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🔥 Enroll for FREE Artificial Intelligence Course & Get your Completion Certificate: 🤍 This video on "What is Deep Learning" provides a fun and simple introduction to its concepts. We learn about where Deep Learning is implemented and move on to how it is different from machine learning and artificial intelligence. We will also look at what neural networks are and how they are trained to recognize digits written by hand. We further look at some popular applications of Deep Learning. So, let’s dive into the world of Deep Learning with this video. Start learning today's most in-demand skills for FREE. Visit us at 🤍 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. Don't forget to take the quiz at 04:26! To learn more about Deep Learning, subscribe to our YouTube channel: 🤍 Watch more videos on Deep Learning: 🤍 #DeepLearning #WhatIsDeepLearning #DeepLearningTutorial #DeepLearningCourse #DeepLearningExplained #Simplilearn Simplilearn’s Deep Learning course will transform you into an expert in Deep Learning techniques using TensorFlow, the open-source software library designed to conduct machine learning & deep neural network research. With our Deep Learning course, you'll master Deep Learning and TensorFlow concepts, learn to implement algorithms, build artificial neural networks and traverse layers of data abstraction to understand the power of data and prepare you for your new role as Deep Learning scientist. Why Deep Learning? It is one of the most popular software platforms used for Deep Learning and contains powerful tools to help you build and implement artificial neural networks. Advancements in Deep Learning are being seen in smartphone applications, creating efficiencies in the power grid, driving advancements in healthcare, improving agricultural yields, and helping us find solutions to climate change. With this Tensorflow course, you’ll build expertise in Deep Learning models, learn to operate TensorFlow to manage neural networks and interpret the results. According to payscale.com, the median salary for engineers with Deep Learning skills tops $120,000 per year. You can gain in-depth knowledge of Deep Learning by taking our Deep Learning certification training course. With Simplilearn’s Deep Learning course, you will prepare for a career as a Deep Learning engineer as you master concepts and techniques including supervised and unsupervised learning, mathematical and heuristic aspects, and hands-on modeling to develop algorithms. Those who complete the course will be able to: 1. Understand the concepts of TensorFlow, its main functions, operations and the execution pipeline 2. Implement Deep Learning algorithms, understand neural networks and traverse the layers of data abstraction which will empower you to understand data like never before 3. Master and comprehend advanced topics such as convolutional neural networks, recurrent neural networks, training deep networks and high-level interfaces 4. Build Deep Learning models in TensorFlow and interpret the results 5. Understand the language and fundamental concepts of artificial neural networks 6. Troubleshoot and improve Deep Learning models 7. Build your own Deep Learning project 8. Differentiate between machine learning, Deep Learning and artificial intelligence There is booming demand for skilled Deep Learning engineers across a wide range of industries, making this Deep Learning course with TensorFlow training well-suited for professionals at the intermediate to advanced level of experience. We recommend this Deep Learning online course particularly for the following professionals: 1. Software engineers 2. Data scientists 3. Data analysts 4. Statisticians with an interest in Deep Learning Learn more at: 🤍 For more information about Simplilearn’s courses, visit: - Facebook: 🤍 - Twitter: 🤍 - LinkedIn: 🤍 - Website: 🤍 Get the Android app: 🤍 Get the iOS app: 🤍
What are the neurons, why are there layers, and what is the math underlying it? Help fund future projects: 🤍 Written/interactive form of this series: 🤍 Additional funding for this project provided by Amplify Partners Typo correction: At 14 minutes 45 seconds, the last index on the bias vector is n, when it's supposed to in fact be a k. Thanks for the sharp eyes that caught that! For those who want to learn more, I highly recommend the book by Michael Nielsen introducing neural networks and deep learning: 🤍 There are two neat things about this book. First, it's available for free, so consider joining me in making a donation Nielsen's way if you get something out of it. And second, it's centered around walking through some code and data which you can download yourself, and which covers the same example that I introduce in this video. Yay for active learning! 🤍 I also highly recommend Chris Olah's blog: 🤍 For more videos, Welch Labs also has some great series on machine learning: 🤍 🤍 For those of you looking to go *even* deeper, check out the text "Deep Learning" by Goodfellow, Bengio, and Courville. Also, the publication Distill is just utterly beautiful: 🤍 Lion photo by Kevin Pluck - Timeline: 0:00 - Introduction example 1:07 - Series preview 2:42 - What are neurons? 