This course is a practical introduction to natural language processing with TensorFlow 2.0. In this tutorial you will go from having zero knowledge to writing an artificial intelligence that can compose Shakespearean prose. No prior experience with deep learning is required, though it is always helpful to have more background information. We’ll use a combination of embedding layers, recurrent neural networks, and fully connected layers to perform the classification. ⭐️Course Contents ⭐️ ⌨️ (01:16) Getting Started with Word Embeddings ⌨️ (33:25) How to Perform Sentiment Analysis on Movie Reviews ⌨️ (59:32) Let’s Write An AI That Writes Shakespeare ⭐️Course Description ⭐️ The basic idea behind natural language processing is that we start out with words, i.e. strings of characters, that are almost impossible for the computer to meaningfully parse. We can transform these strings into a vector in a higher dimensional space. Different words will be represented as vectors of different lengths and directions in this space, and this allows us to find relationships between words by finding the component of one vector along another. Don’t worry, the TensorFlow library handles all of this, we just have to have some basic idea of how it works. Since this is a type of supervised learning, we also have labels for our text. This allows the AI to compare the relationships between words to the training labels, and learn which sequences of words represent good and bad movie reviews. This would also work for finding toxic comments, fake product reviews… just about [More]
Intellipaat Artificial Intelligence Course:- https://intellipaat.com/ai-deep-learning-course-with-tensorflow/ Artificial Intelligence Webinar video is an introduction to what is Ai?, what is Deep Learning?, Industries getting disrupted by AI & Deep Learning, Machine Learning vs AI, Robotics, Tensorflow, Career in AI & Future of AI in this Artificial Intelligence Tutorial in detail. Interested to learn Deep Learning & Machine Learning still more? Please check similar Artificial Intelligence Tutorial and other Artificial Intelligence Course Blogs here:- https://goo.gl/rFFw9L Watch complete Artificial Intelligence, Deep Learning & Machine Learning tutorials here:- https://goo.gl/gyf2g3 This Artificial Intelligence Tutorial conference video helps you to learn following topics: 12:48 – What is Ai? 20:18 – Artificial Intelligence history 24:10 – How A.I. Works? 27:17 – What is Deep Learning? 31:37 – Industries getting disrupted by A.I. 35:30 – Applications of Artificial Intelligence 44:55 – Future of AI 53:33 – Job Trends in Artificial Intelligence Are you looking for something more? Enroll in our Artificial Intelligence Course and become a certified A.I. Professional (https://goo.gl/RdA17B). It is a 32 hrs instructor led AI for everyone training provided by Intellipaat which is completely aligned with industry standards and certification bodies. If you’ve enjoyed this Deep Learning, Machine Learning and Robotics tutorial, Like us and Subscribe to our channel for more similar Robotics, Machine Learning vs AI videos and free tutorials. Got any questions about Artificial Intelligence Course & Future of AI? Ask us in the comment section below. —————————- Intellipaat Edge 1. 24*7 Life time Access & Support 2. Flexible Class Schedule 3. Job Assistance [More]
This video contains stepwise implementation for training dataset of “Face Emotion Recognition or Facial Expression Recognition” using Transfer Learning in Tensorflow-Keras API (00:00:00) concepts (00:23:01) installation (00:30:52) implementation (1:15:08) Live Webcame demo
