We’ve learned how to train different machine learning models and make predictions, but how do we actually choose which model is “best”? We’ll cover the train/test split process for model evaluation, which allows you to avoid “overfitting” by estimating how well a model is likely to perform on new data. We’ll use that same process to locate optimal tuning parameters for a KNN model, and then we’ll re-train our model so that it’s ready to make real predictions. Download the notebook: https://github.com/justmarkham/scikit-learn-videos Quora explanation of overfitting: http://www.quora.com/What-is-an-intuitive-explanation-of-overfitting/answer/Jessica-Su Estimating prediction error: https://www.youtube.com/watch?v=_2ij6eaaSl0&t=2m34s Understanding the Bias-Variance Tradeoff: http://scott.fortmann-roe.com/docs/BiasVariance.html Guiding questions for that article: https://github.com/justmarkham/DAT8/blob/master/homework/09_bias_variance.md Visualizing bias and variance: http://work.caltech.edu/library/081.html WANT TO GET BETTER AT MACHINE LEARNING? HERE ARE YOUR NEXT STEPS: 1) WATCH my scikit-learn video series: https://www.youtube.com/playlist?list=PL5-da3qGB5ICeMbQuqbbCOQWcS6OYBr5A 2) SUBSCRIBE for more videos: https://www.youtube.com/dataschool?sub_confirmation=1 3) JOIN “Data School Insiders” to access bonus content: https://www.patreon.com/dataschool 4) ENROLL in my Machine Learning course: https://www.dataschool.io/learn/ 5) LET’S CONNECT! – Newsletter: https://www.dataschool.io/subscribe/ – Twitter: https://twitter.com/justmarkham – Facebook: https://www.facebook.com/DataScienceSchool/ – LinkedIn: https://www.linkedin.com/in/justmarkham/
🔥Edureka AWS Training: https://www.edureka.co/aws-certification-training This Edureka video on “Deploy an ML Model using Amazon Sagemaker” discusses what is Amazon Sagemaker and how you can build, train and deploy your Machine Learning Models in Amazon Sagemaker. These are the topics covered in the AWS Machine Learning Tutorial video: 00:00:00 Introduction 00:01:14 What is Amazon Sagemaker? 00:04:21 Create your AWS Account 00:06:46 Create your First Notebook Instance 00:17:39 Train your Model on AWS 00:24:37 Deploy your Model on AWS 00:26:33 Evaluate your Model on AWS 00:29:03 AWS SageMaker Case Study: Grammarly 🔹Check Edureka’s complete DevOps playlist here: http://goo.gl/O2vo13 🔹Check Edureka’s Blog playlist here: https://bit.ly/3gfNuZr ——————————————————————————————– 🔴Subscribe to our channel to get video updates. Hit the subscribe button above: https://goo.gl/6ohpTV Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka Instagram: https://www.instagram.com/edureka_learning/ Facebook: https://www.facebook.com/edurekaIN/ SlideShare: https://www.slideshare.net/EdurekaIN Castbox: https://castbox.fm/networks/505?country=in Meetup: https://www.meetup.com/edureka/ #Edureka #DeployAnMlModelUsingAmazonSagemaker #AWSTutorial #AWSCertification #AWSTraining #AWSMachineLearning #AWSMLDeployment #MachineLearningOnCloud #CloudComputing #AWS ——————————————————————————————– How it Works? 1. This is a 5 Week Instructor led Online Course. 2. Course consists of 30 hours of online classes, 30 hours of assignment, 20 hours of project 3. We have a 24×7 One-on-One LIVE Technical Support to help you with any problems you might face or any clarifications you may require during the course. 4. You will get Lifetime Access to the recordings in the LMS. 5. At the end of the training you will have to complete the project based on which we will provide you a Verifiable Certificate! – – – – – – – – – – – – – – About [More]
Confused about understanding machine learning models? Well, this video will help you grab the basics of each one of them. From what they are, to why they are used, and what purpose do they serve. 7 Basic Machine Learning Concepts for Beginners https://www.youtube.com/watch?v=4hlSztfaqoI What is Deep Learning and How it Works | Deep Learning Explained https://www.youtube.com/watch?v=DfRc3CeXLXw Machine Learning Model Deployment Explained https://www.youtube.com/watch?v=SHyFjJ-tIJE What is Neural Network and How it Works | Neural Network Explained https://www.youtube.com/watch?v=Ulx2CuMCyzI After watching this video, you’ll be able to answer, – How many machine learning models are there – Some common machine learning models explained – What is supervised learning – What is unsupervised learning – What is regression – Types of ml models – Common types of regression – Common types of classification – What is classification – What are popular ML models explained – What are the types