Hey guys, I’m starting my machine learning company and in this video I want to touch on what got me started with this and what plans I’ve made. I hope this video is motivational for a lot of you guys who are starting something new, whether it is learning something brand new, switching careers or even starting your own company. I know that there is a huge learning curve ahead and that this will really put me outside of my comfort zone. I’m sure there will to plenty of failures and success (fingers crosses) so it will be great for me to document! To watch all of this, be sure to subscribe! ————————————————————————- LINKS: ————————————————————————– DOWNLOAD Machine Learning Roadmap 2021: https://learnml.substack.com ————————————————————————– MORE VIDEOS: ————————————————————————– 📌Top Machine Learning Certifications For 2021 https://youtu.be/YhXzUZGKhIY 📌Why You Should NOT Learn Machine Learning! https://youtu.be/reY50t2hbuM 📌How I Learnt Machine Learning In 6 Steps (3 months) https://youtu.be/OuC3wgp1Fnw 📌How To Learn Machine Learning For Free https://youtu.be/QNKYKzTGerA ————————————————————————– Follow me: ————————————————————————– Subscribe: http://bit.ly/subscribeToSmitha​​ LinkedIn: http://bit.ly/SmithaKolan​​ Instagram: http://bit.ly/smithacodes​​ background music: bensound.com
AI Teaches Itself How to Escape! In this video an AI named Albert learns how to escape 5 rooms I’ve designed. The AI was trained using Deep Reinforcement Learning, a method of Machine Learning which involves rewarding the agent for doing something correctly, and punishing it for doing anything incorrectly. Albert’s actions are controlled by a Neural Network that’s updated after each attempt in order to try to give Albert more rewards and less punishments over time. Everything in this video (except for the music) was created entirely by myself using Unity. Check the pinned comment for more information on how the AI was trained! Current Subscribers: 0
For more information about Stanford’s Artificial Intelligence professional and graduate programs, visit: https://stanford.io/3CORGu1 This lecture covers many topics within Natural Language Understanding, including: -The Course (10 min) -Human language and word meaning (15 min) -Wordzvec introductions (15 min) -WordZvec objective function gradients (25 min) -Optimization basics (5 min) -Looking at word vectors (10 min for less) Professor Christopher Manning Thomas M. Siebel Professor in Machine Learning, Professor of Linguistics and of Computer Science Director, Stanford Artificial Intelligence Laboratory (SAIL) To follow along with the course schedule and syllabus, visit: http://web.stanford.edu/class/cs224n/index.html#schedule 00:41 Welcome 01:31 Overview for the lecture 01:56 Lecture Plan & Overview 02:02 Course logistics in brief 02:52 What do we hope to teach in this course? 05:39 Course work and grading policy 07:02 High-level plan for problem sets #ChristopherManning #naturallanguageprocessing #deeplearning
This webinar spotlights the updates and progress since the January 2021 release of U.S. Food and Drug Administration’s Center for Device and Radiological Health’s (FDA CDRH) AI/ML Action Plan. Each panel focuses on one aspect of the Action plan, starting with an overall framework for regulating AI, development of Good Machine Learning Practices, and post-market evaluation of AI/ML Software as a Medical Device (SaMD).
