๐ŸšจLISTEN ON SPOTIFY: ๐ŸšจELECTRONIC MUSIC๐Ÿšจ& ELECTRO DANCE BEATS ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ BEST HOUSE BANGER๐Ÿ”ฅ๐Ÿ”Š๐ŸŒ THIS TRACK IS FIRE!๐Ÿ”ฅ๐Ÿšจ๐Ÿ”ฅ๐Ÿšจ๐Ÿ”ฅ...๐Ÿ˜Ž๐Ÿ‘‰STREAM HERE!!! ๐Ÿšจ๐Ÿš€๐Ÿš€๐Ÿš€๐Ÿš€๐Ÿš€๐Ÿš€โค๐Ÿ‘‹

๐ŸšจBREAKING NEWS ALERT ๐ŸšจThis new search engine is amazing!๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅ BOOM๐Ÿ”ฅ...๐Ÿ˜Ž๐Ÿ‘‰Click here!!! ๐Ÿšจ๐Ÿš€๐Ÿš€๐Ÿš€๐Ÿš€๐Ÿš€๐Ÿš€โค๐Ÿ‘‹
โญ๏ธ Content Description โญ๏ธ In this video, I have explained about speech emotion recognition analysis using python. This is a classification project in deep learning. I have build a LSTM neural network to build a classifier. GitHub Code Repo: http://bit.ly/dlcoderepo Dataset link: https://www.kaggle.com/ejlok1/toronto-emotional-speech-set-tess ๐Ÿ”” Subscribe: http://bit.ly/hackersrealm ๐Ÿ—“๏ธ 1:1 Consultation with Me: https://calendly.com/hackersrealm/consult ๐Ÿ“ท Instagram: https://www.instagram.com/aswintechguy ๐Ÿ”ฃ Linkedin: https://www.linkedin.com/in/aswintechguy ๐ŸŽฏ GitHub: https://github.com/aswintechguy ๐ŸŽฌ Share: https://youtu.be/-VQL8ynOdVg โšก๏ธ Data Structures & Algorithms tutorial playlist: http://bit.ly/dsatutorial ๐Ÿ˜Ž Hackerrank problem solving solutions playlist: http://bit.ly/hackerrankplaylist ๐Ÿค– ML projects tutorial playlist: http://bit.ly/mlprojectsplaylist ๐Ÿ Python tutorial playlist: http://bit.ly/python3playlist ๐Ÿ’ป Machine learning concepts playlist: http://bit.ly/mlconcepts โœ๐Ÿผ NLP concepts playlist: http://bit.ly/nlpconcepts ๐Ÿ•ธ๏ธ Web scraping tutorial playlist: http://bit.ly/webscrapingplaylist Make a small donation to support the channel ๐Ÿ™๐Ÿ™๐Ÿ™:- ๐Ÿ†™ UPI ID: hackersrealm@apl ๐Ÿ’ฒ PayPal: https://paypal.me/hackersrealm ๐Ÿ•’ Timeline 00:00 Introduction to Speech Emotion Recognition 03:51 Import Modules 06:20 Load the Speech Emotion Dataset 12:34 Exploratory Data Analysis 25:20 Feature Extraction using MFCC 38:20 Creating LSTM Model 45:37 Plot the Model Results 49:15 End #speechemotionrecognition #machinelearning #hackersrealm #deeplearning #classification #lstm #datascience #model #project #artificialintelligence #beginner #analysis #python #tutorial #aswin #ai #dataanalytics #data #bigdata #programming #datascientist #technology #coding #datavisualization #computerscience #pythonprogramming #analytics #tech #dataanalysis #iot #programmer #statistics #developer #ml #business #innovation #coder #dataanalyst
For more information about Stanfordโ€™s Artificial Intelligence professional and graduate programs, visit: https://stanford.io/3n7saLk Professor Christopher Manning & PhD Candidate Abigail See, Stanford University http://onlinehub.stanford.edu/ 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
Hello Friends, In this episode we are going to do Emotion Detection using Convolutional Neural Network(CNN). I will do the step by step implementation starting for the dataset download, accessing data set, preprocessing images, designing CNN, training CNN , saving trained model and using that saved model to do the emotion detection on video or live stream. Code link : https://github.com/datamagic2020/Emotion_detection_with_CNN Emotion detection in 5 Lines using pre-trained model -: https://youtu.be/ERXqo_ZEnIo =========== Time Code =========== 00:01 Introduction to Emotion Detection using CNN 01:21 FER 2013 Facial Expression Dataset 04:12 files in emotion detection project 05:52 Image preprocessing using Image Data Generator 08:09 Design/Create Convolution Neural Network for Emotion Detection 10:33 Train out CNN with FER 2013 Dataset / Train CNN for Emotion Detection 11:59 Save the trained model weights and structure 13:08 Test Trained Emotion Detection model 14:15 Load saved model 15:05 Access Video or Camera Feed for testing Emotion Detection model 16:20 Face detection with Haarcascade classifier 18:16 Detect and Highlight each face on video 20:06 Predict Emotion