With the Deep Learning scene being dominated by three main frameworks, it is very easy to get confused on which one to use? In this video on Keras vs Tensorflow vs Pytorch, we will clear all your doubts on which framework is better and which framework should be used by beginners, intermediates, and professionals. 🔥Free Deep Learning Course: https://www.simplilearn.com/introduction-to-deep-learning-free-course-skillup?utm_campaign=KerasvsTFvsPytorch&utm_medium=Description&utm_source=youtube The topics covered in this video are : 00:00:00 What is Keras, Tensorflow and Pytorch? 00:05:27 Differences between Keras, TensorFlow and Pytorch 00:11:46 Which framework should you use? Start learning today’s most in-demand skills for FREE. Visit us at https://www.simplilearn.com/skillup-free-online-courses?utm_campaign=ArtificialIntelligence&utm_medium=Description&utm_source=youtube Choose over 300 in-demand skills and get access to 1000+ hours of video content for FREE in various technologies like Data Science, Cybersecurity, Project Management & Leadership, Digital Marketing, and much more. ✅Subscribe to our Channel to learn more about the top Technologies: https://bit.ly/2VT4WtH ⏩ Check out the Deep Learning tutorial videos: https://www.youtube.com/watch?v=6M5VXKLf4D4&list=PLEiEAq2VkUUIYQ-mMRAGilfOKyWKpHSip #KerasvsTensorflowvsPytorch #KerasvsTensorFlow #DeepLearningFrameworks #DeepLearningFrameworksComparision #ArtificialIntelligenceCourse #ArtificialIntelligenceTutorial #ArtificialIntelligenceTutorialForBeginners #Simplilearn Post Graduate Program in AI and Machine Learning: Ranked #1 AI and Machine Learning course by TechGig Fast track your career with our comprehensive Post Graduate Program in AI and Machine Learning, in partnership with Purdue University and in collaboration with IBM. This AI and machine learning certification program will prepare you for one of the world’s most exciting technology frontiers. This Post Graduate Program in AI and Machine Learning covers statistics, Python, machine learning, deep learning networks, NLP, and reinforcement learning. You will build and deploy deep learning models [More]
This talk will focus on creating a production machine learning pipeline using TFX. Using TFX developers can implement machine learning pipelines capable of processing large datasets for both modeling and inference. In addition to data wrangling and feature engineering over large datasets, TFX enables detailed model analysis and versioning. The talk will focus on implementing a TFX pipeline and a discussion of current topics in model understanding. Watch more #io19 here: Machine Learning at Google I/O 2019 Playlist → https://goo.gle/2URpjol TensorFlow at Google I/O 2019 Playlist → http://bit.ly/2GW7ZJM Google I/O 2019 All Sessions Playlist → https://goo.gle/io19allsessions Learn more on the I/O Website → https://google.com/io Subscribe to the TensorFlow Channel → https://bit.ly/TensorFlow1 Get started at → https://www.tensorflow.org/ Speaker(s): Kevin Haas , Tulsee Doshi , Konstantinos Katsiapis T02F52 event: Google I/O 2019; re_ty: Publish; product: TensorFlow – TensorFlow Extended; fullname: Tulsee Doshi;
TensorFlow Dev Summit 2019 is officially a wrap! Machine learning developers from around the world gathered at the Google Event Center in Sunnyvale, California on March 6th and 7th to hear the latest updates from across TensorFlow, Google’s open source machine learning platform for everyone. Those of us who joined in person, and on the livestream (→ https://bit.ly/TensorFlowLive) were able to first-hand witness the official transition to the alpha version of TensorFlow 2.0 The TensorFlow team looks forward to continue bringing you the latest platform updates so you can continue building, training, and deploying your machine learning models with ease. “Recap of the 2019 TensorFlow Dev Summit” blog post → http://bit.ly/2J1pFYW We made our sessions shorter this year 🙂 watch them in this #TFDevSummit ’19 playlist → http://bit.ly/TFDS19Sessions Subscribe to the TensorFlow YouTube channel! → https://bit.ly/TensorFlow1 Event Homepage → https://www.tensorflow.org/dev-summit Event Photo Album → http://bit.ly/TFSummit19 Music by Terra Monk → http://bit.ly/TerraMonkTFDS #PoweredByTF #MachineLearning #Google event: TensorFlow Dev Summit 2019; rs: Livestream; re_ty: Publish; product: TensorFlow – General;
