Hello everyone, If you are looking to learn machine learning, there are several courses available online that can help you get started. Here are some of the best machine learning courses that you can take to learn the skills and knowledge required to succeed in this field: Machine Learning by Andrew Ng: This is one of the most popular machine learning courses available online. Taught by Andrew Ng, a renowned AI expert and founder of Google Brain, this course covers all the basics of machine learning, including supervised and unsupervised learning, linear regression, logistic regression, and more. Applied Data Science with Python by University of Michigan: This course is designed to teach you how to apply machine learning techniques to real-world problems using the Python programming language. You'll learn how to use popular machine learning libraries like scikit-learn and pandas to build predictive models and analyze data. Deep Learning by Yoshua Bengio: This course provides an in-depth understanding of deep learning techniques and architectures.

I once had an interview where I was asked to design a model that would recommend ads to users. I simply drew on the board a simple Recommender engine with a User embedding and an Ads embedding, a couple of non-linear interactions and a "click-or-not" learning task. But the interviewer asked "but wait, we have billions of users, how is this model going to fit on a server?!". That was a great question, and I failed the interview! A naive embedding encoding strategy will assign a vector to each of the categories seen during training, and an "unknown" vector for all the categories seen during serving but not at training time. That can be a relatively safe strategy for NLP problems if you have a large enough training set as the set of possible words or tokens can be quite static.

Tomorrow, Prof. Haddadin from the Munich Institute of Robotics and Machine Intelligence (MIRMI) at the Technical University of Munich will explain live to students the opportunities and limitations of robots. Not without bringing GARMI, of course: He can blink, wave, wait tables and even do rehabilitation exercises with patients as a physiotherapist's assistant. German You will find the broadcast afterward in the ZDF Mediathek - https://lnkd.in/eENS7Hvq

It's an in-person and online event where we'll explore practical uses for Artificial Intelligence. What you can expect: - How to use AI in your day-to-day (including home life!) - How AI can help you remove the boring parts of your life - Creative exploration and brainstorming - Live demos - Good conversation!

Machine learning is a rapidly growing field that is revolutionizing many industries, including healthcare, finance, and technology. With its ability to analyze large amounts of data and make predictions and decisions, machine learning is an essential skill for anyone interested in a career in data science or artificial intelligence. If you're looking to learn more about machine learning, you're in luck! There are many high-quality courses available online, offered by some of the top universities in the world. In this article, we'll introduce you to 10 free machine learning courses from top universities.

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One of the big trends in AI this year: AI is maturing as a domain. We are using AI to address complex problems. That means we will need to be more aware of the potential downsides of AI. I believe that a new trend could manifest: responsible AI by design. I also see other papers and references in this direction: https://lnkd.in/ei8QUqVF

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Are there courses that are beneficial for machine learning and data analysis? Here is a list 20 free courses in Data Science from the Massachusetts Institute of Technology, Stanford University, and Harvard University. 1. Introduction to Computer Science and Programming Using Python (MIT) https://lnkd.in/eFrBJt7V

Do you know how ChatGPT was trained? ChatGPT is "simply" a fined-tuned GPT-3 model with a surprisingly small amount of data! Moreover, ChatGPT is using 1.3B parameters where GPT-3 uses 175B parameters! It is first fine-tuned with supervised learning and then further fine-tuned with reinforcement learning. They hired 40 human labelers to generate the training data.