Control Statements – for loop Slides: https://tinyurl.com/gectpython
Anima Anandkumar of Caltech and NVIDIA. This talk was given on April 1, 2022. Autonomous robots need to be efficient and agile, and be able to handle a wide range of tasks and environmental conditions. This requires the ability to learn good representations of domains and tasks using a variety of sources such as demonstrations and simulations. Representation learning for robotic tasks needs to be generalizable and robust. I will describe some key ingredients to enable this: (1) robust self-supervised learning (2) uncertainty awareness (3) compositionality. We utilize NVIDIA Isaac for GPU-accelerated robot learning at scale on a variety of tasks and domains. 00:48 Generlizable Learning for Robotics 01:53 Trinity of Generalizable AI 05:08 Physical World if Continuous 07:08 Motivating Problem in Robotics 08:35 State estimation through PDE observer 09:59 Grid-free learning for continuous phenomena 12:32 Neural Operator 15:02 Fourier Transform for global convolution 16:06 FNO: Fourier Neural Operator 18:34 First ML method to solve fluid flow 25:42 Nvidia Modulus 29:08 Operational Space Control (OSC) 38:54 Reducing Supervision and enhancing robustness 47:06 Conclusion Learn more about Stanford Online’s Robotics Program and courses here: https://online.stanford.edu/programs/robotics-and-autonomous-systems-graduate-program
Dave Waters once said predicting the future isn’t magic, its artificial intelligence.When it comes to machine learning, you may have heard of it as a subset of AI, but there is more to it. Join our FREE masterclass: https://bit.ly/3wFnlfd What is Artificial Intelligence? Artificial intelligence (AI) is intelligence demonstrated by machines, as opposed to natural intelligence displayed by animals including humans. Leading AI textbooks define the field as the study of “intelligent agents”: any system that perceives its environment and takes actions that maximise its chance of achieving its goals.Some popular accounts use the term “artificial intelligence” to describe machines that mimic “cognitive” functions that humans associate with the human mind, such as “learning” and “problem solving”, however, this definition is rejected by major AI researchers. What is Machine Learning? Machine learning (ML) is the study of computer algorithms that can improve automatically through experience and by the use of data.It is seen as a part of artificial intelligence. Machine learning algorithms build a model based on sample data, known as training data, in order to make predictions or decisions without being explicitly programmed to do so. Machine learning algorithms are used in a wide variety of applications, such as in medicine, email filtering, speech recognition, and computer vision, where it is difficult or unfeasible to develop conventional algorithms to perform the needed tasks. The following discussion will interest you if you are looking forward to picking a career path between the two by deriving a thorough understanding on how [More]
First lecture of MIT course 6.S091: Deep Reinforcement Learning, introducing the fascinating field of Deep RL. For more lecture videos on deep learning, reinforcement learning (RL), artificial intelligence (AI & AGI), and podcast conversations, visit our website or follow TensorFlow code tutorials on our GitHub repo. INFO: Website: https://deeplearning.mit.edu GitHub: https://github.com/lexfridman/mit-deep-learning Slides: http://bit.ly/2HtcoHV Playlist: http://bit.ly/deep-learning-playlist OUTLINE: 0:00 – Introduction 2:14 – Types of learning 6:35 – Reinforcement learning in humans 8:22 – What can be learned from data? 12:15 – Reinforcement learning framework 14:06 – Challenge for RL in real-world applications 15:40 – Component of an RL agent 17:42 – Example: robot in a room 23:05 – AI safety and unintended consequences 26:21 – Examples of RL systems 29:52 – Takeaways for real-world impact 31:25 – 3 types of RL: model-based, value-based, policy-based 35:28 – Q-learning 38:40 – Deep Q-Networks (DQN) 48:00 – Policy Gradient (PG) 50:36 – Advantage Actor-Critic (A2C & A3C) 52:52 – Deep Deterministic Policy Gradient (DDPG) 54:12 – Policy Optimization (TRPO and PPO) 56:03 – AlphaZero 1:00:50 – Deep RL in real-world applications 1:03:09 – Closing the RL simulation gap 1:04:44 – Next step in Deep RL CONNECT: – If you enjoyed this video, please subscribe to this channel. – Twitter: https://twitter.com/lexfridman – LinkedIn: https://www.linkedin.com/in/lexfridman – Facebook: https://www.facebook.com/lexfridman – Instagram: https://www.instagram.com/lexfridman
We’ll share our list of the best machine learning project ideas. Here’s how they can be taken to a business ground and of course, drive revenue. ▶ Contact Jelvix: hello@jelvix.com | jelvix.com We are a technology consulting and software development company eager to share our knowledge and experience. Subscribe for more tech tips and tutorials: https://www.youtube.com/channel/UCEDr9FfkfzsT-hJOQsyKyvg?sub_confirmation=1 ▶ LINKS: – Best Data Analytics Tools – https://jelvix.com/blog/data-analytics-tools – More about ML project ideas – https://jelvix.com/blog/machine-learning-project-ideas ▶ TIME CODES: 00:00 Intro 00:34 ML and AI used in business 00:48 Diagnosing Parkinson’s 01:12 Improving video calls 01:31 Smarter speech recognition 02:03 Content making 02:33 Routine writing 02:51 Detecting objects, animals, and people 03:13 Trying on clothes 03:36 Generating textures and patterns 03:55 Better investments 04:19 Generate ideas 04:52 Contact Jelvix ▶ Follow us: Facebook – https://www.facebook.com/JelvixCompany Twitter – https://twitter.com/jelvix Instagram – https://www.instagram.com/jelvix Linkedin – https://www.linkedin.com/company/jelvix Upwork – https://www.upwork.com/ag/jelvix/ ▶ About this video: The first goal of using AI is solving a problem. But how can a business owner use machine learning to build a product that fulfills tangible needs? So here’s our list of 10 Machine Learning project ideas and real-life case studies.