** AI & Deep Learning with Tensorflow Training: https://www.edureka.co/ai-deep-learning-with-tensorflow ** This Edureka video on “Keras vs TensorFlow vs PyTorch” will provide you with a crisp comparison among the top three deep learning frameworks. It provides a detailed and comprehensive knowledge about Keras, TensorFlow and PyTorch and which one to use for what purposes. Following topics will be covered in this video: 1:06 – Introduction to keras, Tensorflow, Pytorch 2:13 – Parameters of Comparison 2:18 – Level of API 3:06 – Speed 3:28 – Architecture 4:03 – Ease of Code 4:27 – Debugging 4:59 – Community Support 5:19 – Datasets 5:37 – Popularity 6:14 – Suitable use cases Subscribe to our channel to get video updates. Hit the subscribe button above https://goo.gl/6ohpTV PG in Artificial Intelligence and Machine Learning with NIT Warangal : https://www.edureka.co/post-graduate/machine-learning-and-ai Post Graduate Certification in Data Science with IIT Guwahati – https://www.edureka.co/post-graduate/data-science-program (450+ Hrs || 9 Months || 20+ Projects & 100+ Case studies) Instagram: https://www.instagram.com/edureka_learning/ Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka Check our complete Deep Learning With TensorFlow playlist here: https://goo.gl/cck4hE #keras #tensorflow #pytorch #deeplearning #machinelearning #frameworks – – – – – – – – – – – – – – How it Works? 1. This is 21 hrs of Online Live Instructor-led course. Weekend class: 7 sessions of 3 hours each. 2. 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. 3. At the end of the training you [More]
Learn how to use TensorFlow 2.0 in this full tutorial course for beginners. This course is designed for Python programmers looking to enhance their knowledge and skills in machine learning and artificial intelligence. Throughout the 8 modules in this course you will learn about fundamental concepts and methods in ML & AI like core learning algorithms, deep learning with neural networks, computer vision with convolutional neural networks, natural language processing with recurrent neural networks, and reinforcement learning. Each of these modules include in-depth explanations and a variety of different coding examples. After completing this course you will have a thorough knowledge of the core techniques in machine learning and AI and have the skills necessary to apply these techniques to your own data-sets and unique problems. ⭐️ Google Colaboratory Notebooks ⭐️ 📕 Module 2: Introduction to TensorFlow – https://colab.research.google.com/drive/1F_EWVKa8rbMXi3_fG0w7AtcscFq7Hi7B#forceEdit=true&sandboxMode=true 📗 Module 3: Core Learning Algorithms – https://colab.research.google.com/drive/15Cyy2H7nT40sGR7TBN5wBvgTd57mVKay#forceEdit=true&sandboxMode=true 📘 Module 4: Neural Networks with TensorFlow – https://colab.research.google.com/drive/1m2cg3D1x3j5vrFc-Cu0gMvc48gWyCOuG#forceEdit=true&sandboxMode=true 📙 Module 5: Deep Computer Vision – https://colab.research.google.com/drive/1ZZXnCjFEOkp_KdNcNabd14yok0BAIuwS#forceEdit=true&sandboxMode=true 📔 Module 6: Natural Language Processing with RNNs – https://colab.research.google.com/drive/1ysEKrw_LE2jMndo1snrZUh5w87LQsCxk#forceEdit=true&sandboxMode=true 📒 Module 7: Reinforcement Learning – https://colab.research.google.com/drive/1IlrlS3bB8t1Gd5Pogol4MIwUxlAjhWOQ#forceEdit=true&sandboxMode=true ⭐️ Course Contents ⭐️ ⌨️ (00:03:25) Module 1: Machine Learning Fundamentals ⌨️ (00:30:08) Module 2: Introduction to TensorFlow ⌨️ (01:00:00) Module 3: Core Learning Algorithms ⌨️ (02:45:39) Module 4: Neural Networks with TensorFlow ⌨️ (03:43:10) Module 5: Deep Computer Vision – Convolutional Neural Networks ⌨️ (04:40:44) Module 6: Natural Language Processing with RNNs ⌨️ (06:08:00) Module 7: Reinforcement Learning with Q-Learning ⌨️ (06:48:24) Module 8: Conclusion and Next Steps [More]
In this video, Yufeng Guo applies deep learning models to local prediction on mobile devices. Yufeng shows you how to use TensorFlow to implement a machine learning model that is tailored to a custom dataset. You will come away knowing enough to get started solving your own deep learning problems. Missed the conference? Watch all the talks here: https://goo.gl/c1Vs3h Watch more talks about Big Data & Machine Learning here: https://goo.gl/OcqI9k