3:35 - Introducing layers 5:31 - Why layers? 8:38 - Edge detection example 11:34 - Counting weights and biases 12:30 - How learning relates 13:26 - Notation and linear algebra 15:17 - Recap 16:27 - Some final words 17:03 - ReLU vs Sigmoid Correction 14:45 - The final index on the bias vector should be "k" Animations largely made using manim, a scrappy open source python library. 🤍 If you want to check it out, I feel compelled to warn you that it's not the most well-documented tool, and has many other quirks you might expect in a library someone wrote with only their own use in mind. Music by Vincent Rubinetti. Download the music on Bandcamp: 🤍 Stream the music on Spotify: 🤍 If you want to contribute translated subtitles or to help review those that have already been made by others and need approval, you can click the gear icon in the video and go to subtitles/cc, then "add subtitles/cc". I really appreciate those who do this, as it helps make the lessons accessible to more people. 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). If you are new to this channel and want to see more, a good place to start is this playlist: 🤍 Various social media stuffs: Website: 🤍 Twitter: 🤍 Patreon: 🤍 Facebook: 🤍 Reddit: 🤍
Learn the fundamental concepts and terminology of Deep Learning, a sub-branch of Machine Learning. This course is designed for absolute beginners with no experience in programming. You will learn the key ideas behind deep learning without any code. You'll learn about Neural Networks, Machine Learning constructs like Supervised, Unsupervised and Reinforcement Learning, the various types of Neural Network architectures, and more. ✏️ Course developed by Jason Dsouza. Check out his YouTube channel: 🤍 ⭐️ Course Contents ⭐️ ⌨️ (0:00) Introduction ⌨️ (1:18) What is Deep Learning ⌨️ (5:25) Introduction to Neural Networks ⌨️ (6:12) How do Neural Networks LEARN? ⌨️ (12:06) Core terminologies used in Deep Learning ⌨️ (12:11) Activation Functions ⌨️ (22:36) Loss Functions ⌨️ (23:42) Optimizers ⌨️ (30:10) Parameters vs Hyperparameters ⌨️ (32:03) Epochs, Batches & Iterations ⌨️ (34:24) Conclusion to Terminologies ⌨️ (35:18) Introduction to Learning ⌨️ (35:34) Supervised Learning ⌨️ (40:21) Unsupervised Learning ⌨️ (43:38) Reinforcement Learning ⌨️ (46:25) Regularization ⌨️ (51:25) Introduction to Neural Network Architectures ⌨️ (51:37) Fully-Connected Feedforward Neural Nets ⌨️ (54:05) Recurrent Neural Nets ⌨️ (1:04:40) Convolutional Neural Nets ⌨️ (1:08:07) Introduction to the 5 Steps to EVERY Deep Learning Model ⌨️ (1:08:23) 1. Gathering Data ⌨️ (1:11:27) 2. Preprocessing the Data ⌨️ (1:19:05) 3. Training your Model ⌨️ (1:19:33) 4. Evaluating your Model ⌨️ (1:19:55) 5. Optimizing your Model's Accuracy ⌨️ (1:25:15) Conclusion to the Course Learn to code for free and get a developer job: 🤍 Read hundreds of articles on programming: 🤍
What is Machine Learning → 🤍 What is Deep Learning → 🤍 Get a unique perspective on what the difference is between Machine Learning and Deep Learning - explained and illustrated in a delicious analogy of ordering pizza by IBMer and Master Inventor, Martin Keen. Download a free AI ebook → 🤍 Get started for free on IBM Cloud → 🤍 Subscribe to see more videos like this in the future → 🤍 #AI #Software #ITModernization #DeepLearning #MachineLearning
An introductory lecture for MIT course 6.S094 on the basics of deep learning including a few key ideas, subfields, and the big picture of why neural networks have inspired and energized an entire new generation of researchers. For more lecture videos on deep learning, reinforcement learning (RL), artificial intelligence (AI & AGI), and podcast conversations, visit our website or follow TensorFlow code tutorials on our GitHub repo. INFO: Website: 🤍 GitHub: 🤍 Slides: 🤍 Playlist: 🤍 Blog post: 🤍 OUTLINE: 0:00 - Introduction 0:53 - Deep learning in one slide 4:55 - History of ideas and tools 9:43 - Simple example in TensorFlow 11:36 - TensorFlow in one slide 13:32 - Deep learning is representation learning 16:02 - Why deep learning (and why not) 22:00 - Challenges for supervised learning 38:27 - Key low-level concepts 46:15 - Higher-level methods 1:06:00 - Toward artificial general intelligence CONNECT: - If you enjoyed this video, please subscribe to this channel. - Twitter: 🤍 - LinkedIn: 🤍 - Facebook: 🤍 - Instagram: 🤍
MIT Introduction to Deep Learning 6.S191: Lecture 1 *New 2022 Edition* Foundations of Deep Learning Lecturer: Alexander Amini For all lectures, slides, and lab materials: 🤍 Lecture Outline 0:00 - Introduction 6:35 - Course information 9:51 - Why deep learning? 12:30 - The perceptron 14:31 - Activation functions 17:03 - Perceptron example 20:25 - From perceptrons to neural networks 26:37 - Applying neural networks 29:18 - Loss functions 31:19 - Training and gradient descent 35:46 - Backpropagation 38:55 - Setting the learning rate 41:37 - Batched gradient descent 43:45 - Regularization: dropout and early stopping 47:58 - Summary Subscribe to stay up to date with new deep learning lectures at MIT, or follow us on 🤍MITDeepLearning on Twitter and Instagram to stay fully-connected!!