using model 20:21 Display Emotion on video 21:53 Emotion Detection Demo 24:58 emotion detection improvisations Stay tuned and enjoy Machine Learning !!! Cheers !!! #emotiondetection #CNN #DeepLearning Connect with me, โ˜‘๏ธ YouTube : https://www.youtube.com/c/DataMagic2020 โ˜‘๏ธ Facebook : https://www.facebook.com/datamagic2020 โ˜‘๏ธ Instagram : http://instagram.com/datamagic2020 โ˜‘๏ธ Twitter : http://www.twitter.com/datamagic5 โ˜‘๏ธ Telegram: https://t.me/datamagic2020 For Business Inquiries : datamagic2020@gmail.com Best book for Machine Learning : https://amzn.to/3qCe0Rf ๐ŸŽฅ Playlists : โ˜‘๏ธMachine Learning Basics https://www.youtube.com/playlist?list=PLTmQbi1PYZ_E1iTkBrZWK_htO0hY4vcGK โ˜‘๏ธFeature Engineering/ Data Preprocessing https://www.youtube.com/playlist?list=PLTmQbi1PYZ_EnBmO1-E0Z81ArnE-zSR1a โ˜‘๏ธOpenCV Tutorial [Computer Vision] https://www.youtube.com/playlist?list=PLTmQbi1PYZ_GrjMHiGCYa0WyDZfxu-yTz โ˜‘๏ธMachine Learning Algorithms [More]
This video talks about what you need to know when sourcing parts to build your own deep learning machine similar Lambda Labs Workstation. What type of CPU do you need? or What GPU is good for your use case. How much RAM? This video breaks it all down. Outline: 00:01:30 – GPU 00:03:59 – CPU 00:05:38 – RAM 00:06:21 – Motherboard 00:06:55 – Storage 00:07:44 – Power Supply 00:08:19 – Cooling 00:08:51- Case 00:09:15 – Parts Compatibility 00:10:01 – Training on Cloud vs Your Own Machine Reference: https://l7.curtisnorthcutt.com/build-pro-deep-learning-workstation parts list: https://pcpartpicker.com/user/learnedvector/saved/#view=f4c8Jx Audo Studio | Automagically Make Audio Recordings Studio Quality https://www.audostudio.com/ Magic Mic | Join waitlist and get it FREE forever when launched! ๐ŸŽ™๏ธ https://magicmic.ai/ Audo AI | Audio Background Noise Removal Developer API and SDK https://audo.ai/ Lambda Labs https://lambdalabs.com/ Discord Server: Join a community of A.I. Hackers https://discord.gg/9wSTT4F Subscribe to my email newsletter for updated Content. No spam ๐Ÿ™…โ€โ™‚๏ธ only gold ๐Ÿฅ‡. https://bit.ly/320hUdx
Start your Artificial Intelligence and Machine Learning journey by joining โ€œDeep Learning and its applications : Beginners to Advanceโ€ course. The program builds a solid foundation from basics to advance by covering the most popular and widely used deep learning technologies and its applications. Interactive learning: thatโ€™s what we do, and weโ€™d like to share that with you. Come explore your learning journey with us! For any support ,Please write us @ support@infyni.com, we will revert back within 48 Hours
For more information about Stanfordโ€™s Artificial Intelligence professional and graduate programs, visit: https://stanford.io/3Depe55 Professor Christopher Manning, Stanford University http://onlinehub.stanford.edu/ 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
๐Ÿ”ฅEdureka PG Diploma in Artificial Intelligence & ML from E & ICT Academy NIT Warangal(Use Code: YOUTUBE20): https://www.edureka.co/executive-programs/machine-learning-and-ai This Edureka video on ‘Emotion Detection using OpenCV & Python’ will give you an overview of Emotion Detection using OpenCV & Python and will help you understand various important concepts that concern Emotion Detection using OpenCV & Python Following pointers are covered in this Emotion Detection using OpenCV & Python: 00:00:00 Agenda 00:01:54 Introduction to Deep Learning 00:04:14 What is Image Processing? 