In this video, we will learn about Automatic text generation using Tensorflow, Keras, and LSTM. Automatic text generation is the generation of natural language texts by computer. It has applications in automatic documentation systems, automatic letter writing, automatic report generation, etc. In this project, we are going to generate words given a set of input words. We are going to train the LSTM model using William Shakespeare’s writings. Long Short-Term Memory (LSTM) networks are a modified version of recurrent neural networks, which makes it easier to remember past data in memory. Generally, LSTM is composed of a cell (the memory part of the LSTM unit) and three “regulators”, usually called gates, of the flow of information inside the LSTM unit: an input gate, an output gate and a forget gate. Intuitively, the cell is responsible for keeping track of the dependencies between the elements in the input sequence. The input gate controls the extent to which a new value flows into the cell, the forget gate controls the extent to which a value remains in the cell and the output gate controls the extent to which the value in the cell is used to compute the output activation of the LSTM unit. The activation function of the LSTM gates is often the logistic sigmoid function. There are connections into and out of the LSTM gates, a few of which are recurrent. The weights of these connections, which need to be learned during training, determine how the gates operate. 🔊 Watch [More]
AI Advocate Laurence Moroney sits down with Google Senior Fellow, Jeff Dean following his keynote presentation at TensorFlow World. They discuss how advances in computer vision and language understanding are expanding what’s possible with machine learning, as well as Jeff’s ideas about the future of ML. Watch Jeff Dean’s keynote address at TF World → https://goo.gle/2pcC8jq #TFWorld All Sessions → https://goo.gle/TFWorld19 Watch more episodes of TensorFlow Meets → https://goo.gle/TensorFlow-Meets Subscribe to the TensorFlow channel → https://goo.gle/TensorFlow
We introduce TensorFlow Quantum, an open-source library for the rapid prototyping of novel hybrid quantum-classical ML algorithms. This library will extend the scope of current ML under TensorFlow and provides the necessary toolbox for bringing quantum computing and machine learning research communities together to control and model quantum data. Speaker: Masoud Mohseni – Research Scientist Watch all TensorFlow Dev Summit 2020 sessions → https://goo.gle/TFDS20 Subscribe to the TensorFlow YouTube channel → https://goo.gle/TensorFlow
Monet or Picasso? In this episode, we’ll train our own image classifier, using TensorFlow for Poets. Along the way, I’ll introduce Deep Learning, and add context and background on why the classifier works so well. Here are links to learn more, thanks for watching, and have fun! TensorFlow for Poets Codelab: https://goo.gl/QTwZ3v Google’s Udacity class on Deep Learning: https://goo.gl/iRqXsy TensorFlow tutorial: https://goo.gl/0Oz7B5 Google Research blog on Inception: https://goo.gl/CSrfJ1 You can follow me on Twitter at https://twitter.com/random_forests for updates on episodes, and of course – Google Developers. Subscribe to Google Developers: http://goo.gl/mQyv5L – Subscribe to the brand new Firebase Channel: https://goo.gl/9giPHG And here’s our playlist: https://goo.gl/KewA03
In this video, we’ll make a super simple speech recognizer in 20 lines of Python using the Tensorflow machine learning library. I go over the history of speech recognition research, then explain (and rap about) how we can build our own speech recognition system using the power of deep learning. The code for this video is here: https://github.com/llSourcell/tensorflow_speech_recognition_demo Mick’s winning code: https://github.com/mickvanhulst/tf_chatbot_lotr The weekly challenge can be found at the end of the ‘Make a Game Bot’ video: https://www.youtube.com/watch?v=mGYU5t8MO7s More learning resources: https://www.superlectures.com/iscslp2014/tutorial-4-deep-learning-for-speech-generation-and-synthesis http://andrew.gibiansky.com/blog/machine-learning/speech-recognition-neural-networks/ https://www.youtube.com/watch?v=LFDU2GX4AqM https://www.youtube.com/watch?v=g-sndkf7mCs Please subscribe! And like and comment. That’s what keeps me going. And please support me on Patreon! I don’t work for anyone, although I did make a one-off video for OpenAI because I love them: 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/ 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!
