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Playlists
Machine Learning: StatQuest with Josh Starner
Machine Learning is one of those things that is chock full of hype and confusion terminology. In this StatQuest, we cut through all of that to get at the most basic ideas that make a foundation for the whole thing. These ideas are simple and easy to understand. After watching this StatQuest, you'll be ready to learn all kinds of new and exciting things about Machine Learning.
Machine Learning Crash Course with TensorFlow APIs
Google's fast-paced, practical introduction to machine learning, featuring a series of lessons with video lectures, real-world case studies, and hands-on practice exercises.
Machine Learning with Python
The objective of this course is to give you a holistic understanding of machine learning, covering theory, application, and inner workings of supervised, unsupervised, and deep learning algorithms.
In this series, we'll be covering linear regression, K Nearest Neighbors, Support Vector Machines (SVM), flat clustering, hierarchical clustering, and neural networks.
Stanford AA289 - Robotics and Autonomous Systems Seminar
The Stanford Robotics and Autonomous Systems Seminar hosts both guest and internal speakers to a weekly seminar series. The aim is to bring the robotics community together and provide a platform to overview and foster discussion about the progress and challenges in the various disciplines of modern robotics and autonomous design. Past speakers have included experts from industry, faculty from peer institutions, and Stanford faculty.
Podcasts
Data Skeptic
Data Skeptic launched as a podcast in 2014. Hundreds of interviews and tens of millions of downloads later, we're a widely recognized authoritative source on data science, artificial intelligence, machine learning, and similar topics.
Data Skeptic runs in seasons. We explore each season's theme by talking to active researchers and industry professionals contributing in some way to our theme.
We hand pick our guests through an internal process. We don't work with PR agencies and are not able to respond to the unsolicited submissions we get on a daily basis. The best way to get on the show is to publish good research to the arxiv. We crawl it. We'll find you.
Beyond the podcast, Data Skeptic is a boutique consulting firm. Kyle is personally involved in all projects our team takes on. Our work tends to focus in algorithmic design, cloud infrastructure, and end-to-end machine learning.
The Gradient Podcast
Articles, interviews, and news coverage about AI brought to you by a team of AI researchers and builders.
Learning Machines 101
Billing itself as “A Gentle Introduction to Artificial Intelligence and Machine Learning”, this podcast can still get quite technical and complex, covering topics like: “How to Catch Spammers using Spectral Clustering” and “How to Enhance Learning Machines with Swarm Intelligence”.
Lex Fridman Podcast
Conversations about science, technology, history, philosophy and the nature of intelligence, consciousness, love, and power. Lex is an AI researcher at MIT and beyond.
The Road to Autonomy
How would you feel if the transport truck beside you on the highway had no driver? Or the car passing beside you had no driver? Would it make a difference if the widespread deployment of autonomous trucks could ease supply chain problems almost overnight and that autonomous vehicles do not get distracted or speed? And would you feel better if you knew autonomous trucks and vehicles could reduce carbon emissions by 30 percent or more?
Learn more from world’s leading mobility experts on The Road to Autonomy, an ahead-of-the-curve podcast hosted by Grayson Brulte.
Talking Machines
Talking Machines is your window into the world of machine learning. Your hosts, Katherine Gorman and Neil Lawrence, bring you clear conversations with experts in the field, insightful discussions of industry news, and useful answers to your questions. Machine learning is changing the questions we can ask of the world around us. Here, we explore how to ask the best questions and what to do with the answers.
Talking Machines was founded in 2015 by Katherine Gorman and Ryan Adams.
You can catch new episodes every other Thursday during the season on Art19, Apple Podcasts, or wherever you get your podcasts.
This Week in Machine Learning & AI (TWIML)
Intelligent content that gives practitioners, innovators and leaders an inside look at the present and future of ML & AI technologies.
Courses Available through O'Reilly
The Complete Self-Driving Car Course - Applied Deep Learning
This is the first and one of the only courses that make practical use of deep learning and applies it to building a self-driving car. You’ll learn and master deep learning in this fun and exciting course with top instructor Rayan Slim. Having trained thousands of students, Rayan is a highly rated and experienced instructor who follows a learning-by-doing approach. By the end of the course, you will have built a fully functional self-driving car powered entirely by deep learning. This powerful simulation will impress even the most senior developers and ensure you have hands-on skills in neural networks that you can bring to any project or company.
Deep Learning with TensorFlow, Keras, and PyTorch
Deep Learning with TensorFlow, Keras, and PyTorch LiveLessons is an introduction to deep learning that brings the revolutionary machine-learning approach to life with interactive demos from the most popular deep learning library, TensorFlow, and its high-level API, Keras, as well as the hot new library PyTorch. Essential theory is whiteboarded to provide an intuitive understanding of deep learning’s underlying foundations; i.e., artificial neural networks. Paired with tips for overcoming common pitfalls and hands-on code run-throughs provided in Python-based Jupyter notebooks, this foundational knowledge empowers individuals with no previous understanding of neural networks to build powerful state-of-the-art deep learning models.
The Essential Machine Learning Foundations: Math, Probability, Statistics, and Computer Science
This master class includes the following courses:
Linear Algebra for Machine Learning
Calculus for Machine Learning LiveLessons
Probability and Statistics for Machine Learning
Data Structures, Algorithms, and Machine Learning Optimization
Machine Vision, GANs, and Deep Reinforcement Learning
Machine Vision, GANs, Deep Reinforcement Learning LiveLessons is an introduction to three of the most exciting topics in Deep Learning today. Modern machine vision involves automated systems outperforming humans on image recognition, object detection, and image segmentation tasks. Generative Adversarial Networks cast two Deep Learning networks against each other in a “forger-detective” relationship, enabling the fabrication of stunning, photorealistic images with flexible, user-specifiable elements. Deep Reinforcement Learning has produced equally surprising advances, including the bulk of the most widely-publicized “artificial intelligence” breakthroughs. Deep RL involves training an “agent” to become adept in given “environments,” enabling algorithms to meet or surpass human-level performance on a diverse range of complex challenges, including Atari video games, the board game Go, and subtle hand-manipulation tasks. Throughout these lessons, essential theory is brought to life with intuitive explanations and interactive, hands-on Jupyter notebook demos. Examples feature Python and straightforward Keras layers in TensorFlow 2, the most popular Deep Learning library.
Mastering Image Segmentation with PyTorch using Real-World Projects
Image segmentation is a key technology in the field of computer vision, which enables computers to understand the content of an image at a pixel level. It has numerous applications, including autonomous vehicles, medical imaging, and augmented reality.
You will start by exploring tensor handling, automatic gradient calculation with autograd, and the fundamentals of PyTorch model training. As you progress, you will build a strong foundation, covering critical topics such as working with datasets, optimizing hyperparameters, and the art of saving and deploying your models.
With a robust understanding of PyTorch, you will dive into the heart of the course—semantic segmentation. You will explore the architecture of popular models such as UNet and FPN, understand the intricacies of upsampling, grasp the nuances of various loss functions, and become fluent in essential evaluation metrics.
Moreover, you will apply this knowledge in real-world scenarios, learning how to train a semantic segmentation model on a custom dataset. This practical experience ensures that you are not just learning theory but gaining the skills to tackle actual projects with confidence.
By course end, you will wield the power to perform multi-class semantic segmentation on real-world datasets.