00:04:58 Libraries used in Project 00:07:30 Steps to execute the Project 00:08:47 Implementation ———————————— Github link for codes: https://github.com/dhruvpandey662/Emotion-detection dataset link: https://www.dropbox.com/s/w3zlhing4dkgeyb/train.zip?dl=0 ———————————— ๐Ÿ”นCheck Edureka’s Deep Learning & TensorFlow Tutorial playlist here: https://goo.gl/cck4hE ๐Ÿ”นCheck Edureka’s Deep Learning & TensorFlow Tutorial Blog Series: http://bit.ly/2sqmP4s ๐Ÿ”ดSubscribe 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/ ———๐„๐๐ฎ๐ซ๐ž๐ค๐š ๐Ž๐ง๐ฅ๐ข๐ง๐ž ๐“๐ซ๐š๐ข๐ง๐ข๐ง๐  ๐š๐ง๐ ๐‚๐ž๐ซ๐ญ๐ข๐Ÿ๐ข๐œ๐š๐ญ๐ข๐จ๐ง——— ๐Ÿ”ต Data Science Online Training: https://bit.ly/2NCT239 ๐ŸŸฃ Python Online Training: https://bit.ly/2CQYGN7 ๐Ÿ”ต AWS Online Training: https://bit.ly/2ZnbW3s ๐ŸŸฃ RPA Online Training: https://bit.ly/2Zd0ac0 ๐Ÿ”ต DevOps Online Training: https://bit.ly/2BPwXf0 ๐ŸŸฃ Big Data Online Training: https://bit.ly/3g8zksu ๐Ÿ”ต Java Online Training: https://bit.ly/31rxJcY ———๐„๐๐ฎ๐ซ๐ž๐ค๐š ๐Œ๐š๐ฌ๐ญ๐ž๐ซ๐ฌ ๐๐ซ๐จ๐ ๐ซ๐š๐ฆ๐ฌ——— ๐ŸŸฃMachine Learning Engineer Masters Program: https://bit.ly/388NXJi ๐Ÿ”ตDevOps Engineer Masters Program: https://bit.ly/2B9tZCp ๐ŸŸฃCloud Architect Masters Program: https://bit.ly/3i9z0eJ ๐Ÿ”ตData Scientist Masters Program: https://bit.ly/2YHaolS ๐ŸŸฃBig Data Architect Masters Program: https://bit.ly/31qrOVv ๐Ÿ”ตBusiness Intelligence Masters Program: https://bit.ly/2BPLtn2 —————–๐„๐๐ฎ๐ซ๐ž๐ค๐š ๐GD ๐‚๐จ๐ฎ๐ซ๐ฌ๐ž๐ฌ————— ๐Ÿ”ตArtificial and Machine Learning PGD: https://bit.ly/2Ziy7b1 #edureka #edurekadeeplearning #deeplearning #EmotionDetectionusingOpenCV&Python #RealTimeEmotionDetection #machinelearningpretrainedmodels #deeplearningtutorial #edurekatraining ——————————————————————– Why Machine Learning & [More]
For more information about Stanfordโ€™s Artificial Intelligence professional and graduate programs, visit: https://stanford.io/3waBO2R Jacob Devlin, Google AI Language https://research.google/people/106320/ Professor Christopher Manning Thomas M. Siebel Professor in Machine Learning, Professor of Linguistics and of Computer Science Director, Stanford Artificial Intelligence Laboratory (SAIL)
For more information about Stanfordโ€™s Artificial Intelligence professional and graduate programs, visit: https://stanford.io/3nd2ZH2 Professor Christopher Manning, Stanford University http://onlinehub.stanford.edu/ 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
In this Python Tutorial we build a simple chatbot using PyTorch and Deep Learning. I will also provide an introduction to some basic Natural Language Processing (NLP) techniques. 1) Theory + NLP concepts (Stemming, Tokenization, bag of words) 2) Create training data 3) PyTorch model and training 4) Save/load model and implement the chat Resource: This tutorial was inspired and adapted from the following article: “Contextual Chatbots with Tensorflow”: https://chatbotsmagazine.com/contextual-chat-bots-with-tensorflow-4391749d0077 ๐Ÿช Code faster with Kite, AI-powered autocomplete that integrates into VS Code: https://www.kite.com/get-kite/?utm_medium=referral&utm_source=youtube&utm_campaign=pythonengineer&utm_content=description-only * โœ… Write cleaner code with Sourcery, instant refactoring suggestions in VS Code & PyCharm: https://sourcery.ai/?utm_source=youtube&utm_campaign=pythonengineer * ๐Ÿ“š Get my FREE NumPy Handbook: https://www.python-engineer.com/numpybook ๐Ÿ““ Notebooks available on Patreon: https://www.patreon.com/patrickloeber โญ Join Our Discord : https://discord.gg/FHMg9tKFSN If you enjoyed this video, please subscribe to the channel! NLTK: https://www.nltk.org You can find the code on GitHub: https://github.com/python-engineer/pytorch-chatbot PyTorch Beginner Course: https://www.youtube.com/playlist?list=PLqnslRFeH2UrcDBWF5mfPGpqQDSta6VK4 Please checkout my website to see all tutorials: https://www.python-engineer.com You can find me here: Twitter: https://twitter.com/python_engineer GitHub: https://github.com/python-engineer Icons: https://fontawesome.com/icons/comments https://fontawesome.com/icons/robot #PyTorch #NLP #DeepLearning ———————————————————————————————————- * This is a sponsored or an affiliate link. By clicking on it you will not have any additional costs, instead you will support me and my project. Thank you so much for the support! ๐Ÿ™