Learning bike skills doesn’t have to be boring – it can be fun too!! Harley loves to jump onto his manual machine and try a few manuals out – it’s why we just leave it set up in the front room most of the time!! With practice he’s starting to get the hang of the ‘squash and pop’ technique and now isn’t using the brakes to control his height – he’s just using the sofa behind him to land on instead :p He has on a number of occasions managed to hold the bike up at the balance point for a few seconds… something he gets overly excited about when it happens!!! For those wandering: Why we do this? It’s because he enjoys it!! What is he training for? Nothing at all… he’s just happily learning some bike skills on his terms and in his own time. What is the point in a manual machine? Well… it’s actually taught Harley a much better technique on the BMX racing track when he’s attempting to manual there and it’s improved his ability to jump – he can now clear small doubles/gap jumps riding on his MTB and dirt jump bike thanks to this!! Videos recorded as a 2 year old riding a @Cult Crew Juvenile 12inch BMX (first clip and in his PJs!) and as a 3 year old (nearly 4… a week before his birthday) on a @Spawn Cycles Yoji 16inch MTB (that he uses as his BMX because he prefers [More]
China is pursuing an ambitious plan to make an omnipresent video surveillance network to track where people are and what they’re up to. The Post’s Simon Denyer looks at the technology that will make this possible. Subscribe to The Washington Post on YouTube: http://bit.ly/2qiJ4dy Follow us: Twitter: https://twitter.com/washingtonpost Instagram: https://www.instagram.com/washingtonpost/ Facebook: https://www.facebook.com/washingtonpost/
Language barriers are very much still a real thing. We can take baby steps to help close that. Speech to text and translators have made it a heap easier. But what about for those that maybe don’t speak or can’t hear? What about them? Well…you can begin to use Tensorflow Object Detection and Python to help close that gap. And in this video, you’ll learn how to take the first steps to doing just that! In this video, you’ll learn how to build an end-to-end custom object detection model that allows you to translate sign language in real time. In this video you’ll learn how to: 1. Collect images for deep learning using your webcam and OpenCV 2. Label images for sign language detection using LabelImg 3. Setup Tensorflow Object Detection pipeline configuration 4. Use transfer learning to train a deep learning model 5. Detect sign language in real time using OpenCV Get the training template here: https://github.com/nicknochnack/RealTimeObjectDetection Other Links Mentioned in the Video Face Mask Detection Video: https://youtu.be/IOI0o3Cxv9Q LabelImg: https://github.com/tzutalin/labelImg Installing the Tensorflow Object Detection API: https://tensorflow-object-detection-api-tutorial.readthedocs.io/en/latest/install.html Oh, and don’t forget to connect with me! LinkedIn: https://www.linkedin.com/in/nicholasrenotte Facebook: https://www.facebook.com/nickrenotte/ GitHub: https://github.com/nicknochnack Happy coding! Nick P.s. Let me know how you go and drop a comment if you need a hand!
