenomena, and the problem of quantum gravity, as time permits).
Browse Machine Learning courses and specializations. Machine learning courses focus on creating systems to utilize and learn from large sets of data. Topics of study include predictive algorithms, natural language processing, and statistical analysis.
What is Machine Learning? Machine learning is the science of getting computers to act without being explicitly programmed. In the past decade, machine learning has given us self-driving cars, practical speech recognition, effective web search, and a vastly improved understanding of the human genome. Machine learning is so pervasive today that you probably use it dozens of times a day without knowing it. Many researchers also think it is the best way to make progress towards human-level AI. In this class, you will learn about the most effective machine learning techniques, and gain practice implementing them and getting them to work for yourself. More importantly, you'll learn about not only the theoretical underpinnings of learning, but also gain the practical know-how needed to quickly and powerfully apply these techniques to new problems. Finally, you'll learn about some of Silicon Valley's best practices in innovation as it pertains to machine learning and AI.
This list of courses provides a broad introduction to machine learning, datamining, and statistical pattern recognition. Topics include: (i) Supervised learning (parametric/non-parametric algorithms, support vector machines, kernels, neural networks). (ii) Unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning). (iii) Best practices in machine learning (bias/variance theory; innovation process in machine learning and AI). The course will also draw from numerous case studies and applications, so that you'll also learn how to apply learning algorithms to building smart robots (perception, control), text understanding (web search, anti-spam), computer vision, medical informatics, audio, database mining, and other areas.
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