Progress in artificial intelligence (AI), deep learning, and robotics allow new capabilities that will affect military strategies assertively.
In Lecture 15, guest lecturer Song Han discusses algorithms and specialized hardware that can be used to accelerate training and inference of deep learning workloads. We discuss pruning, weight sharing, quantization, and other techniques for accelerating inference, as well as parallelization, mixed precision, and other techniques for accelerating training. We discuss specialized hardware for deep learning such as GPUs, FPGAs, and ASICs, including the Tensor Cores in NVIDIAโ€™s latest Volta GPUs as well as Googleโ€™s Tensor Processing Units (TPUs). Keywords: Hardware, CPU, GPU, ASIC, FPGA, pruning, weight sharing, quantization, low-rank approximations, binary networks, ternary networks, Winograd transformations, EIE, data parallelism, model parallelism, mixed precision, FP16, FP32, model distillation, Dense-Sparse-Dense training, NVIDIA Volta, Tensor Core, Google TPU, Google Cloud TPU Slides: http://cs231n.stanford.edu/slides/2017/cs231n_2017_lecture15.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 โ€œdeep learningโ€) 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. [More]
In Lecture 8 we discuss the use of different software packages for deep learning, focusing on TensorFlow and PyTorch. We also discuss some differences between CPUs and GPUs. Keywords: CPU vs GPU, TensorFlow, Keras, Theano, Torch, PyTorch, Caffe, Caffe2, dynamic vs static computational graphs Slides: http://cs231n.stanford.edu/slides/2017/cs231n_2017_lecture8.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 โ€œdeep learningโ€) 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/
This all began with a doubt… AI vs ML vs DL My doubt was why these words used interchangeably even when they are not the same. I came to a conclusion about this while I was going through AI vs ML vs DL which I’ll be briefing in the last. Hierarchy- DL is subset of ML which is further a subset of AI. DL is the way in which ML is achieved. ML is the way in which AI is achieved. AI- Artificial intelligence is mimicking human intelligence and trying to to make machine as intelligent as human. This can be achieved by again mimicking the process by which by which human becomes intelligent and that is learning so here comes machine-learning which tries to make machine learn and make itself intelligent which is again a part of artificial. So here we came across the term machine learning so machine learning was about described as making machine learn and make itself intelligent but how can we make machine learn? Here again we go for a mimicking human by which he Learns and uses his intelligence. How does human achieve this? He does this by or with the help of his brain how does his brain become capable of doing search extremely wonderful things that is because of the neural network in his brain which is a collection for cluster of millions and billions of neurones forming a network which are interlinked through a synapse. Show the process of making this [More]
github link: https://github.com/krishnaik06/Gender-Recognition-and-Age-Estimator weights: https://drive.google.com/file/d/12Ub2ZUtiYXL1QKUPlAy6oOG4Qhn0GM0H Please donate if you want to support the channel through GPay UPID, Gpay: krishnaik06@okicici Discord Server Link: https://discord.gg/tvAJuuy Telegram link: https://t.me/joinchat/N77M7xRvYUd403DgfE4TWw 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
Todayโ€™s modern-day machine learning data centers require complex computations and fast, efficient data delivery. The NVIDIA Scalable Hierarchical Aggregation and Reduction Protocol (SHARP) takes advantage of the in-network computing capabilities in the NVIDIA Quantum switch, dramatically improving the performance of distributed machine learning workloads. https://developer.nvidia.com/networking/hpc-x #infiniBand #ISC21 #Networking