Description I used the Doc2Vec framework to analyze user comments on German online news articles and uncovered some interesting relations among the data. Furthermore, I fed the resulting Doc2Vec document embeddings as inputs to a supervised machine learning classifier. Can we determine for a particular user comment from which news site it originated? Abstract Doc2Vec is a nice neural network framework for text analysis. The machine learning technique computes so called document and word embeddings, i.e. vector representations of documents and words. These representations can be used to uncover semantic relations. For instance, Doc2Vec may learn that the word “King” is similar to “Queen” but less so to “Database”. I used the Doc2Vec framework to analyze user comments on German online news articles and uncovered some interesting relations among the data. Furthermore, I fed the resulting Doc2Vec document embeddings as inputs to a supervised machine learning classifier. Accordingly, given a particular comment, can we determine from which news site it originated? Are there patterns among user comments? Can we identify stereotypical comments for different news sites? Besides presenting the results of my experiments, I will give a short introduction to Doc2Vec. www.pydata.org PyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, [More]
In this video I explain the difference between AI and ML and how both can be used in business to solve real world problems. If you would like more information on this topic, please feel free to visit my website and sign up for content updates! I write articles every week on various different topics such as Big Data, Artificial Intelligence & Machine Learning. Visit the Artificial Intelligence & Machine Learning topic page: https://bernardmarr.com/default.asp?contentID=1314 Thanks for watching!
Join the community session https://ineuron.ai/course/Mega-Community . Here All the materials will be uploaded. Playlist: https://www.youtube.com/watch?v=11unm2hmvOQ&list=PLZoTAELRMXVMgtxAboeAx-D9qbnY94Yay The Oneneuron Lifetime subscription has been extended. In Oneneuron platform you will be able to get 100+ courses(Monthly atleast 20 courses will be added based on your demand) Features of the course 1. You can raise any course demand.(Fulfilled within 45-60 days) 2. You can access innovation lab from ineuron. 3. You can use our incubation based on your ideas 4. Live session coming soon(Mostly till Feb) Use Coupon code KRISH10 for addition 10% discount. And Many More….. Enroll Now OneNeuron Link: https://one-neuron.ineuron.ai/ Direct call to our Team incase of any queries 8788503778 6260726925 9538303385 866003424
In this project we will build a car price predictor using Linear Regression. We will also convert it into a full-fledged website using the flask framework. Link to dataset: https://github.com/rajtilakls2510/car_price_predictor/blob/master/quikr_car.csv Link to Notebook: https://github.com/rajtilakls2510/car_price_predictor/blob/master/quikr_car.csv
In this video, we are building a system in Python that can predict whether an object is either Rock or Mine with SONAR Data. For this use case, we are using Logistic Regression Model for our prediction. We will be doing our Python Programming in Google Colaboratory. Enroll at One Neuron to learn from 100 courses in one subscription with 5% discount: https://courses.ineuron.ai/neurons/Tech-Neuron?campaign=affiliate&coupon_code=SID5 Hi guys! I am Siddhardhan. I work in the field of Data Science and Machine Learning. It all started with my curiosity to learn about Artificial Intelligence and the ability of AI to solve several Real Life Problems. I worked on several Machine Learning & Deep Learning projects involving Computer Vision. I am on this journey to empower as many students & working professionals as possible with the knowledge of Machine Learning and Artificial Intelligence. Hello everyone! I am setting up a donation campaign for my YouTube Channel. If you like my videos and wish to support me financially, you can donate through the following means: From India 👉 UPI ID : siddhardhselvam2317@oksbi Outside of India? 