of supervised learning – What are the types of unsupervised learning – Understanding the basics of machine learning models – Learn machine learning models from scratch – What are common machine learning models for beginners – Understand machine learning models overview – Whats are few ml models basics to grasp Obviously, there is a ton of complexity if you dive into any particular model, but this should give you a fundamental understanding of how each machine learning model works! Read the full blog on https://brandlitic.com/basics-of-machine-learning-models-explained/ Like my content? Be sure to smash that like button and hit Subscribe to get the latest updates! Let’s get social! [More]
Lecture 10 introduces translation, machine translation, and neural machine translation. Google’s new NMT is highlighted followed by sequence models with attention as well as sequence model decoders. ——————————————————————————- Natural Language Processing with Deep Learning Instructors: – Chris Manning – Richard Socher Natural language processing (NLP) deals with the key artificial intelligence technology of understanding complex human language communication. This lecture series provides a thorough introduction to the cutting-edge research in deep learning applied to NLP, an approach that has recently obtained very high performance across many different NLP tasks including question answering and machine translation. It emphasizes how to implement, train, debug, visualize, and design neural network models, covering the main technologies of word vectors, feed-forward models, recurrent neural networks, recursive neural networks, convolutional neural networks, and recent models involving a memory component. For additional learning opportunities please visit: http://stanfordonline.stanford.edu/
So you’ve built / found the perfect Deep Learning model, now how do you put that into production and maintain it? This talk is all about Machine Learning Software Engineering (MLEng) on the Cloud using Java technologies. In particular we will cover : – Machine Learning Quick Primer (Code & Concepts) using TensorFlow for Java, Tribuo, DJL and PyTorch for Java – Use Case Deep Dive : Learn how to take pre-trained ML model and deploy it using micro-services architecture (Micronaut framework) on the Cloud The talk will be hands on with a mix of presentation and live coding. All the code will be available on GitHub after the presentation.
Rick van de Zedde & Pieter de Visser, Wageningen University & Research. At Wageningen University & Research from April 2021 onwards, a digital twin (DT) will be operational. The DT will digitally represent a tomato crop of individual, virtual plants in their local greenhouse environment, and grown simultaneously. The DT will feature real-time updating of plant parameters and environmental variables based on high-tech sensor equipment available in the Netherlands Plant Eco-phenotyping Centre (NPEC) facilities. In the DT, each tomato plant in the crop will be modelled in 3D integrating a set of traits that correspond to model parameters. Thereby, the DT enables us to predict crop response (growth, development and production) to greenhouse and management conditions that affect production efficiency; light intensity and quality, CO2 dosing, nutrient availability and leaf pruning. Thus, the DT can support greenhouse management in real-time. This will be the first-ever 3D simulation model of individual plants growing in greenhouses that get updated by sensor data and that delivers updated predictions as the real plants grow. In that sense, it is a true digital twin, which does not yet exist for plants. This is an important extension of the plant and greenhouse modelling that exists today. As well, the DT allows for hypothesis testing and in silico experiments. As a scientific aim, we will develop and study novel methods on e.g. deep learning for processing of sensor data to transform the raw data to plant traits. Moreover, novel methods will be dealt with on Bayesian inference [More]
TechBites: Digital twin – älyä suunnitteluun 22 May 2019, Tampere University, Hervanta Campus http://techbites.fi/ Professorit Asko Ellman ja Kari Koskinen tutkivat ja kehittävät Tampereen yliopiston tekniikan ja luonnontieteiden tiedekunnassa suunnittelumenetelmiä ja elinkaaren hallintamenetelmiä. Suunnittelutyökalut, kuten XR, SXR, Digital Twin, AI ja ML tuovat uusia mahdollisuuksia koneiden, tuotannon ja rakennetun ympäristön elinkaaren hallintaan. Näissä on nähtävissä isoja mahdollisuuksia suunnittelun tuottavuuden kasvattamiseen.
