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Today’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
🙋♂️ We’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 🔗 Resources used • Notebook created in the workshop: https://jovian.ai/aakashns-6l3/deep-learning-project-live • Guidelines and datasets for deep learning projects: https://jovian.ai/learn/deep-learning-with-pytorch-zero-to-gans/assignment/course-project 💻 In this live hands-on workshop, we’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 “Deep Learning with PyTorch: Zero to GANs” to learn the required skills: http://zerotogans.com Here’s an outline of the workshop: 📄 Find an interesting unstructured dataset online (images, text, audio, etc.) ❓ Identify the type of problem: regression, classification, generative modeling, etc. 🤔 Identify the type of neural network you need: fully connected, convolutional, recurrent, etc. 🛠 Prepare the dataset for training (set up batches, apply augmentations & transforms) 🔃 Define a network architecture and set up a training loop ⚡ Train the model and evaluate its performance using a validation/test set 🧪 Experiment with different network architectures, hyperparameters & regularization techniques 📰 Document and publish your work in a Jupyter notebook or blog post 📒 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 ⚙ Check out these projects for inspiration: • Blindness [More]
Sentiment analysis is an active research field where researchers aim to automatically determine the polarity of text , 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 , and social media analysis . In this talk, we present a new algorithm based on deep learning, which not only outperforms state-of-the-art method  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: • Show where deep learning is being applied in engineering and science, and how it is driving MATLAB’s development. • Demonstrate a workflow for how you can research, develop and deploy your own deep learning application. • 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 – 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]
Check out my follow-up video where I explain how some financial market crashes can be predicted: https://youtu.be/6×4-GcIFDlM
Panellists: • Professor Toby Walsh, Scientia Professor of Artificial Intelligence, UNSW Sydney and Data61 • Kathy Baxter, User Research Architect, Salesforce • 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===== email@example.com Plz like, comment, share, subscribe and don’t forget to press Bell icon for new updates😊
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]
In this talk I will discuss recent trends and developments in deep learning research. I’ll then touch on the significant progress that this research has produced in a number of areas, including computer vision, language understanding, translation, healthcare, and robotics. These advances are driven by both new algorithmic approaches to some of these problems, and by the ability to scale computation for training ever large models on larger datasets. I’ll discuss both of these issues. Finally, one of the reasons for the rapid spread of the ideas and techniques of deep learning has been the availability of open source libraries such as TensorFlow. I’ll give an overview of why these software libraries have an important role in making the benefits of machine learning available throughout the world.
( TensorFlow Training – https://www.edureka.co/ai-deep-learning-with-tensorflow ) This Edureka “Neural Network Tutorial” video (Blog: https://goo.gl/4zxMfU) will help you to understand the basics of Neural Networks and how to use it for deep learning. It explains Single layer and Multi layer Perceptron in detail. Below are the topics covered in this tutorial: 1. Why Neural Networks? 2. Motivation Behind Neural Networks 3. What is Neural Network? 4. Single Layer Percpetron 5. Multi Layer Perceptron 6. Use-Case 7. Applications of Neural Networks Subscribe to our channel to get video updates. Hit the subscribe button above. Check our complete Deep Learning With TensorFlow playlist here: https://goo.gl/cck4hE 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) – – – – – – – – – – – – – – 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 will have to undergo a 2-hour LIVE Practical Exam based on which we will provide you a Grade and a Verifiable Certificate! – – – – – – – – – – – – – – About the Course Edureka’s Deep learning with [More]
Thore Graepel, Research Scientist shares an introduction to machine learning based AI as part of the Advanced Deep Learning & Reinforcement Learning Lectures.