๐Ÿ™‹โ€โ™‚๏ธ Weโ€™re launching an exclusive part-time career-oriented certification program called the Zero to Data Science Bootcamp with a limited batch of participants. Learn more and enroll here: https://www.jovian.ai/zero-to-data-science-bootcamp ๐Ÿ”— Resources used โ€ข Notebook created in the workshop: https://jovian.ai/aakashns-6l3/deep-learning-project-live โ€ข Guidelines and datasets for deep learning projects: https://jovian.ai/learn/deep-learning-with-pytorch-zero-to-gans/assignment/course-project ๐Ÿ’ป In this live hands-on workshop, weโ€™ll build a deep learning project from scratch in 2.5 – 3 hours. You can follow along to build your own project. Take our Free Certification Course โ€œDeep Learning with PyTorch: Zero to GANsโ€ to learn the required skills: http://zerotogans.com Hereโ€™s an outline of the workshop: ๐Ÿ“„ Find an interesting unstructured dataset online (images, text, audio, etc.) โ“ Identify the type of problem: regression, classification, generative modeling, etc. ๐Ÿค” Identify the type of neural network you need: fully connected, convolutional, recurrent, etc. ๐Ÿ›  Prepare the dataset for training (set up batches, apply augmentations & transforms) ๐Ÿ”ƒ Define a network architecture and set up a training loop โšก Train the model and evaluate its performance using a validation/test set ๐Ÿงช Experiment with different network architectures, hyperparameters & regularization techniques ๐Ÿ“ฐ Document and publish your work in a Jupyter notebook or blog post ๐Ÿ“’ Datasets from the workshop: Chest X-Ray Images (Pneumonia) – https://www.kaggle.com/paultimothymooney/chest-xray-pneumonia Fruits 360 – https://www.kaggle.com/moltean/fruits Flowers Recognition – https://www.kaggle.com/alxmamaev/flowers-recognition Malaria Cell Images Dataset – https://www.kaggle.com/iarunava/cell-images-for-detecting-malaria Intel Image Classification – https://www.kaggle.com/puneet6060/intel-image-classification Best Artworks of All Time – https://www.kaggle.com/ikarus777/best-artworks-of-all-time CelebFaces Attributes (CelebA) Dataset – https://www.kaggle.com/jessicali9530/celeba-dataset Open Datasets – https://github.com/JovianML/opendatasets โš™ Check out these projects for inspiration: โ€ข Blindness [More]
Sentiment analysis is an active research field where researchers aim to automatically determine the polarity of text [1], either as a binary problem or as a multi-class problem where multiple levels of positiveness and negativeness are reported. Recently, there is an increasing interest in going beyond sentiment, and analyzing emotions such as happiness, fear, anger, surprise, sadness and others. Emotion detection has many use cases for both enterprises and consumers. The best-known examples are customer service performance monitoring [2], and social media analysis [3]. In this talk, we present a new algorithm based on deep learning, which not only outperforms state-of-the-art method [4] in emotion detection from text, but also automatically decides on length of emotionally-intensive text blocks in a document. Our talk presents the problem by examples, with business motivations related to the Microsoft Cognitive Services suite. We present a technique to capture both semantic and syntactic relationships in sentences using word embeddings and Long Short-Term Memory (LSTM) based modeling. Our algorithm exploits lexical information of emotions to enrich the data representation. We present empirical results based on ISAER and SemEval-2007 datasets [5,6]. We then motivate the problem of detecting emotionally-intensive text blocks of various sizes, along with an entropy-based technique to solve it by determining the granularity on which the emotions model is applied. We conclude with a live demonstration of the algorithm on diverse types of data: interviews, customer service, and social media.