Digital twins are invaluable when it comes to optimizing processes in the product life cycle of machine tools and implementing business models. https://www.siemens.com/sinumerik-digitaltwin
Hi, everyone. You are very welcome to week two of our NLP course. And this week is about very core NLP tasks. So we are going to speak about language models first, and then about some models that work with sequences of words, for example, part-of-speech tagging or named-entity recognition. All those tasks are building blocks for NLP applications. And they’re very, very useful. So first thing’s first. Let’s start with language models. Imagine you see some beginning of a sentence, like This is the. How would you continue it? Probably, as a human,you know that This is how sounds nice, or This is did sounds not nice. You have some intuition. So how do you know this? Well, you have written books. You have seen some texts. So that’s obvious for you. Can I build similar intuition for computers? Well, we can try. So we can try to estimate probabilities of the next words, given the previous words. But to do this, first of all,we need some data. So let us get some toy corpus. This is a nice toy corpus about the house that Jack built. And let us try to use it to estimate the probability of house, given This is the. So there are four interesting fragments here. And only one of them is exactly what we need. This is the house. So it means that the probability will be one 1 of 4. By c here, I denote the count. So this the count of [More]
ML Systems Workshop @ NIPS 2017 https://nips.cc/Conferences/2017/Schedule?showEvent=8774 Contributed Talk 3: NSML: A Machine Learning Platform That Enables You to Focus on Your Models by Nako Sung. This Video is by Jung-Woo Ha.
Deployment Videos Link :https://www.youtube.com/watch?v=bjsJOl8gz5k&list=PLZoTAELRMXVOAvUbePX1lTdxQR8EY35Z1 Please join as a member in my channel to get additional benefits like materials in Data Science, live streaming for Members and many more https://www.youtube.com/channel/UCNU_lfiiWBdtULKOw6X0Dig/join Please do subscribe my other channel too https://www.youtube.com/channel/UCjWY5hREA6FFYrthD0rZNIw Connect with me here: Twitter: https://twitter.com/Krishnaik06 Facebook: https://www.facebook.com/krishnaik06 instagram: https://www.instagram.com/krishnaik06
Data scientists spend a lot of time on data cleaning and munging, so that they can finally start with the fun part of their job: building models. After you have engineered the features and tested different models, you see how the prediction performance improves. However, the job is not done when you have a high performing model. The deployment of your models is a crucial step in the overall workflow and it is the point in time when your models actually become useful to your company. In this session you will learn about various possibilities and best practices to bring machine learning models into production environments. The goal is not only to make live prediction calls or have the models available as REST API, but also what needs to be considered to maintain them. This talk will focus on solutions with Python (flask, Cloud Foundry, Docker, and more) and the well established ML packages such as Spark MLlib, scikit-learn, and xgboost, but the concepts can be easily transferred to other languages and frameworks. Speaker SUMIT GOYAL Software Engineer IBM
To learn more, please visit: https://aws.amazon.com/sagemaker Amazon SageMaker is a fully-managed platform that enables developers and data scientists to quickly and easily build, train, and deploy machine learning (ML) models at any scale. Amazon SageMaker removes all the barriers that typically slow down developers who want to use machine learning. In this tech talk, we will introduce you to the concepts of Amazon SageMaker including a one-click training environment, highly-optimized machine learning algorithms with built-in model tuning, and deployment of ML models. With zero setup required, Amazon SageMaker significantly decreases your training time and the overall cost of getting ML models from concept to production. Learning Objectives: – Learn the fundamentals of building, training & deploying machine learning models – Learn how Amazon SageMaker provides managed distributed training for machine learning models with a modular architecture – Learn to quickly and easily build, train & deploy machine learning models using Amazon SageMaker
