About AI Summer
In the history of Artificial Intelligence (AI), an AI winter was a period of reduced research funding. Going back to 1974, the field of AI has previously experienced several seasonal hypes, followed by disappointment and criticism, and as a result funding cuts. The reason is simple: too many expectations for that time!
We believe that AI has moved forward to a new era. Even though we are still ambitious, we are completely aware of the expectations of AI. The advent of deep neural networks brought hope in the field and revolutionized a plethora of domains (image processing, natural language, etc.)
To this end, we offer our knowledge to all the spectrum of the AI world. To achieve that, we combined our research and software engineering skills to cover all the aspects of AI. Our aim is to provide you 100% authentic deep learning content.
Our mission
We are trying to collect and organize all Deep Learning related information from mathematics and models to programming and software architecture into a single place. To present the latest trends in the field and to help you understand how and why Artificial Intelligence is going to play a vital role in humanity’s future.
And it’s not about just learning stuff. It’s also about advancing your career in the field and finding real solutions to your business problems.
There is one other thing. It’s completely free. No ads, no paywall, no memberships, nothing. We do not charge for our blog posts, because we believe in accessible and open knowledge sharing.
We do offer some paid courses and books, but the majority of the content is already available for free. They simply exist as a way to cover hosting and other basic expenses.
Interested in contributing to AI Summer ?
Contributions are more than welcome as long as they meet the quality standard of our past articles. In our mind, the ideal use of our website would be for people to collaborate and create high-quality articles. In fact, we created our discord server for this very reason. So if you want to publish your content, feel free to join our community and we can discuss more. Otherwise, you can contact us via email or on social.
Each article will be thoroughly reviewed by us and other members of the community for scientific accuracy, clarity, and writing quality. This process can take weeks or even months. During this period, we will work together to ensure the highest possible quality.
We generally look for two types of articles:
1) Comprehensive overviews of SOTA algorithms, methodologies, or architectures. Some examples include Transformers in computer vision, Deep learning on computational biology and bioinformatics tutorial and How Graph Neural Networks (GNN) work: introduction to graph convolutions from scratch
2) Thorough and complete tutorials on libraries or frameworks. Examples include A complete Hugging Face tutorial: how to build and train a vision transformer, Build a Transformer in JAX from scratch: how to write and train your own models
Our team
Sergios Karagiannakos
Sergios is a Machine Learning Engineer that is passionate about helping companies build their Artificial Intelligence products and educating people about Deep Learning.
After graduating with a degree in EIectrical and Computer Engineering, he worked as a Software Engineer at Hubspot as part of the Machine Learning Infrastructure team and as a Data Scientist at Eworx S.A. He has also collaborated with different companies in developing and deploying Machine Learning solutions.
Nikolas Adaloglou
Nikolas is an Electrical and Biomedical Engineer that is interested in the development of human-centered AI solutions. He is currently working as a Machine Learning research engineer applied to radiotherapy for people with cancer. Previous research projects that he consciously participated include Greek Sign Language solution systems and educational games for people with disabilities.
His AI research interests include 3D medical imaging, image and video analysis, graph-based deep learning models and generative deep learning.
FAQ
AI Summer is created for sharing original AI content. We do not charge for our blog posts, because we believe in accessible and open knowledge sharing. However, the fact our content is free doesn't mean that it is inferior to paid courses. We want to open source a lot of projects, make AI simple and intuitive. We rely on the AI community to support us through donations or buy books that are heavily based on our free content to express your support.
We have reviewed, revised and provided feedback in a couple of paid mini-courses. In our experience the amount of money you pay is not always associated with the quality of information and code you get. Conclusion: be careful when you invest your money in AI education courses and appreciate the content that is provided to you for free.
The amount of money you pay is not always associated with the quality of information and code you get.
Our huge feedback from the AI community demonstrates that our free tutorials are of high quality. We have a high impact on social media and forums such as reddit because readers appreciate our work and want to engage and share it. It is likely that many of our posts may be of higher quality than a lot of paid material you’ll find online. Believe us, we ‘ve been there!
The main difference is that we invest even more effort into our paid content.
- We aim to maximize flow between concepts, so you don’t google stuff all the time
- We minimize the external links so that you can stay focused on your learning journey.