How to process human language in a Recurrent Neural Network (LSTM / GRU) in TensorFlow and Keras. Demonstrated on Sentiment Analysis of the IMDB dataset. https://github.com/Hvass-Labs/TensorFlow-Tutorials
In this episode of TensorFlow Meets, Laurence Moroney sits down to chat with Pete Warden, Tech Lead for TensorFlow on Mobile. They discuss the benefits of taking the framework of TensorFlow and fitting it down for mobile application. Watch to learn more about TensorFlow Lite, TensorFlow on Raspberry Pi, and even something for beginners- TensorFlow for Poets! Subscribe to our channel to get the latest tips and tutorials on our open source machine learning framework built for everyone. TensorFlow Lite → http://bit.ly/2xSBU4C TensorFlow on Raspberry Pi → http://bit.ly/2y1tpV1 TensorFlow for Poets codelab (no coding required) → http://bit.ly/2Hk9zDv TensorFlow Meets playlist → https://goo.gl/DTNXjd Subscribe to the TensorFlow channel here → https://goo.gl/ht3WGe
TensorFlow is a truly open source platform with over 1,900 contributors. On this episode of TensorFlow Meets, Laurence (@lmoroney) talks to Open Source Strategist Edd Wilder-James (@edd) about how things like TensorFlow’s Request for Comments process, Special Interest Groups, and the modularity of its codebase make it easier for the community to build TensorFlow together. They also discuss the upcoming O’Reilly TensorFlow World, which is accepting applications to participate now through April 23rd. TensorFlow community → https://bit.ly/2D14XTB Watch Edd’s talk at TF Dev Summit ‘19 → https://bit.ly/2uTx4zr O’Reilly TensorFlow World 2019 → https://oreil.ly/2OP4Uii Subscribe to the TensorFlow channel → http://bit.ly/TensorFlow1 Watch more episodes of TensorFlow Meets → http://bit.ly/2lbyLDK
Can AI be used to detect various diseases from a simple body scan? Yes! Normally, doctors train for years to do this and the error rate is still relatively high. From mammograms to cat scans, AI can diagnose a disease better than any human can if given the right training dataset. This will drastically reduce patient death, save medical practices a lot of money, and aid doctors in the patient care process. Everyone will win and its important to remember that AI won’t replace doctors, it will become the most powerful tool they’ve ever used. And once enough AI startups start impacting the field of healthcare, it will become as common a tool as the stethoscope has been. Code for this video: https://github.com/llSourcell/AI_in_Medicine_Clinical_Imaging_Classification 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 Curriculum: https://github.com/llSourcell/AI_For_Business_Curriculum More learning resources: https://www.youtube.com/watch?v=3LkbUxqGTfo https://www.youtube.com/watch?v=S4GvBCMfRew https://www.youtube.com/watch?v=LxHHsujnF9c https://www.youtube.com/watch?v=ZPXCF5e1_HI https://www.youtube.com/watch?v=QfNvhPx5Px8&t=202s Join us in the Wizards Slack channel: http://wizards.herokuapp.com/ Sign up for the next course at The School of AI: https://www.theschool.ai https://github.com/gregwchase/dsi-capstone 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 Hit the Join button above to sign up to become a member of my channel for access to exclusive content!
Provides steps for applying Image classification & recognition with easy to follow example. R file: https://goo.gl/fCYm19 Data: https://goo.gl/To15db Machine Learning videos: https://goo.gl/WHHqWP To install EBimage package, you can run following 2 lines; install.packages(“BiocManager”) BiocManager::install(“EBImage”) Uses TensorFlow (by Google) as backend. Includes, – load keras and EBImage packages – read images – explore images and image data – resize and reshape images – one hot encoding – sequential model – compile model – fit model – evaluate model – prediction – confusion matrix Image Classification & Recognition with Keras is an important tool related to analyzing big data or working in data science field. R is a free software environment for statistical computing and graphics, and is widely used by both academia and industry. R software works on both Windows and Mac-OS. It was ranked no. 1 in a KDnuggets poll on top languages for analytics, data mining, and data science. RStudio is a user friendly environment for R that has become popular.
This video goes through an example of using TensorFlow for image recognition. Ubuntu is used via virtualbox on a Windows machine.
In this episode of TensorFlow Meets, Laurence Moroney sits down with Jeff Dean, a Google Senior Fellow working in the area of Machine Intelligence Engineering. Laurence taps into Jeff’s insights about machine learning (ML) and how it’s impacting many different engineering domains and scientific domains in general. Jeff Dean and his team have conducted research on how to use ML to tackle quantum chemistry problems at ~300,000x the speed of traditional methods. From broadening research horizons to decreasing the cost of solar energy and increasing the efficiency of health care systems, ML has massive potential to solve global problems. Subscribe to the channel and stay tuned for more TensorFlow Meets! TF Dev Summit ’18 Keynote w/ Jeff Dean → https://goo.gl/k81f5N TensorFlow Meets Playlist → https://goo.gl/Wy3DSc Subscribe to the TensorFlow channel → https://goo.gl/ht3WGe #TFDevSummit
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).
TensorFlow is an open source software library for numerical computation using data flow graphs. Originally developed by researchers and engineers working on the Google Brain Team within Google’s Machine Intelligence research organization for the purposes of conducting machine learning and deep neural networks research. Learn more at http://tensorflow.org