The following table is not complete. Please feel free to report if you find something incorrect or missing. Please read the contribution guidelines first. Also, please feel free to report any error.

Finding professors in machine learning and climate change is difficult, because they are spread across various departments and research a wide breadth of optics. Whether you're applying for graduate school, look for collaborators, or inspiring projects - this list is intended to get you started by finding the right people. This is a safe, open, and inclusive community. The list is most surely incomplete, so please add your favorite professors through commenting in an issue or creating a pull request. Students in CCML include Vincent Meijer.

This projects contains demo video, steps and source codes / tutorial for easiness or reference purpose. This curated list is suitable for beginners and intermediate ML Practitioners. Step 4. Find area using FindContours Firstly, the algorithm have to find where the grids are! Once grids are extracted, for each grid you've to: Cyril Diagne (the creator of this project) has used BASNet for salient object detection and background removal. The accuracy and range of this model are stunning and there are many nice use cases so I packaged it as a micro-service / docker image: Basnet.

Artificial Intelligence (AI) refers to the simulation of human intelligence in machines that are programmed to think like humans and mimic their actions. The term may also be applied to any machine that exhibits traits associated with a human mind such as learning and problem-solving. Artificial Intelligence is advancing by leaps and bounds. Recent research in the fields of Data Science, Machine Learning, Natural Language Processing and other sub fields of AI has already started to impact the lives of common people. AI is no more a superficial concept.

There is so much noise in artificial intelligence and tough to find which are the top companies, resources, tutorials in ai and machine learning. As I started learning about AI myself, I watched ton's of youtube videos to figure out which ones were good and which ones are just crap. In order to solve that issue, I created and categorized artificial intelligence tools, machine learning tools, and tutorials. The goal is to save you time and find the best of the best so you can focus on what more important things. Please feel free to share this video or website link with anyone whom you think will benefit from it.

In this article, we bring you a list of the Top 10 Deep Learning Github Repositories on a trend that has been sorted by the number of stars. TensorFlow, that, within Google's Machine Intelligence Research organization was originally developed by researchers and engineers working on the Google Brain team is an open source software library used for numerical computation making the use of data flow graphs for the purpose of scalable machine learning. Keras, written in Python is a high-level neural networks API that is capable of running on top of TensorFlow, CNTK, or Theano. It was developed with a focus on enabling fast experimentation that through user friendliness, modularity, and extensibility allows for easy as well as fast prototyping. In short, can be said to be Deep Learning For Humans.

The effort of building a chatbot and running is already abstracted in a way by bot development platforms like api.ai, wit.ai and so on. They help you to build a bot that makes your entire app or website boiled down too few lines of conversations. Many bots are being developed, but very few bots are successful. The bots being developed are not fit for real world applications and are just good enough for demo purposes. This is because the creators are not aware of how the end user will be using it or what are the numerous issues which make the bot unusable.

This article comes from GitHub. A curated list of resources dedicated to bayesian deep learning. To read the original article, click here.

When I was writing books on networking and programming topics in the early 2000s, the web was a good, but an incomplete resource. Blogging had started to take off, but YouTube wasn't around yet, nor was Quora, Twitter, or podcasts. Over ten years later as I've been diving into AI and machine learning, it is a completely different ballgame. There are so many resources -- it's difficult to know where to start (and stop)! To save you some of the effort I went through in researching all the different nooks and crannies of the web to find the best content; I've organized them into a big collection here.

Open source software is an important piece of the data science puzzle. According to the most recent KDnuggets data science software poll results, 73% of data scientists used free software in the previous 12 months. While there are many sources of such tools on the internet, Github has become a de facto clearinghouse for all types of open source software, including tools used in the data science community. The importance, and central position, of machine learning to the field of data science does not need to be pointed out. The following is an overview of the top 10 machine learning projects on Github.* The top project is, unsurprisingly, the go-to machine learning library for Pythonistas the world over, from industry to academia.