Posted by Josh Gordon, Developer Advocate A new HarvardX TinyML course on edX.orgProf. Vijay Janapa Reddi of Harvard, the TensorFlow Lite Micro team, and the edX online learning platform are sharing a series of short TinyML courses this fall that you can observe for free, or sign up to take and receive a certificate. In this article, I'll share a bit about TinyML, what you can do with it, and the …
TL;DR: The Become a Speed Reading Machine course is on sale for £19.14 as of August 5, saving you 87% on list price. If you're being honest, you've probably always been secretly -- and irrationally -- jealous of speedy readers. Back in school, there were always a few classmates who zoomed through a dense chapter and got to start lunch early. The rest of us were stuck decoding a confusing, run-on sentence while our milk got warm. Now those kids are colleagues who answer emails quicker, read more news, and are arguably more productive throughout the day.
Online Courses Udemy - Deep Reinforcement Learning 2.0, The smartest combination of Deep Q-Learning, Policy Gradient, Actor Critic, and DDPG Created by Hadelin de Ponteves, Kirill Eremenko, SuperDataScience Team English [Auto] Students also bought Unsupervised Deep Learning in Python Deep Learning: Advanced Computer Vision (GANs, SSD, More!) Data Science: Natural Language Processing (NLP) in Python Recommender Systems and Deep Learning in Python Cutting-Edge AI: Deep Reinforcement Learning in Python Ensemble Machine Learning in Python: Random Forest, AdaBoost Preview this course GET COUPON CODE Description Welcome to Deep Reinforcement Learning 2.0! In this course, we will learn and implement a new incredibly smart AI model, called the Twin-Delayed DDPG, which combines state of the art techniques in Artificial Intelligence including continuous Double Deep Q-Learning, Policy Gradient, and Actor Critic. The model is so strong that for the first time in our courses, we are able to solve the most challenging virtual AI applications (training an ant/spider and a half humanoid to walk and run across a field). To approach this model the right way, we structured the course in three parts: Part 1: Fundamentals In this part we will study all the fundamentals of Artificial Intelligence which will allow you to understand and master the AI of this course. These include Q-Learning, Deep Q-Learning, Policy Gradient, Actor-Critic and more.
At the end of the Course you will understand the basics of Artificial Neural Networks. The course will have step by step guidance for Artificial Neural network development in Python. I have 9 years of work experience as a Researcher, Senior Lecturer, Project Supervisor & Engineer. I have completed a MSc in Artificial Intelligence.
Throughout the rest of my high school, I learned about game development, advanced data structures and algorithms, but not much about AI. The only exposure I had to Machine Learning was this website here, which didn't make a whole lot of sense to me back then. Fast forward, I returned to India and was attending Eastern Public School, finishing up my 12th grade with an International Baccalaureate diploma. I started the Stanford University Machine Learning course taught by Dr. Andrew Ng, http://ml-class.org/. The best part is it does not use any high level libraries to teach the concepts to you, so you have to use MATLAB to answer all the programming assignments.
Online Courses Udemy - TensorFlow 2.0 Practical, Master Tensorflow 2.0, Google's most powerful Machine Learning Library, with 10 practical projects 4.3 (197 ratings), Created by Dr. Ryan Ahmed, Ph.D., MBA, Kirill Eremenko, Hadelin de Ponteves, SuperDataScience Team, Mitchell Bouchard, English [Auto-generated] Preview this Udemy course -. GET COUPON CODE Description Artificial Intelligence (AI) revolution is here and TensorFlow 2.0 is finally here to make it happen much faster! TensorFlow 2.0 is Google's most powerful, recently released open source platform to build and deploy AI models in practice. AI technology is experiencing exponential growth and is being widely adopted in the Healthcare, defense, banking, gaming, transportation and robotics industries. The purpose of this course is to provide students with practical knowledge of building, training, testing and deploying Artificial Neural Networks and Deep Learning models using TensorFlow 2.0 and Google Colab.
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