🔥 Enroll for FREE Artificial Intelligence Course & Get your Completion Certificate: 🤍 This Deep Learning full course covers all the concepts and techniques that will help you become an expert in Deep Learning. First, you will learn the basics of Deep Learning and understand how to install TensorFlow, what is TensorFlow, TensorFlow Object detection API works, different Deep Learning frameworks, convolutional neural networks, recurrent neural networks in Python, Deep Learning applications and look at the essential interview questions. Dataset link - 🤍 Below topics are explained in this Deep Learning full course video: 1. Deep Learning 2. Working of neural networks 3. Horus Technology 4. What is Deep Learning? 5. Image Recognition 6. Why do we need Deep Learning? 7. Applications of Deep Learning 8. What is a Neural Network? 9. Biological Neuron vs Artificial Neuron 10. Why are Deep Neural Nets hard to train? 11. Neural Network Prediction 12. Top Deep Learning Libraries 13. Why TensorFlow? 14. What is TensorFlow? 15. What are Tensors? 16. What is a Data Flow graph? 17. Program Elements in TensorFlow 18.TensorFlow program basics 19. Use case Implementation using TensorFlow 20.TensorFlow Object Detection 21. COCO Dataset 22.TensorFlow Object Detection API Tutorial 23. Deep Learning Frameworks 24. Keras 25.PyTorch 26. How image recognition works? 27. How does CNN recognizes images? 28. Types of Recurrent Neural Network 29. Working of LSTMs 30. Deep Learning Applications To learn more about Deep Learning, subscribe to our YouTube channel: 🤍 Watch more videos on Deep Learning: 🤍 #DeepLearningFullCourse #DeepLearningUseCases #DeepLearningApplicationsInRealLife #DeepLearning #Datasciencecourse #DataScience #SimplilearnMachineLearning #DeepLearningCourse We've partnered with Purdue University and collaborated with IBM to offer you the unique Post Graduate Program in AI and Machine Learning. Learn more about it here - 🤍 Simplilearn’s Deep Learning course will transform you into an expert in deep learning techniques using TensorFlow, the open-source software library designed to conduct machine learning & deep neural network research. With our deep learning course, you'll master deep learning and TensorFlow concepts, learn to implement algorithms, build artificial neural networks, and traverse layers of data abstraction to understand the power of data and prepare you for your new role as a deep learning scientist. You can gain in-depth knowledge of Deep Learning by taking our Deep Learning certification training course. With Simplilearn’s Deep Learning course, you will prepare for a career as a Deep Learning engineer as you master concepts and techniques including supervised and unsupervised learning, mathematical and heuristic aspects, and hands-on modeling to develop algorithms. Those who complete the course will be able to: 1. Understand the concepts of TensorFlow, its main functions, operations, and the execution pipeline 2. Implement deep learning algorithms, understand neural networks, and traverse the layers of data abstraction which will empower you to understand data like never before 3. Master and comprehend advanced topics such as convolutional neural networks, recurrent neural networks, training deep networks, and high-level interfaces Learn more at: 🤍 For more information about Simplilearn’s courses, visit: - Facebook: 🤍 - Twitter: 🤍 - LinkedIn: 🤍 - Website: 🤍 - Instagram: 🤍 - Telegram Mobile: 🤍 - Telegram Desktop: 🤍 Get the Simplilearn app: 🤍
Google, Facebook & Amazon all use deep learning methods, but how does it work? Research Fellow & Deep Learning Expert Brais Martinez explains. EXTRA BITS from this Video: 🤍 HTML: Poison or Panacea?: Coming Soon! AI's Game Playing Challenge: 🤍 Pong & Object Oriented Programming: 🤍 Botnets: 🤍 🤍 🤍 This video was filmed and edited by Sean Riley. Computer Science at the University of Nottingham: 🤍 Computerphile is a sister project to Brady Haran's Numberphile. More at 🤍
