There are many low-quality free courses and YouTube courses that provide no help in building strong machine learning fundamentals. You will end up even more confused and quit pursuing the career. I am a big advocate of paid courses, but you can also learn a lot from interactive free courses by Udacty, Coursera, and FastAI. These courses cover fundamentals and introduce you to supervised, unsupervised, and deep learning algorithms. You will be introduced to machine learning applications, examples, and building your first linear and logistic regression model on Jupyter Notebook.

Today's post is slightly off-track. I was invited to the first Fast.AI unconference in Brisbane, Queensland this week. It was an honor to be part of the community and I'm having a blast meeting with so many brilliant AI researchers around the globe! In short, UnConferences are "unconventional conferences". Anyone can propose an agenda, organize a session to any topics they want.

Jeremy Howard, a creator of fast.ai and an ex-President of Kaggle says that most of the research in the deep learning world is a total waste of time. He explains why it is so and what is currently being under studied i.e. active learning and transfer learning. Active learning and transfer learning are further elaborated in this blog post. When asked a question "what's wrong with Artificial Intelligence?", However, when you literally dig into the question, the industry of AI is fighting its own demons.

Full Stack Deep Learning covers the full lifecycle of an AI application, from ideation through deployment but it does not cover theory or model fitting.

Half of this crazy year is behind us and summer is here. Over the years, we machine learning engineers at Ximilar have gathered a lot of interesting ML/AI material from which we draw. I have chosen the best ones from podcasts to online courses that I recommend to listen to, read, and check. Some of them are introductory, others more advanced. However, all of them are high-quality ones made by the best people in the field and they are worth checking.

You may have also used or heard of their equally high quality deep learning, machine learning, linear algebra, and natural language processing courses. It has also been a major protagonist in the development of transfer learning for natural language processing; performed an investigation and evaluation of the research into the use of face masks for suppressing the spread of COVID-19; been a voice at the forefront of applied data ethics. This library provides easier API access to a variety of machine learning-related functionality, especially when it comes to neural networks. Much of this aspect of the library sits atop PyTorch, making the creation of neural networks with this lower level library easier and flexible for machine learning coders of all skill levels. As a bridge between their courseware and the fastai library which it uses, Jeremey Howard and Sylvain Gugger are working on a book titled Deep Learning for Coders with fastai and PyTorch: AI Applications Without a PhD, which is not yet available.

Although the book is currently in pre-order status, it is highly anticipated by readers and has long been ranked the first on Amazon's Computer Science book list. The draft of the book has published 20 chapters (including introduction and conclusion). The content starts with the most well known AI "Hello Word problem", the MNIST image classification, then NLP, recurrent neural network, convolutional neural network, and interpretability. This course is not for beginner and the prerequisites are knowledge of Python and PyTorch. They can all be installed directly via PyPI.

Does AI have the power to control the spread of infection of COVID-19, discover cures and vaccines, and aid in the treatment of the critically ill? Or should AI practitioners step back and let the epidemiologists, clinicians, and microbiologists manage the response? Can we trust AI to guide decision making? Do we have access to the data we need and how can we share it whilst balancing patient privacy? We have assembled a world class panel of AI and medical experts to tackle our biggest crisis.

Python is widely considered the best and most effective language for data science. Most of the polls and surveys that I've come across in recent years peg Python as the market leader in this space. But here's the thing – data science is a vast and ever-evolving field. The languages we use to build our data science models have to evolve with it. Remember when R was the go-to language?

This is a clip from a conversation with Jeremy Howard from Aug 2019. You can watch the full conversation here: https://www.youtube.com/watch?v J6XcP... (more links below) Podcast full episodes playlist: https://www.youtube.com/playlist?list... Podcasts clips playlist: https://www.youtube.com/playlist?list... Podcast website: https://lexfridman.com/ai Note: I select clips with insights from these much longer conversation with the hope of helping make these ideas more accessible and discoverable. Ultimately, this podcast is a small side hobby for me with the goal of sharing and discussing ideas. I did a poll and 92% of people either liked or loved the posting of daily clips, 2% were indifferent, and 6% hated it, some suggesting that I post them on a separate YouTube channel.