Deep learning is a key technology driving the current artificial intelligence (AI) megatrend. You may have heard of some mainstream applications of deep learning, but how many of them would you consider applying to your engineering and science applications? MATLAB and Simulink developers have purpose-built the MATLAB deep learning functionality for engineering and science workflows. We understand that success goes beyond just developing a deep learning model. Ultimately, models need to be incorporated into an entire system design workflow to deliver a product or a service to the market. The aim of the session is to provide an overview of how MATLAB enables you to take advantage of disruptive technologies like deep learning. We will: โ€ข Show where deep learning is being applied in engineering and science, and how it is driving MATLABโ€™s development. โ€ข Demonstrate a workflow for how you can research, develop and deploy your own deep learning application. โ€ข Outline what MATLAB and Simulink engineers can do to help support you achieve success with deep learning. Demo files (note: this is a large download at 433 MB): https://www.mathworks.com/content/dam/mathworks/mathworks-dot-com/company/events/post-event-email/3228951-Presentation.zip Check out these other great resources: * See if your school has a MATLAB campus license: https://bit.ly/33hvREb * Get a free product trial: https://bit.ly/2SeH5mA * MATLAB EXPO 2020 On Demand: https://bit.ly/3n8KgKL * Join the Simulink Student Challenge: https://bit.ly/30iLVUb * Learn more about MATLAB: https://bit.ly/3l5xkDR * Learn more about Simulink: https://bit.ly/36lYuSw * See what’s new in MATLAB and Simulink: https://bit.ly/33iHRp0
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 โ€“ What is Ai? 20:18 โ€“ Artificial Intelligence history 24:10 โ€“ How A.I. Works? 27:17 โ€“ What is Deep Learning? 31:37 โ€“ Industries getting disrupted by A.I. 35:30 โ€“ Applications of Artificial Intelligence 44:55 โ€“ Future of AI 53:33 โ€“ 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 youโ€™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]
Check out my follow-up video where I explain how some financial market crashes can be predicted: https://youtu.be/6×4-GcIFDlM
Panellists: โ€ข Professor Toby Walsh, Scientia Professor of Artificial Intelligence, UNSW Sydney and Data61 โ€ข Kathy Baxter, User Research Architect, Salesforce โ€ข Jake Lucchi, Head of Content and AI, Public Policy and Government Relations, Google Asia Pacific We held a major international conference on human rights and technology on 24 July 2018 in Sydney. The conference explored the human rights implications of unprecedented technological change, and launched a major Australian Human Rights Commission project led by Human Rights Commissioner, Edward Santow. For more on the conference, see https://tech.humanrights.gov.au/conference
The aim of the project is about the detection of the emotions elicited by the speaker while talking. As an example, speech produced in a state of fear, anger, or joy becomes loud and fast, with a higher and wider range in pitch, whereas emotions such as sadness or tiredness generate slow and low-pitched speech. Detection of human emotions through voice-pattern and speech-pattern analysis has many applications such as better assisting human-machine interactions. In particular, we are presenting a classification model of emotion elicited by speeches based on deep neural networks (CNNs), SVM, MLP Classification based on acoustic features such as Mel Frequency Cepstral Coefficient (MFCC). The model has been trained to classify eight different emotions (neutral, calm, happy, sad, angry, fearful, disgust, surprise). Our evaluation shows that the proposed approach yields accuracies of 86%, 84%, and 82% using CNN, MLP Classifier and SVM Classifiers, respectively, for 8 emotions using Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS) dataset and Toronto Emotional Speech Set (TESS) Dataset. Read more: https://tinyurl.com/y73zmdu3 For more detail visit our website===== Leadingindia.ai Follow us on Twitter ===== https://twitter.com/LeadingindiaAI?s=08 Flow us on instagram===== https://www.instagram.com/technology_ucan/ Like our Facebook page===== https://www.facebook.com/techucan/ Also subscribe this channel for Technical videos===== https://www.youtube.com/channel/UCdimTrr7ZsKbhI50j3VmJiQ Contact us===== madhushi.verma@bennett.edu.in Plz like, comment, share, subscribe and don’t forget to press Bell icon for new updates๐Ÿ˜Š