Enjoy these videos? Consider sharing one or two. Help fund future projects: 🤍 Special thanks to these supporters: 🤍 Written/interactive form of this series: 🤍 This video was supported by Amplify Partners. For any early-stage ML startup founders, Amplify Partners would love to hear from you via 3blue1brown🤍amplifypartners.com To learn more, I highly recommend the book by Michael Nielsen 🤍 The book walks through the code behind the example in these videos, which you can find here: 🤍 MNIST database: 🤍 Also check out Chris Olah's blog: 🤍 His post on Neural networks and topology is particular beautiful, but honestly all of the stuff there is great. And if you like that, you'll *love* the publications at distill: 🤍 For more videos, Welch Labs also has some great series on machine learning: 🤍 🤍 "But I've already voraciously consumed Nielsen's, Olah's and Welch's works", I hear you say. Well well, look at you then. That being the case, I might recommend that you continue on with the book "Deep Learning" by Goodfellow, Bengio, and Courville. Thanks to Lisha Li (🤍lishali88) for her contributions at the end, and for letting me pick her brain so much about the material. Here are the articles she referenced at the end: 🤍 🤍 🤍 Music by Vincent Rubinetti: 🤍 - Video timeline 0:00 - Introduction 0:30 - Recap 1:49 - Using training data 3:01 - Cost functions 6:55 - Gradient descent 11:18 - More on gradient vectors 12:19 - Gradient descent recap 13:01 - Analyzing the network 16:37 - Learning more 17:38 - Lisha Li interview 19:58 - Closing thoughts 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). If you are new to this channel and want to see more, a good place to start is this playlist: 🤍 Various social media stuffs: Website: 🤍 Twitter: 🤍 Patreon: 🤍 Facebook: 🤍 Reddit: 🤍
Please get all the materials and pdfs in the below link which is for free. 🤍 Time Stamp: 0:00:00 Introduction 0:03:50 AI vs ML vs DL vs Data Science 0:14:29 Why Deep Learning Is Becoming Popular? 0:22:17 Introduction To Perceptron 0:30:27 Working Of Perceptron With Weights And Bias 1:00:21 Forward Propogation,Backward Propogation And Weight Updateion Formula 1:18:56 Chain Rule Of Derivatives 1:33:43 Vanishing Gradient Problem 1:44:58 Different types Of Activation Functions 2:04:19 Different types Of Loss functions 2:34:39 Different type Of Optimizers 3:31:24 Practical Implementation OF ANN 4:30:48 Black Box Models VsWhite Box Models 4:36:43 Convolutional Neural Network 5:30:50 Practical Implementation Of CNN Join our amazing Programs Currently in iNeuron there are 3 main program that are going on. Full Stack Data science program with job guaranteed which started from May 6th(Price : 15k+ gst)-Lifetime Access 🤍 Full Stack Data Analytics with placement assistance starting from June 18th(Price: 4000rs inr including gst)- Lifetime Access 🤍 Tech Neuron with 210+ courses (price: 7080 including gst for 2 years subscription) 🤍 From my side you can avail additional 10% discount by using coupon code Krish10 Or Sudhanshu10. Don't miss this opportunity grab it before it is too late. Happy Learning!! Connect with me here: Twitter: 🤍 Facebook: 🤍 instagram: 🤍
AI & Deep Learning with TensorFlow (Use Code: YOUTUBE20): 🤍 This Edureka Deep Learning Full Course video will help you understand and learn Deep Learning & Tensorflow in detail. This Deep Learning Tutorial is ideal for both beginners as well as professionals who want to master Deep Learning Algorithms. Below are the topics covered in this Deep Learning tutorial video: 00:00 Introduction 3:11 What is Deep Learning 3:55 Why Artificial Intelligence? 5:48 What is AI? 6:53 Applications of AI 8:43 Machine Learning 10:28 Types of Machine Learning 10:33 Supervised Learning 11:43 Unsupervised Learning 13:08 Reinforcement Learning 14:38 Limitations of Machine Learning 16:08 Deep Learning to the Rescue 19:28 What is Deep Learning? 