In this video, we will talk about first text classification model on top of features that we have described. And let’s continue with the sentiment classification. We can actually take the IMDB movie reviews dataset, that you can download, it is freely available. It contains 25,000 positive and 25,000 negative reviews. And how did that dataset appear? You can actually look at IMDB website and you can see that people write reviews there, and they actually also provide the number of stars from one star to ten star. They actually rate the movie and write the review. And if you take all those reviews from IMDB website, you can actually use that as a dataset for text classification because you have a text and you have a number of stars, and you can actually think of stars as sentiment. If we have at least seven stars, you can label it as positive sentiment. If it has at most four stars, that means that is a bad movie for a particular person and that is a negative sentiment. And that’s how you get the dataset for sentiment classification for free. It contains at most 30 reviews per movie just to make it less biased for any particular movie. These dataset also provides a 50/50 train test split so that future researchers can use the same split and reproduce their results and enhance the model. For evaluation, you can use accuracy and that actually happens because we have the same number of [More]
github url :https://github.com/krishnaik06/Google-Cloud-Platform-Deployment Please join as a member in my channel to get additional benefits like materials in Data Science, live streaming for Members and many more https://www.youtube.com/channel/UCNU_lfiiWBdtULKOw6X0Dig/join Please do subscribe my other channel too https://www.youtube.com/channel/UCjWY5hREA6FFYrthD0rZNIw Connect with me here: Twitter: https://twitter.com/Krishnaik06 Facebook: https://www.facebook.com/krishnaik06 instagram: https://www.instagram.com/krishnaik06
In Lecture 13 we move beyond supervised learning, and discuss generative modeling as a form of unsupervised learning. We cover the autoregressive PixelRNN and PixelCNN models, traditional and variational autoencoders (VAEs), and generative adversarial networks (GANs). Keywords: Generative models, PixelRNN, PixelCNN, autoencoder, variational autoencoder, VAE, generative adversarial network, GAN Slides: http://cs231n.stanford.edu/slides/2017/cs231n_2017_lecture13.pdf ————————————————————————————– Convolutional Neural Networks for Visual Recognition Instructors: Fei-Fei Li: http://vision.stanford.edu/feifeili/ Justin Johnson: http://cs.stanford.edu/people/jcjohns/ Serena Yeung: http://ai.stanford.edu/~syyeung/ Computer Vision has become ubiquitous in our society, with applications in search, image understanding, apps, mapping, medicine, drones, and self-driving cars. Core to many of these applications are visual recognition tasks such as image classification, localization and detection. Recent developments in neural network (aka “deep learning”) approaches have greatly advanced the performance of these state-of-the-art visual recognition systems. This lecture collection is a deep dive into details of the deep learning architectures with a focus on learning end-to-end models for these tasks, particularly image classification. From this lecture collection, students will learn to implement, train and debug their own neural networks and gain a detailed understanding of cutting-edge research in computer vision. Website: http://cs231n.stanford.edu/ For additional learning opportunities please visit: http://online.stanford.edu/
