Yann LeCun's deep learning course -- Deep Learning DS-GA 1008 -- at NYU Centre for Data Science has been made free and accessible online for all. The course will be led by Yann LeCun himself, along with Alfredo Canziani, an assistant professor of computer science at NYU, in Spring 2020. This deep learning course will focus on the latest techniques in deep learning and representation learning. It will also focus on an in-depth understanding of supervised and unsupervised deep learning, embedding methods, metric learning, and convolutional and recurrent nets. The course will further talk about the applications to computer vision, natural language understanding, and speech recognition.

Reinforcement learning has grown rapidly in the past few years, from tabular methods that can only solve simple toy problems to powerful algorithms that tackle incredibly complex problems such as playing Go, learning robotic manipulation skills or controlling autonomous vehicles. Unfortunately, adoption of RL for real-world applications has been somewhat slow, and while current RL methods have proven their ability to find high performing policies for challenging problems with high-dimensional raw observations (such as images), actually using them is often difficult or impractical. This is in stark contrast to supervised learning methods, which are highly prevalent in many fields of industry and research and are utilized with great success. Most RL research papers and implementations are geared towards the online learning setting, in which the agent interacts with an environment and gathers data, using its current policy and some exploration scheme to explore the state-action space and find higher-reward areas. Such online RL algorithms interact with the environment and use the gathered experience either immediately or via some replay buffer to update the policy.

This Friday the 13th of November at 8pm UTC (3pm EDT/12pm PDT), Robotics Today are hosting an online seminar with Professor Davide Scaramuzza from the University of Zurich. "Robotics Today – A series of technical talks" is a virtual robotics seminar series. The goal of the series is to bring the robotics community together during these challenging times. The seminars are open to the public. The format of the seminar consists of a technical talk live captioned and streamed via Web and Twitter, followed by an interactive discussion between the speaker and a panel of faculty, postdocs, and students that will moderate audience questions.

The motive behind Creating this repo is to feel the fear of mathematics and do what ever you want to do in Machine Learning, Deep Learning and other fields of AI . So, try this Code in your python notebook which is provided in edx Course. In this Repo you will also learn the Libraries which are essential like numpy, pandas, matplotlib... I am going to upload new material when i find those material useful, you can also help me in keeping this repo fresh. Selecting the right algorithm which includes giving considerations to accuracy, training time, model complexity, number of parameters and number of features.

Let us consider that you are running an E-commerce website. You have a long list of products(in millions) available, and you want to eliminate the duplicate product names that might be listed under different categories. For example, there are two different products named iPhone X and Apple iPhone 10 (both mean the same product, but why do we need different names?) Another instance I encountered was a case where Amazon was selling the same N-95 Masks on different names like - 1)Covid-19 Masks. Your final task is to rename all the duplicate names to one common name. But firstly, you need to find out the products which are having duplicate names.

Machine Learning foners Second Edition has been written and designed for absolute beginners. This means plain-English explanations and no coding experience required. Where core algorithms are introduced, clear explanations and visual examples are added to make it easy and engaging to follow along at home. This major new edition features many topics not covered in the First Edition, including Cross Validation, Data Scrubbing and Ensemble Modeling.

Register for the Orbit Online Workshop on Context-sensitive Responsible Innovation Assessment Register for our Context-sensitive Responsible Innovation Assessment workshop on 12th November 2-4pm to: Hear how industry leading organisations tailor their responsible innovation practices to different stages of the innovation lifecycle Learn about new research in responsible innovation assessment Discuss Responsible Innovation assessment methodologies including self-assessment tools and BSI PAS440 Contribute to research on context-sensitive responsible innovation assessment Confirmed Speakers Fran Baker Global Social Innovation Lead, Arm Holdings Fran provides strategic direction for social innovation at Arm, creating and surfacing new opportunities that enable technology solutions for social impact at scale, particularly in low resource settings. Nurturing entrepreneurial social innovations, through to managing our global partnerships with UNICEF Innovation and the Bill and Melinda Gates Foundation, and collaborating internally for sustainable transformation and growth. Lucy Gonzalez Research Project Manager, Arm Holdings As a professional in the field of project management with experience delivering research projects in a variety of contexts and disciplines and particular interests in sustainability, Lucy provides insight into key considerations for integrating responsibility issues into different stages of product lifecycles. Cecily Morrison Principal Researcher, Microsoft Research Cambridge Cecily’s research in the Human Experience & Design (HXD) community at Microsoft Research focusses on the development of novel technologies to enable people’s health and well-being in the broadest sense, at the intersection of Human-Computer Interaction and Artificial Intelligence. Working in a cross-disciplinary collaboration, her current focus is on AI applications for those with visual disabilities. She holds a PhD in Computer Science from University of Cambridge. Daniel Barlow Head of Innovation Policy at BSI Daniel is the Head of Innovation Policy at BSI, the UK’s National Standards Body. He is responsible for promoting standards to supercharge the economic, societal and environmental benefit of UK innovation. Previous experience includes leading Digital Manufacturing at Rolls-Royce and Aerospace trade strategy in the Department for International Trade. He is a Chartered Engineer.

Created by Lazy Programmer Team, Lazy Programmer Inc. Students also bought Deep Learning A-Z: Hands-On Artificial Neural Networks Complete Guide to TensorFlow for Deep Learning with Python Data Science: Deep Learning in Python Natural Language Processing with Deep Learning in Python Preview this course Udemy GET COUPON CODE Welcome to PyTorch: Deep Learning and Artificial Intelligence! Although Google's Deep Learning library Tensorflow has gained massive popularity over the past few years, PyTorch has been the library of choice for professionals and researchers around the globe for deep learning and artificial intelligence. Is it possible that Tensorflow is popular only because Google is popular and used effective marketing? Why did Tensorflow change so significantly between version 1 and version 2? Was there something deeply flawed with it, and are there still potential problems? It is less well-known that PyTorch is backed by another Internet giant, Facebook (specifically, the Facebook AI Research Lab - FAIR).

This channel publishes interviews with data scientists from big companies like Google, Uber, Airbnb, etc. From these videos, you can get an idea of what it is like to be a data scientist and acquire valuable advice to apply in your life. A new ML Youtube channel that everyone should check out, Machine Learning 101 posts explainer videos on beginner AI concepts. The channel also posts podcasts with expert data scientists and professionals working on AI in commercial industries. FreeCodeCamp is an incredible non-profit organization. It is an open-source community that offers a collection of resources that helps people learn to code for free and create their projects.

This is THE practice exam course to give you the winning edge. Considered to be the toughest of all AWS certification exams, the MLS-C01 tests you in three areas – AWS specific concepts, Deep Learning fundamentals and real-world experience of building solutions by bridging AWS services with Deep Learning solutions. Our AWS Certified Machine Learning--Specialty practice exams are CLOSEST to the actual exam. You'll need deep and broad knowledge of SageMaker and AWS's other machine learning services, including Rekognition, Translate, Polly, and Comprehend. You'll need to know how to process big data using Kinesis, S3, Glue, and Athena.