22:58 Deep Learning Example 24:28 Deep Learning Applications 25:48 Deep Learning Tutorial 27:08 Understanding Deep Learning With an Analogy 29:58 How Deep Learning works? 31:12 Why We need Artificial Neuron? 32:58 Perceptron Learning Algorithm 36:13 Types of Activation Functions 41:33 Single Layer Perceptron Use-case 42:33 What is TensorFlow? 44:18 Tensorflow Code Basics 49:08 TensorFlow Example 59:13 What is a Computational Graph? 1:27:08 Limitations of Single Layer Perceptron 1:28:08 Multilayer Perceptron 1:29:18 How it works? 1:29:23 What is Backpropagation? 1:30:23 Backpropagation Learning Algorithm 1:34:43 Multilayer Perceptron Use-case 1:37:48 Top 8 Deep Learning Frameworks 1:38:18 Chainer 1:39:18 CNTK 1:40:48 Caffe 1:42:28 MXNet 1:43:33 Deeplearning4j 1:45:23 Keras 1:46:58 PyTorch 1:48:23 TensorFlow 1:50:23 TensorFlow Tutorial 1:50:43 Rock or Mine Prediction Use-case 1:52:53 How to Create This Model? 1:54:13 What are Tensors? 1:54:38 Tensor Rank 1:55:58 What is TensorFlow? 2:02:28 Graph Visualization 2:05:10 Constant, Placeholder & Variables 2:08:55 Creating A Model 2:17:06 Reducing The Loss 2:18:31 Batch Gradient Descent 2:22:01 Implementing Rock or Mine Prediction Use-case 2:36:24 Artificial Neural Network Tutorial 2:39:29 Why Neural Network? 2:40:29 Problems Before Neural Network 2:42:09 What is Artificial Neural Network? 2:44:04 How It Works? 2:46:24 Perceptron Learning Algorithm - Beer Analogy 2:52:24 Multilayer Perceptron 2:53:34 Artificial Neutral Network 2:54:24 Training A Neural Network 3:05:54 Applications of Network Networks 3:09:04 Backpropagation & Gradient Descent Tutorial 3:09:49 Perceptron 3:10:44 How does the Network Learn? 3:11:09 MNIST Dataset 3:11:59 Cost Function 3:13:54 Finding Local Minima 3:16:09 Gradient Descent Learning 3:17:19 Back Propagation 3:21:29 Recurrent Neural Networks 3:22:04 Why not Feedforward Network? 3:24:29 What is Recurrent Neural Networks? 3:29:24 Training A Recurrent Neural Network 3:29:49 Vanishing & Exploding Gradient Problem 3:34:09 Long Short Term Memory Networks 3:51:04 Convolutional Neural Network 3:51:29 How A Computer Reads An Image? 3:52:14 Why Not Fully Connected Network? 3:53:29 What Convolutional Neural Network? 3:54:04 How CNN Works? 3:54:39 Convolution Layer 3:59:04 ReLU Layer 4:03:49 Fully Connected Layer 4:11:59 Autoencoders Tutorial 4:13:49 PCA vs Autoencoders 4:15:14 Introduction to Autoencoders 4:17:09 Properties of Autoencoders 4:18:09 Training Autoencoders 4:19:14 Architecture of Autoencoders 4:23:49 Types of Autoencoders 4:25:49 Convolutional Autoencoders 4:26:44 Sparse Autoencoders 4:28:29 Deep Autoencoders 4:30:29 Contractive Autoencoders 4:31:54 Demo 4:35:09 Restricted Boltzmann Machine 4:38:54 Working of RBMs 4:40:29 RBM: Energy-Based Model 4:42:34 RBM: Probabilistic Model 4:42:54 RBM Training 4:44:09 RBM: Training to Prediction 4:44:39 RBM: Example 4:46:29 TensorFlow Object Detection 4:47:34 What is Object Detection? 4:48:24 Object Detection Applications 4:51:04 Workflow of Object Detection 4:52:49 Object Detection in TensorFlow 4:53:59 Object Detection Demo 5:10:44 Creating Chatbots Using Tensorflow 5:12:14 What is Chatbots? 5:12:19 How Does ChatBot Works? 5:14:44 Applications of Chatbot 5:15:54 Layers of Chatbot 5:16:14 Natural Language Processing 5:19:59 Demo 5:21:44 Layers of Chatbot 5:21:59 Deep Learning Interview Questions PG in Artificial Intelligence and Machine Learning with NIT Warangal : 🤍 Post Graduate Certification in Data Science with IIT Guwahati - 🤍 (450+ Hrs || 9 Months || 20+ Projects & 100+ Case studies) Instagram: 🤍 Facebook: 🤍 Twitter: 🤍 LinkedIn: 🤍 For more information, please write back to us at sales🤍edureka.in or call us at IND: 9606058406 / US: 18338555775 (toll-free).