👉 Paypal id: siddhardhselvam2317@gmail.com (No donation is small. Every penny counts) Thanks in advance! Let’s build a Community of Machine Learning experts! Kindly Subscribe here👉 https://tinyurl.com/md0gjbis I am making a “Hands-on Machine Learning Course with Python” in YouTube. I’ll be posting 3 videos per week. 2 videos on Machine Learning basics (Monday & Wednesday Evening). 1 video on a Machine Learning project (Friday Evening). Dataset file link: https://drive.google.com/file/d/1pQxtljlNVh0DHYg-Ye7dtpDTlFceHVfa/view?usp=drivesdk Colab file link: [More]
Visit https://brilliant.org/PythonProgrammer/ to get started for free and get 20% off your annual subscription. Thanks to Brilliant for sponsoring this video 🙂 Here are the courses mentioned:- https://docs.microsoft.com/en-us/learn/ https://developers.google.com/machine-learning/crash-course/ https://www.kaggle.com/learn https://www.youtube.com/watch?v=C1lhuz6pZC0&list=PLUl4u3cNGP619EG1wp0kT-7rDE_Az5TNd https://www.youtube.com/user/StevenSkiena https://www.pythonhealthdatascience.com/content/front_page.html https://www.earthdatascience.org/tutorials/ https://www.sql-easy.com/ https://pandas.pydata.org/docs/getting_started/index.html https://www.freecodecamp.org/learn/data-analysis-with-python/ Learn Python with Giles 🎓 Exploratory Data Analysis with Python and Pandas – https://bit.ly/2QXMpxJ 🎓 Complete Python Programmer Bootcamp – http://bit.ly/2OwUA09 📚 My favourite python books for beginners (affiliate links) 📗 Python Crash Course 2nd Edition https://amzn.to/33tATAE 📘 Automate the Boring Stuff with Python https://amzn.to/3qM1DFl 📙 Python Basics – A Practical Introduction to Python 3 https://amzn.to/3fHRMdb 📕 Python Programming An Introduction to Computer Science https://amzn.to/33VeQCr 📗 Invent Your Own Computer Games with Python https://amzn.to/3FM3H4b 🆓 Free Python Resource https://python-programming.quantecon.org/intro.html (This is a great introduction to python) ⚙ My Gear 💡 BenQ Screen Bar Desk Light – https://amzn.to/3tH6ysL 🎧 Sony Noise Cancelling Headphones – https://amzn.to/3tLl82G 📱 Social Media https://www.instagram.com/gilesmcmullen/ https://twitter.com/GilesMcMullen 👌 SUBSCRIBE to ME!👌 https://www.youtube.com/channel/UC68KSmHePPePCjW4v57VPQg?sub_confirmation=1
What is Machine Learning → http://ibm.biz/machine-learning-is-simple What is Deep Learning → http://ibm.biz/Get-deep-with-deep-learning Get a unique perspective on what the difference is between Machine Learning and Deep Learning – explained and illustrated in a delicious analogy of ordering pizza by IBMer and Master Inventor, Martin Keen. Download a free AI ebook → http://ibm.biz/Get-my-ebook Get started for free on IBM Cloud → http://ibm.biz/Get-on-IBM-cloud-now Subscribe to see more videos like this in the future → http://ibm.biz/subscribe-now #AI #Software #ITModernization #DeepLearning #MachineLearning
Special Thanks To Ashish Patel And All The Bloggers For the Amazing Contribution https://www.linkedin.com/in/ashishpatel2604/ https://github.com/ashishpatel26/500-AI-Machine-learning-Deep-learning-Computer-vision-NLP-Projects-with-code ⭐ Kite is a free AI-powered coding assistant that will help you code faster and smarter. The Kite plugin integrates with all the top editors and IDEs to give you smart completions and documentation while you’re typing. I’ve been using Kite for a few months and I love it! https://www.kite.com/get-kite/?utm_medium=referral&utm_source=youtube&utm_campaign=krishnaik&utm_content=description-only Starter In Data Science 1 Complete Machine Learning Playlist:(Top 24 videos) 2 Statistics in Machine Learning:(Understand some Concepts With Respect To