- We may revisit concepts and decide to delve into them more, i.e. more examples, further analyses of intuitions.
- Easy to use Colab notebooks or git repositories with AI industry standards code with plenty of comments relating theory and implementation. Documentation is the key to learning AI.
- Lifetime access with free updates for any new version that we publish. Mistakes are always there. By maintaining all our work online we can constantly improve it.
We truly appreciate your support as it helps us continue writing articles and tutorials as well as creating new courses and books. If you find our content useful, please share the word out. You can:
- Tweet your favorite article
- Share a tutorial on Linkedin
- Share a link with your friends or coworkers who are interested in Deep Learning.
If that’s not enough for you and you want to make a monetary contribution, you can do so in our support-us page .
Thank you so much in advance!
AI Summer is primarily run by Sergios Karagiannakos and Nikolas Adaloglou.
Sergios is a Machine Learning Engineer with plenty of experience in building scalable ML systems and an inherent passion for learning new things. He has worked with many different companies (e.g HubSpot, Eworx) and individuals helping them build Machine Learning products either as a Data Scientist or as a Software Engineer.
Nikolas is working as an AI researcher and his main interests include 3D medical imaging, image and video analysis, graph-based deep learning models and generative deep learning. Some of his previous projects include applications of radiotherapy for people with cancer, Greek Sign Language solution systems and educational games for people with disabilities.
One thing that we both can tell you for sure is that we believe in sharing our Machine Learning experiences with writing, we want to help others understand and exploit the great potential of Artificial Intelligence. Above all, we aim in the development and promotion of human-centered AI solutions.
AI Summer started as a medium to document and advance our knowledge in the field of Deep Learning. As we invested more and more time in writing articles, our goal shifted towards helping other people as well discover the potential of AI.
We aim to provide 100% authentic deep learning content, covering all aspects of the field from research to software engineering. We are trying to collect and organize all Deep Learning related information from mathematics and models to programming and software architecture into a single blog. To present the latest trends in the field and to help people understand how and why Artificial Intelligence is going to play a vital role in humanity’s future.
We hope you enjoy your time spent on AI Summer!
In the history of Artificial Intelligence (AI), an AI winter was a period of reduced research funding. Going back to 1974, the field of AI has previously experienced several seasonal hypes, followed by disappointment and criticism, and as a result of funding cuts. The reason is simple: too many expectations for that time!
We believe that AI has moved forward to a new era. Even though we are still ambitious, we are completely aware of the expectations of AI. The advent of deep neural networks brought hope in the field and revolutionized a plethora of domains. AI solutions are everywhere, from visual recognition to natural language processing
The answer is absolutely yes! We would love to create a community of PhD students, professional AI researchers, and engineers that share their content. However, we would not sacrifice the authenticity of our blog. For this reason we created our Discord server, so we get to know you first. Join and we can chat about possible contributions or projects.
If you have a business opportunity for us, please use our contact form or send us an email at sergios@theaisummer.com and/or nikolas@theaisummer.com
You can also ping us on X or Linkedin, but probably the best place to reach out is on our Discord server
You are free and even encouraged to use our content in your project, whether it’s for educational purposes, for fun, or for profit.
However, we do ask that you cite AI Summer by providing a link back to our blog along with the title of the article and the name of the author.
You can also use the following format to cite us:
@article{aisummer, title = “title of the article”, author = “author”, journal = "https://theaisummer.com/", year = “publishing year”, url = "article url" }
Absolutely. We do offer consulting services and contracting work through AI Summer.
We also collaborate with different other experienced engineers and ML researchers depending on the use case, so we can help you in a variety of ways.
We kindly ask you to send us an email outlining your project and your timeline and then we can hop on a call discussing the details, if we really believe that we can help you!
We will not undertake a project we cannot provide additional value.
We are always open to new content ideas, so feel free to contact us with any suggestions and we’ll get back to you as soon as possible!
We really want to help you and that is one of the main reasons we started AI Summer.
However, we think it’s best that you ask first your university advisor or teacher because he probably knows best. Feel free though to send us an email asking questions and we may address them in a future article.
Finally, we do not offer 1-1 coaching apart from exceptional cases!
It would be awesome if you do! For starters an email to nikolas@theaisummer.com would work just fine. However, we plan to make something more easy and immediate in the next version of our website!