Deep Learning with Convolutional Neural Networks - Dr Mike Pound explains. CNN background: 🤍 Onion Routing (TOR): 🤍 🤍 🤍 This video was filmed and edited by Sean Riley. Computer Science at the University of Nottingham: 🤍 Computerphile is a sister project to Brady Haran's Numberphile. More at 🤍
Learn about the differences between deep learning and machine learning in this MATLAB® Tech Talk. - MATLAB for Deep Learning: 🤍 Walk through several examples, and learn how to decide which method to use. Learn more about Deep Learning: 🤍 Download a trial: 🤍 The video outlines the specific workflow for solving a machine learning problem. The video also outlines the differing requirements for machine learning and deep learning. You’ll learn about the key questions to ask before deciding between machine learning and deep learning. The choice between machine learning or deep learning depends on your data and the problem you’re trying to solve. MATLAB can help you with both of these techniques – either separately or as a combined approach.
This video describes the variety of neural network architectures available to solve various problems in science ad engineering. Examples include convolutional neural networks (CNNs), recurrent neural networks (RNNs), and autoencoders. Book website: 🤍 Steve Brunton's website: eigensteve.com Follow updates on Twitter 🤍eigensteve This video is part of a playlist "Intro to Data Science": 🤍
Neural Networks are one of the most popular Machine Learning algorithms, but they are also one of the most poorly understood. Everyone says Neural Networks are "black boxes", but that's not true at all. In this video I break each piece down and show how it works, step-by-step, using simple mathematics that is still true to the algorithm. By the end of this video you will have a deep understanding of what Neural Networks do. For a complete index of all the StatQuest videos, check out: 🤍 If you'd like to support StatQuest, please consider... Buying my book, The StatQuest Illustrated Guide to Machine Learning: PDF - 🤍 Paperback - 🤍 Kindle eBook - 🤍 Patreon: 🤍 ...or... YouTube Membership: 🤍 ...a cool StatQuest t-shirt or sweatshirt: 🤍 ...buying one or two of my songs (or go large and get a whole album!) 🤍 ...or just donating to StatQuest! 🤍 Lastly, if you want to keep up with me as I research and create new StatQuests, follow me on twitter: 🤍 0:00 Awesome song and introduction 2:01 A simple dataset and problem 3:37 Description of Neural Networks 7:54 Creating a squiggle from curved lines 15:25 Using the Neural Network to make a prediction 16:38 Some more Neural Network terminology #StatQuest #NeuralNetworks
Cette formation sur le Deep Learning vous apprendra à développer des réseaux de neurones artificiels, en voyant tous les détails mathématiques qui se cachent derrière l'intelligence artificielle. Nous apprendrons à développer des modeles de Deep Learning avec Numpy, mais aussi avec Keras et Tensorflow. Nous verrons en détails le modele du perceptron, les fonctions d'activations, la backpropagation, et nous réaliserons des projets de Deep Learning tels que le célebre projet MNIST. 00:00 : Introduction MNIST 01:57 : Les Bases du Machine Learning et du Deep Learning 05:19 : Le Neurone Artificiel de McCulloch et Pitts 10:47 : Le Perceptron de Frank Rosenblatt 15:13 : Le Perceptron Multicouches de Geoffrey Hinton 20:47 : Le Deep Learning Moderne 25:14 : Programme de cette Formation 28:06 : Informations Importantes et Conclusion ► MON SITE INTERNET EN COMPLÉMENT DE CETTE VIDÉO: 🤍 ► REJOINS NOTRE COMMUNAUTÉ DISCORD 🤍 ► Me soutenir financierement sur Tipeee ou Utip (et obtenir des vidéos BONUS) 🤍 🤍 ► Recevez gratuitement mon Livre: APPRENDRE LE MACHINE LEARNING EN UNE SEMAINE CLIQUEZ ICI: 🤍 ► Télécharger gratuitement mes codes sur github: 🤍 ► Abonnez-vous : 🤍 ► Pour En Savoir plus : Visitez Machine Learnia : 🤍 ► Qui suis-je ? Je m’appelle Guillaume