Hello All, In this video we will be discussing about the differences Between Infrastructure as a Service and Platform as a Service cloud platforms Support me in Patreon: https://www.patreon.com/join/2340909? You can buy my book on Finance with Machine Learning and Deep Learning from the below url amazon url: https://www.amazon.in/Hands-Python-Finance-implementing-strategies/dp/1789346371/ref=as_sl_pc_qf_sp_asin_til?tag=krishnaik06-21&linkCode=w00&linkId=ac229c9a45954acc19c1b2fa2ca96e23&creativeASIN=1789346371 Buy the Best book of Machine Learning, Deep Learning with python sklearn and tensorflow from below amazon url: https://www.amazon.in/Hands-Machine-Learning-Scikit-Learn-Tensor/dp/9352135210/ref=as_sl_pc_qf_sp_asin_til?tag=krishnaik06-21&linkCode=w00&linkId=a706a13cecffd115aef76f33a760e197&creativeASIN=9352135210 Connect with me here: Twitter: https://twitter.com/Krishnaik06 Facebook: https://www.facebook.com/krishnaik06 instagram: https://www.instagram.com/krishnaik06 Subscribe my unboxing Channel https://www.youtube.com/channel/UCjWY5hREA6FFYrthD0rZNIw Below are the various playlist created on ML,Data Science and Deep Learning. Please subscribe and support the channel. Happy Learning! Deep Learning Playlist: https://www.youtube.com/watch?v=DKSZHN7jftI&list=PLZoTAELRMXVPGU70ZGsckrMdr0FteeRUi Data Science Projects playlist: https://www.youtube.com/watch?v=5Txi0nHIe0o&list=PLZoTAELRMXVNUcr7osiU7CCm8hcaqSzGw NLP playlist: https://www.youtube.com/watch?v=6ZVf1jnEKGI&list=PLZoTAELRMXVMdJ5sqbCK2LiM0HhQVWNzm Statistics Playlist: https://www.youtube.com/watch?v=GGZfVeZs_v4&list=PLZoTAELRMXVMhVyr3Ri9IQ-t5QPBtxzJO Feature Engineering playlist: https://www.youtube.com/watch?v=NgoLMsaZ4HU&list=PLZoTAELRMXVPwYGE2PXD3x0bfKnR0cJjN Computer Vision playlist: https://www.youtube.com/watch?v=mT34_yu5pbg&list=PLZoTAELRMXVOIBRx0andphYJ7iakSg3Lk Data Science Interview Question playlist: https://www.youtube.com/watch?v=820Qr4BH0YM&list=PLZoTAELRMXVPkl7oRvzyNnyj1HS4wt2K- You can buy my book on Finance with Machine Learning and Deep Learning from the below url amazon url: https://www.amazon.in/Hands-Python-Finance-implementing-strategies/dp/1789346371/ref=sr_1_1?keywords=krish+naik&qid=1560943725&s=gateway&sr=8-1 🙏🙏🙏🙏🙏🙏🙏🙏 YOU JUST NEED TO DO 3 THINGS to support my channel LIKE SHARE & SUBSCRIBE TO MY YOUTUBE CHANNEL
Let’s discuss whether you should train your models locally or in the cloud. I’ll go through several dedicated GPU options, then compare three cloud options; AWS, Google Cloud, and FloydHub. I was not endorsed by anyone for this. Code for this video: https://github.com/floydhub/fast-style-transfer Please Subscribe! And like. And comment. That’s what keeps me going. High Budget GPU: Titan XP https://www.amazon.com/NVIDIA-GeForce-Pascal-GDDR5X-900-1G611-2500-000/dp/B01JLKP3IS Medium Budget GPU: https://www.amazon.com/MSI-GAMING-GTX-1060-6G/dp/B01IEKYD5U Small Budget GPU: https://www.amazon.com/dp/B01MF7EQJZ Build a Deep Learning machine: https://medium.com/@ncondo/build-a-deep-learning-rig-for-800-4434e21a424f https://medium.com/towards-data-science/building-your-own-deep-learning-box-47b918aea1eb https://www.oreilly.com/learning/build-a-super-fast-deep-learning-machine-for-under-1000 More learning resources: http://www.infoworld.com/article/3179785/cloud-computing/aws-vs-azure-vs-google-cloud-which-free-tier-is-best.html https://thehftguy.com/2016/06/15/gce-vs-aws-in-2016-why-you-should-never-use-amazon/ https://medium.com/@davidmytton/aws-vs-google-cloud-flexibility-vs-operational-simplicity-dca4324b03d4 https://news.ycombinator.com/item?id=13659914 Join us in the Wizards Slack channel: http://wizards.herokuapp.com/ And please support me on Patreon: https://www.patreon.com/user?u=3191693 Follow me: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology Instagram: https://www.instagram.com/sirajraval/ Signup for my newsletter for exciting updates in the field of AI: https://goo.gl/FZzJ5w Hit the Join button above to sign up to become a member of my channel for access to exclusive content!
Watch this presentation to learn how to effectively build and deploy TensorFlow based Deep learning models on the mobile platforms. Sample code: https://github.com/AndreaPisoni EVENT: TensorFlow and Deep Learning Singapore 2017 SPEAKER: Andrea Pisoni PERMISSIONS: The original video was published on Engineers.SG YouTube channel with the Creative Commons Attribution license (reuse allowed).