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We're going to predict the closing price of the S&P 500 using a special type of recurrent neural network called an LSTM network. I'll explain why we use recurrent nets for time series data, and why LSTMs boost our network's memory power. Coding challenge for this video: https://github.com/llSourcell/How-to-Predict-Stock-Prices-Easily-Demo Vishal's winning code: https://github.com/erilyth/DeepLearning-SirajologyChallenges/tree/master/Image_Classifier Jie's runner up code: https://github.com/jiexunsee/Simple-Inception-Transfer-Learning More Learning Resources: http://colah.github.io/posts/2015-08-Understanding-LSTMs/ http://deeplearning.net/tutorial/lstm.html https://deeplearning4j.org/lstm.html https://www.tensorflow.org/tutorials/recurrent http://machinelearningmastery.com/time-series-prediction-lstm-recurrent-neural-networks-python-keras/ https://blog.terminal.com/demistifying-long-short-term-memory-lstm-recurrent-neural-networks/ Please subscribe! And like. And comment. That's what keeps me going. Join other Wizards in our Slack channel: http://wizards.herokuapp.com/ And please support me on Patreon: https://www.patreon.com/user?u=3191693 music in the intro is chambermaid swing by parov stelar Follow me: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology Instagram: https://www.instagram.com/sirajraval/ Instagram: https://www.instagram.com/sirajraval/ Signup for my newsletter for exciting updates in the field of AI: https://goo.gl/FZzJ5w
Do you need to know math to do machine learning? Yes! The big 4 math disciplines that make up machine learning are linear algebra, probability theory, calculus, and statistics. I'm going to cover how each are used by going through a linear regression problem that predicts the price of an apartment in NYC based on its price per square foot. Then we'll switch over to a logistic regression model to change it up a bit. This will be a hands-on way to see how each of these disciplines are used in the field. Code for this video (with coding challenge): https://github.com/llSourcell/math_of_machine_learning Please Subscribe! And like. And comment. That's what keeps me going. Want more education? Connect with me here: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology instagram: https://www.instagram.com/sirajraval Sign up for the next course at The School of AI: http://theschool.ai/ More learning resources: https://towardsdatascience.com/the-mathematics-of-machine-learning-894f046c568 https://ocw.mit.edu/courses/mathematics/18-657-mathematics-of-machine-learning-fall-2015/ https://www.quora.com/How-do-I-learn-mathematics-for-machine-learning https://courses.washington.edu/css490/2012.Winter/lecture_slides/02_math_essentials.pdf Join us in the Wizards Slack channel: http://wizards.herokuapp.com/ And please support me on Patreon: https://www.patreon.com/user?u=3191693 Signup for my newsletter for exciting updates in the field of AI: https://goo.gl/FZzJ5w
First 500 people get 2 months free of Skillshare: https://skl.sh/polymatter11 Patreon: https://patreon.com/polymatter Twitter: https://twitter.com/polymatters Pins & T-Shirts: https://standard.tv/collections/polymatter Reddit: https://reddit.com/r/PolyMatter Discord: https://discord.gg/polymatter China’s housing bubble has left 50 million homes empty and put its government between a rock and a hard place. This includes a paid sponsored promotion which had no part in the writing, editing, or production of the rest of the video. Music by Epidemic Sound: http://epidemicsound.com California Flag image from: Vecteezy.com Provinces map: China with Provinces - Single Color by FreeVectorMaps.com Full list of sources: https://pastebin.com/FPVU9SaG
High-level APIs like tf.keras enable developers to train models easily and effectively. This session will introduce these APIs, and notebooks you can run live in the browser to get started using Colab. We'll walk you through writing your first neural network in TensorFlow using just 10 lines of code with tf.keras, and then we’ll introduce you to Eager execution. We'll close with educational resources you can use to learn more about ML. By releasing easier and more intuitive APIs, we hope to make TensorFlow, an open-source machine learning framework more accessible for all. Rate this session by signing-in on the I/O website here → https://goo.gl/fZTwce Try TensorFlow with zero install → https://goo.gl/NrJAEz Train your first neural network with just 10 lines of code → https://goo.gl/6SRkzf Use the same Keras-compatible API with TensorFlow.js! → https://goo.gl/ZBbzJH Learn more about ML → https://goo.gl/36baeH Follow Josh on Twitter → https://goo.gl/WQ4EFs Follow Laurence on Twitter → https://goo.gl/ZsvMuf
Never thought this day would come where I was writing my own Machine Learning Neural Network Projects... prepare to have SOME FUN! PATREON: https://www.patreon.com/Jabrils SUBSCRIBE FOR MORE SEFD SCIENCE: http://sefdstuff.com/science Table Of Contents ----- 0:00 - Intro 0:10 - My AI Story 1:58 - Starting point 2:16 - Introducing Forrest 2:35 - Discovering Forrest's Problem 3:20 - How the joystick works 3:59 - Exploring our A.I. options 4:47 - Monster Boss Battle Course 4:53 - Recap on whats going on 5:40 - Setting up our inputs 6:30 - Our Neural Network structure & how it works 8:11 - Inputting our Neural Network into Forrest 8:56 - Conclusion Please follow me on social networks: twitter: http://sefdstuff.com/twitter instagram: http://sefdstuff.com/insta reddit: https://www.reddit.com/r/SEFDStuff/ facebook: http://sefdstuff.com/faceb Music ----- Coming soon REMEMBER TO ALWAYS FEED YOUR CURIOSITY #AI #MachineLearning #gamedev
Tensorflow is a popular open source machine learning library released by Google that allows developers to build ML models easily. How are you supposed to build an AI business using Tensorflow? There are so many startups and companies using Tensorflow in production environments with sometimes millions of users relying on the software. In this video, I'm going to explain how all the different tools in the Tensorflow ecosystem (Serving, Lite, Imperative Programming, Visualizations, etc.) work together to allow the developer to use an end to end pipeline for developing and deploying AI software. We'll go over code, theory, and we'll think through two business use cases for applying AI to a problem. Hope you like it!
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