Saint-Cirgue et je suis Data Scientist au Royaume Uni. Après avoir suivi un parcours classique maths sup maths spé et avoir intégré une bonne école d’ingénieur, je me suis tourné vers l’intelligence artificielle de ma propre initiative et j’ai commencé à apprendre tout seul le machine learning et le deep learning en suivant des formations payantes, en lisant des articles scientifiques, en suivant les cours du MIT et de Stanford et en passant des week end entier à développer mes propres codes. Aujourd’hui, je veux vous offrir ce que j’ai appris gratuitement car le monde a urgemment besoin de se former en Intelligence Artificielle. Que vous souhaitiez changer de vie, de carrière, ou bien développer vos compétences à résoudre des problèmes, ma chaîne vous y aidera. C’est votre tour de passer à l’action ! ► Une question ? Contactez-moi: contact🤍machinelearnia.com
What's actually happening to a neural network as it learns? Help fund future projects: 🤍 An equally valuable form of support is to simply share some of the videos. Special thanks to these supporters: 🤍 Written/interactive form of this series: 🤍 And by CrowdFlower: 🤍 Home page: 🤍 The following video is sort of an appendix to this one. The main goal with the follow-on video is to show the connection between the visual walkthrough here, and the representation of these "nudges" in terms of partial derivatives that you will find when reading about backpropagation in other resources, like Michael Nielsen's book or Chis Olah's blog. Video timeline: 0:00 - Introduction 0:23 - Recap 3:07 - Intuitive walkthrough example 9:33 - Stochastic gradient descent 12:28 - Final words
WIRED has challenged computer scientist and Hidden Door cofounder and CEO Hilary Mason to explain machine learning to 5 different people; a child, teen, a college student, a grad student and an expert. Still haven’t subscribed to WIRED on YouTube? ►► 🤍 Listen to the Get WIRED podcast ►► 🤍 Want more WIRED? Get the magazine ►► 🤍 Get more incredible stories on science and tech with our daily newsletter: 🤍 Also, check out the free WIRED channel on Roku, Apple TV, Amazon Fire TV, and Android TV. Here you can find your favorite WIRED shows and new episodes of our latest hit series Tradecraft. ABOUT WIRED WIRED is where tomorrow is realized. Through thought-provoking stories and videos, WIRED explores the future of business, innovation, and culture. Computer Scientist Explains Machine Learning in 5 Levels of Difficulty | WIRED
Ready to learn the fundamentals of TensorFlow and deep learning with Python? Well, you’ve come to the right place. After this two-part code-first introduction, you’ll have written 100s of lines of TensorFlow code and have hands-on experience with two important problems in machine learning: regression (predicting a number) and classification (predicting if something is one thing or another). Open a Google Colab (if you’re not sure what this is, you’ll find out soon) window and get ready to code along. Sign up for the full course - 🤍 Get all of the code/materials on GitHub - 🤍 Ask a question - 🤍 See part 2 - 🤍 TensorFlow Python documentation - 🤍 Connect elsewhere: Web - 🤍 Livestreams on Twitch - 🤍 Get email updates on my work - 🤍 Timestamps: 0:00 - Intro/hello/how to approach this video 1:50 - MODULE 0 START (TensorFlow/deep learning fundamentals) 1:53 - [Keynote] 1. What is deep learning? 6:31 - [Keynote] 2. Why use deep learning? 16:10 - [Keynote] 3. What are neural networks? 26:33 - [Keynote] 4. What is deep learning actually used for? 35:10 - [Keynote] 5. What is and why use TensorFlow? 43:05 - [Keynote] 6. What is a tensor? 