Hello All, In this video we will understand which is the best laptop configuration for Learning Machine Learning and Deep Learning MSI Laptops : https://www.amazon.in/MSI-GL63-9RCX-213IN-i5-9300H-Graphics/dp/B07TNMKVW8/ref=sr_1_3?keywords=msi&qid=1569687428&sr=8-3 Papperspace GPU url: https://www.paperspace.com/ Support me in Patreon: https://www.patreon.com/join/2340909? Connect with me here: Twitter: https://twitter.com/Krishnaik06 Facebook: https://www.facebook.com/krishnaik06 instagram: https://www.instagram.com/krishnaik06 If you like music support my brother’s channel https://www.youtube.com/channel/UCdupFqYIc6VMO-pXVlvmM4Q 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 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 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
Watch Rachel Thomas’s talk, “How to Learn Deep Learning (When You’re Not a Computer Science PhD)” from the free, live online Demystifying Data Science conference hosted by Metis on September 27, 2017. Rachel Thomas has a math Ph.D. from Duke and was selected by Forbes as one of “20 Incredible Women Advancing AI Research”. She is co-founder of fast.ai and a researcher-in-residence at the University of San Francisco Data Institute. Her background includes working as a quant in energy trading, a data scientist + backend engineer at Uber, and a full-stack software instructor at Hackbright.
Scale By the Bay 2019 is held on November 13-15 in sunny Oakland, California, on the shores of Lake Merritt: https://scale.bythebay.io. Join us! —– In this talk, I will describe deep learning algorithms that learn representations for language that are useful for solving a variety of complex language problems. I will focus on 3 tasks: Fine-Grained sentiment analysis; Question answering to win trivia competitions (like Whatson’s Jeopardy system but with one neural network); Multimodal sentence-image embeddings (with a fun demo!) to find images that visualize sentences. I will also show some demos of how deepNLP can be made easy to use with MetaMind.io’s software. Richard Socher is the CTO and founder of MetaMind, a startup that seeks to improve artificial intelligence and make it widely accessible. He obtained his PhD from Stanford working on deep learning with Chris Manning and Andrew Ng. He is interested in developing new AI models that perform well across multiple different tasks in natural language processing and computer vision. He was awarded the 2011 Yahoo! Key Scientific Challenges Award, the Distinguished Application Paper Award at ICML 2011, a Microsoft Research PhD Fellowship in 2012 and a 2013 ‘Magic Grant’ from the Brown Institute for Media Innovation and the 2014 GigaOM Structure Award.
** 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]
Some ASEAN countries may be on the road to economic recovery, but many economists warned that it won’t be smooth. CABA ASEAN Summit 2020 brings a panel of experts to address how technology like AI, deep tech and blockchain to act as a tech enabler for the businesses and governments. The panel led by Anndy Lian has covered the following topics: – How technology like AI, deep tech and blockchain will affect lives? – How AI can help in good data and 5G – How to tackle teething problems such as security for AI? – How can blockchain technology improve on security aspects of things? – Do you really trust AI? – What should investors and people who want to get into the technology industry look at? What is the future? Moderated by: – Anndy Lian, Advisory Board Member of Hyundai DAC Panel members: – Dr Andrew Wu, Founder & Chief Executive Officer, Meshbio Pte Ltd – Sheeram Iyer, Chief Executive Officer & Founder, Prisma Global – Stephen Ho, Group Chief Operating Officer, Skylab Group
Andrew Ng, Adjunct Professor & Kian Katanforoosh, Lecturer – Stanford University https://stanford.io/3eJW8yT Andrew Ng Adjunct Professor, Computer Science Kian Katanforoosh Lecturer, Computer Science To follow along with the course schedule and syllabus, visit: http://cs230.stanford.edu/
Machine learning is everywhere in today’s NLP, but by and large machine learning amounts to numerical optimization of weights for human designed representations and features. The goal of deep learning is to explore how computers can take advantage of data to develop features and representations appropriate for complex interpretation tasks. This tutorial aims to cover the basic motivation, ideas, models and learning algorithms in deep learning for natural language processing. Recently, these methods have been shown to perform very well on various NLP tasks such as language modeling, POS tagging, named entity recognition, sentiment analysis and paraphrase detection, among others. The most attractive quality of these techniques is that they can perform well without any external hand-designed resources or time-intensive feature engineering. Despite these advantages, many researchers in NLP are not familiar with these methods. Our focus is on insight and understanding, using graphical illustrations and simple, intuitive derivations.