Data)- Complete Playlist https://www.youtube.com/playlist?list=PLZoTAELRMXVMhVyr3Ri9IQ-t5QPBtxzJO 3. Feature Engineering(Complete Playlist) https://www.youtube.com/playlist?list=PLZoTAELRMXVPwYGE2PXD3x0bfKnR0cJjN 4. Continue The Complete Machine Learning Playlist(24-all the videos) 5. Live Stream Playlist:(Top 10 videos) https://www.youtube.com/playlist?list=PLZoTAELRMXVPUyxuK8AphGMuIJHTyuWna 6. Machine Learning Pipelines 7. Complete Deep Learning Playlist: Tensorflow And Keras-https://www.youtube.com/playlist?list=PLZoTAELRMXVPGU70ZGsckrMdr0FteeRUi Pytorch: https://www.youtube.com/playlist?list=PLZoTAELRMXVNxYFq_9MuiUdn2YnlFqmMK 8. Live Projects Playlist: https://www.youtube.com/playlist?list=PLZoTAELRMXVOFnfSwkB_uyr4FT-327noK 9. Live stream Playlist: https://www.youtube.com/playlist?list=PLZoTAELRMXVPUyxuK8AphGMuIJHTyuWna 10.Docker Playlist: https://www.youtube.com/playlist?list=PLZoTAELRMXVNKtpy0U_Mx9N26w8n0hIbs 11. Mongodb: https://www.youtube.com/playlist?list=PLZoTAELRMXVN_8zzsevm1bm6G-plsiO1I 12. Machine Learning Interviews: https://www.youtube.com/playlist?list=PLZoTAELRMXVM0zN0cgJrfT6TK2ypCpQdY
#WhatisMachineLearning #QuickSupport #Technology What is Machine Learning With Full Information? – [Hindi] – Quick Support. आज इस विडियो में हम एक बहुत ही मशहुर तकनीक के बारे में जानेंगें जिसका नाम है Machine Learning. और आप में से बहुत से लोगों ने इसका नाम सुना होगा लेकिन अगर इसके बारे में आप और अधिक जानकारी हासिल करना चाहते हैं तो इस video में हमारे साथ बने रहिये जिसमे हम आपको बताने वाले हैं machine learning क्या है, यह काम कैसे करता है और इसके क्या फायेदे होते हैं. Website: https://qsofficial.com Facebook: https://www.facebook.com/QuickSupportChannel Twitter: https://twitter.com/QS_Channel Instagram: https://www.instagram.com/quick_support007 Youtube: https://www.youtube.com/c/QuickSupport Linkedin: https://www.linkedin.com/in/Anil-Nakrani Channel Owner: Anil Nakrani
The DeepMind team shares how JAX and TPUs have been used to push state-of-the-art research in protein folding and how it made AlphaFold possible. Speaker: Tim Green (Research Engineer, DeepMind) Watch all Google’s Machine Learning Virtual Community Day sessions → https://goo.gle/mlcommunityday-all Subscribe to the TensorFlow channel → https://goo.gle/TensorFlow #MLCommunityDay product: TensorFlow – General; event: ML Community Day 2021; fullname: Tim Green; re_ty: Publish;
Why Machine Learning Is the Next Big Thing In Future – Sundar Pichai Hey Beautiful People Mk Here ! Subscribe for new motivational videos every Day ✨ Sundar Pichai ; Pichai Sundararajan, better known as Sundar Pichai, is an Indian-American business executive. He is the chief executive officer of Alphabet Inc. and its subsidiary Google. Born in Madurai, India, Pichai earned his degree from IIT Kharagpur in metallurgical engineering. Let’s Learn together Follow Instagram Page For More Inspiring Content 😍 https://www.instagram.com/onehabit7/ Pinterest ; https://www.pinterest.com/onehabit/ Video footage All video footage used is either licensed through either CC-BY or from various stock footage websites. ________________________________________________________________________ #sundarpichai #machinelearning #Ai #artificialintelligence #ceoofgoogle #inspirationaltalk #motivation #short #lifelessons #idea #businessideas #future #technology #technews #onehabit #youtubeshorts ________________________________________________________________________ COPYRIGHT DISCLAIMER Copyright Disclaimer Under Section 107 of the Copyright Act 1976, allowance is made for “fair use” for purposes such as criticism, comment, news reporting, teaching, scholarship, and research. Fair use is a use permitted by copyright statutes that might otherwise be infringing. If you own any of the content in our video and you don’t want it to appear in our channel, please notify us via private message or email. The content will be REMOVED within 24 hours.