This short course will explore how Artificial Intelligence (AI) will impact a workplace from an L&D and HR perspective. You will learn the gap in skillsets that are required for the future workforce. We will explore the benefits and pitfalls of AI and what organisations need to do. You will learn the basics of Artificial Intelligence and how L&D is the main driver for this transformational change and the importance of AI Ethics Codes of Practice. Covering some of the key topics that you can easily implement when your organisation goes through a technological transformation.

This tutorial provides a simple introduction to using multi-agent reinforcement learning, assuming a little experience in machine learning and knowledge of Python. Reinforcement stems from using machine learning to optimally control an agent in an environment. It works by learning a policy, a function that maps an observation obtained from its environment to an action. Policy functions are typically deep neural networks, which gives rise to the name "deep reinforcement learning." The goal of reinforcement learning is to learn an optimal policy, a policy that achieves the maximum expected reward from the environment when acting.

Arduino Deep Learning From Ground Up Build Artificial Intelligence Sketch from Scratch on Arduino What you'll learn Welcome to the Arduino Deep Learning From Ground Up . We are going to embark on a very exciting journey together. We are going to learn how to build deep neural networks from scratch on our Arduino. We shall begin by learning the basics of deep learning with practical code showing each of the basic building blocks that end up making a giant deep neural network. As we begin to deal with large datasets we shall start training our neural networks on our computers and then deploying the the trained models on our microcontrollers.

Free Coupon Discount - Careers in Data Science A-Z, How to Become a Top Level Data Scientist - Learn What to Expect, How to be Prepared, How to Stand Out and More... Created by Kirill Eremenko Hadelin de Ponteves SuperDataScience Team Students also bought Data Manipulation in Python: A Pandas Crash Course Cluster Analysis and Unsupervised Machine Learning in Python Statistics Masterclass for Data Science and Data Analytics Neural Networks in Python from Scratch: Complete guide Data Analysis Bootcamp 21 Real World Case Studies Data Science Career Guide - Interview Preparation Preview this Udemy Course GET COUPON CODE Description Becoming a Data Scientist might be on your mind right now. Named the "Sexiest Job of the 21st Century", this career seems like a great idea not only due to its high demand, but lack of supply of skilled proffesionals. But the million dollar question is: What makes the difference between Top Level Data Scientist and just another one from the bunch? Here is where this course jumps in... With over 8 years combined experience in the field, we've decided to step back and put all of the lessons we've learned through our careers into one simple course.

In this podcast series of episodes we are going to explain how to create a robotics startup step by step. We are going to learn how to select your co-founders, your team, how to look for investors, how to test your ideas, how to get customers, how to reach your market, how to build your product… Starting from zero, how to build a successful robotics startup. I'm Ricardo Tellez, CEO and co-founder of The Construct startup, a robotics startup at which we deliver the best learning experience to become a ROS Developer, that is, to learn how to program robots with ROS. Our company is already 5 years long, we are a team of 10 people working around the world. We have more than 100.000

Applied Machine Learning with R (Trading Use Case) - 2020 Learn the complete quantitative finance workflow and use machine learning algorithms in R to develop trading strategies What you'll learn The course is designed to fully immerse you into the complete quantitative trading/finance workflow, going from hypothesis generation to data preparation, feature engineering and training testing of multiple machine learning algorithms (backtesting). It is a bootcamp designed to get you from zero to hero using R. The course is aimed at teaching about trading, giving you understanding of the differences between discretionary and quantitative trading. You will learning about different trading instruments/products or also known as asset classes. Disclaimer This course is for educational purpose and does not constitute trading or investment advice.

In computer vision, object detection is the problem of locating one or more objects in an image. Besides the traditional object detection techniques, advanced deep learning models like R-CNN and YOLO can achieve impressive detection over different types of objects. These models accept an image as the input and return the coordinates of the bounding box around each detected object. This tutorial discusses the confusion matrix, and how the precision, recall and accuracy are calculated. In another tutorial, the mAP will be discussed. In binary classification each input sample is assigned to one of two classes. Generally these two classes are assigned labels like 1 and 0, or positive and negative.

Learn to build Toxic Question Classifier engine with BERT and TensorFlow 2.4. A Powerful Skill at Your Fingertips Learning the fundamentals of text classification h puts a powerful and very useful tool at your fingertips. Python and Jupyter are free, easy to learn, have excellent documentation. Text classification is a fundamental task in natural language processing (NLP) world. No prior knowledge of word embedding or BERT is assumed.

Good news for folks looking to learn about the latest AI development techniques: Nvidia is now allowing the general public to access the online workshops it provides through its Deep Learning Institute (DLI). The GPU giant today announced today that selected workshops in the DLI catalog will be open to everybody. These workshops previously were available only to companies that wanted specialized training for their in-house developers, or to folks who had attended the company's GPU Technology Conferences. Two of the open courses will take place next month, including "Fundamentals of Accelerated Computing with CUDA Python," which explores developing parallel workloads with CUDA and NumPy and cost $500. There is also "Applications of AI for Predictive Maintenance," which explores technologies like XGBoost, LSTM, Keras, and Tensorflow, and costs $700.

We present a new concept called Game Mechanic Alignment theory as a way to organize game mechanics through the lens of environmental rewards and intrinsic player motivations. By disentangling player and environmental influences, mechanics may be better identified for use in an automated tutorial generation system, which could tailor tutorials for a particular playstyle or player. Within, we apply this theory to several well-known games to demonstrate how designers can benefit from it, we describe a methodology for how to estimate mechanic alignment, and we apply this methodology on multiple games in the GVGAI framework. We discuss how effectively this estimation captures intrinsic/extrinsic rewards and how our theory could be used as an alternative to critical mechanic discovery methods for tutorial generation.