One of the main challenges for AI remains unsupervised learning, at which humans are much better than machines, and which we link to another challenge: bringing deep learning to higher-level cognition. We review earlier work on the notion of learning disentangled representations and deep generative models and propose research directions towards learning of high-level abstractions. This follows the ambitious objective of disentangling the underlying causal factors explaining the observed data. We argue that in order to efficiently capture these, a learning agent can acquire information by acting in the world, moving our research from traditional deep generative models of given datasets to that of autonomous learning or unsupervised reinforcement learning. We propose two priors which could be used by an agent acting in its environment in order to help discover such high-level disentangled representations of abstract concepts. The first one is based on the discovery of independently controllable factors, i.e., in jointly learning policies and representations, such that each of these policies can independently control one aspect of the world (a factor of interest) computed by the representation while keeping the other uncontrolled aspects mostly untouched. This idea naturally brings fore the notions of objects (which are controllable), agents (which control objects) and self. The second prior is called the consciousness prior and is based on the hypothesis that our conscious thoughts are low-dimensional objects with a strong predictive or explanatory power (or are very useful for planning). A conscious thought thus selects a few abstract factors (using the attention [More]
How does a group of animals — or cells, for that matter — work together when no one’s in charge? Tiny swarming robots–called Kilobots–work together to tackle tasks in the lab, but what can they teach us about the natural world? ↓ More info, videos, and sources below ↓ DEEP LOOK: a new ultra-HD (4K) short video series created by KQED San Francisco and presented by PBS Digital Studios. See the unseen at the very edge of our visible world. Get a new perspective on our place in the universe and meet extraordinary new friends. Explore big scientific mysteries by going incredibly small. More KQED SCIENCE: Tumblr: http://kqedscience.tumblr.com Twitter: https://www.twitter.com/kqedscience KQED Science: http://ww2.kqed.org/science About Kilobots How do you simultaneously control a thousand robots in a swarm? The question may seem like science fiction, but it’s one that has challenged real robotics engineers for decades. In 2010, the Kilobot entered the scene. Now, engineers are programming these tiny independent robots to cooperate on group tasks. This research could one day lead to robots that can assemble themselves into machines, or provide insights into how swarming behaviors emerge in nature. In the future, this kind of research might lead to collaborative robots that could self-assemble into a composite structure. This larger robot could work in dangerous or contaminated areas, like cleaning up oil spills or conducting search-and-rescue activities. What is Emergent Behavior? The universe tends towards chaos, but sometimes patterns emerge, like a flock of birds in flight. Like termites building skyscrapers [More]
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 Hit the Join button above to sign up to become a member of my channel for access to exclusive content!
Deep learning is a revolutionary technique for discovering patterns from data. We’ll see how this technology works and what it offers us for computer graphics. Attendees learn how to use these tools to power their own creative and practical investigations and applications.
Help fund future projects: https://www.patreon.com/3blue1brown An equally valuable form of support is to simply share some of the videos. Special thanks to these supporters: http://3b1b.co/nn3-thanks This one is a bit more symbol-heavy, and that’s actually the point. The goal here is to represent in somewhat more formal terms the intuition for how backpropagation works in part 3 of the series, hopefully providing some connection between that video and other texts/code that you come across later. For more on backpropagation: http://neuralnetworksanddeeplearning.com/chap2.html https://github.com/mnielsen/neural-networks-and-deep-learning http://colah.github.io/posts/2015-08-Backprop/ Music by Vincent Rubinetti: https://vincerubinetti.bandcamp.com/album/the-music-of-3blue1brown —————— Video timeline 0:00 – Introduction 0:38 – The Chain Rule in networks 3:56 – Computing relevant derivatives 4:45 – What do the derivatives mean? 5:39 – Sensitivity to weights/biases 6:42 – Layers with additional neurons 9:13 – Recap —————— 3blue1brown is a channel about animating math, in all senses of the word animate. And you know the drill with YouTube, if you want to stay posted on new videos, subscribe, and click the bell to receive notifications (if you’re into that): http://3b1b.co/subscribe If you are new to this channel and want to see more, a good place to start is this playlist: http://3b1b.co/recommended Various social media stuffs: Website: https://www.3blue1brown.com Twitter: https://twitter.com/3Blue1Brown Patreon: https://patreon.com/3blue1brown Facebook: https://www.facebook.com/3blue1brown Reddit: https://www.reddit.com/r/3Blue1Brown