46:40 - [Keynote] 7. What we're going to cover 51:12 - [Keynote] 8. How to approach this course 56:45 - 9. Creating our first tensors with TensorFlow 1:15:32 - 10. Creating tensors with tf Variable 1:22:40 - 11. Creating random tensors 1:32:20 - 12. Shuffling the order of tensors 1:42:00 - 13. Creating tensors from NumPy arrays 1:53:57 - 14. Getting information from our tensors 2:05:52 - 15. Indexing and expanding tensors 2:18:27 - 16. Manipulating tensors with basic operations 2:24:00 - 17. Matrix multiplication part 1 2:35:55 - 18. Matrix multiplication part 2 2:49:25 - 19. Matrix multiplication part 3 2:59:27 - 20. Changing the datatype of tensors 3:06:24 - 21. Aggregating tensors 3:16:14 - 22. Tensor troubleshooting 3:22:27 - 23. Find the positional min and max of a tensor 3:31:56 - 24. Squeezing a tensor 3:34:57 - 25. One-hot encoding tensors 3:40:44 - 26. Trying out more tensor math operations 3:45:31 - 27. Using TensorFlow with NumPy 3:51:14 - MODULE 1 START (neural network regression) 3:51:25 - [Keynote] 28. Intro to neural network regression with TensorFlow 3:58:57 - [Keynote] 29. Inputs and outputs of a regression model 4:07:55 - [Keynote] 30. Architecture of a neural network regression model 4:15:51 - 31. Creating sample regression data 4:28:39 - 32. Steps in modelling with TensorFlow 4:48:53 - 33. Steps in improving a model part 1 4:54:56 - 34. Steps in improving a model part 2 5:04:22 - 35. Steps in improving a model part 3 5:16:55 - 36. Evaluating a model part 1 ("visualize, visualize, visualize") 5:24:20 - 37. Evaluating a model part 2 (the 3 datasets) 5:35:22 - 38. Evaluating a model part 3 (model summary) 5:52:39 - 39. Evaluating a model part 4 (visualizing layers) 5:59:56 - 40. Evaluating a model part 5 (visualizing predictions) 6:09:11 - 41. Evaluating a model part 6 (regression evaluation metrics) 6:17:19 - 42. Evaluating a regression model part 7 (MAE) 6:23:10 - 43. Evaluating a regression model part 8 (MSE) 6:26:29 - 44. Modelling experiments part 1 (start with a simple model) 6:40:19 - 45. Modelling experiments part 2 (increasing complexity) 6:51:49 - 46. Comparing and tracking experiments 7:02:08 - 47. Saving a model 7:11:32 - 48. Loading a saved model 7:21:49 - 49. Saving and downloading files from Google Colab 7:28:07 - 50. Putting together what we've learned 1 (preparing a dataset) 7:41:38 - 51. Putting together what we've learned 2 (building a regression model) 7:55:01 - 52. Putting together what we've learned 3 (improving our regression model) 8:10:45 - [Code] 53. Preprocessing data 1 (concepts) 8:20:21 - [Code] 54. Preprocessing data 2 (normalizing data) 8:31:17 - [Code] 55. Preprocessing data 3 (fitting a model on normalized data) 8:38:57 - MODULE 2 START (neural network classification) 8:39:07 - [Keynote] 56. Introduction to neural network classification with TensorFlow 8:47:31 - [Keynote] 57. Classification inputs and outputs 8:54:08 - [Keynote] 58. Classification input and output tensor shapes 9:00:31 - [Keynote] 59. Typical architecture of a classification model 9:10:08 - 60. Creating and viewing classification data to model 9:21:39 - 61. Checking the input and output shapes of our classification data 9:26:17 - 62. Building a not very good classification model 9:38:28 - 63. Trying to improve our not very good classification model 9:47:42 - 64. Creating a function to visualize our model's not so good predictions 10:02:50 - 65. Making our poor classification model work for a regression dataset #tensorflow #deeplearning #machinelearning
This course will teach you how to use Keras, a neural network API written in Python and integrated with TensorFlow. We will learn how to prepare and process data for artificial neural networks, build and train artificial neural networks from scratch, build and train convolutional neural networks (CNNs), implement fine-tuning and transfer learning, and more! ⭐️🦎 COURSE CONTENTS 🦎⭐️ ⌨️ (00:00:00) Welcome to this course ⌨️ (00:00:16) Keras Course Introduction ⌨️ (00:00:50) Course Prerequisites ⌨️ (00:01:33) DEEPLIZARD Deep Learning Path ⌨️ (00:01:45) Course Resources ⌨️ (00:02:30) About Keras ⌨️ (00:06:41) Keras with TensorFlow - Data Processing for Neural Network Training ⌨️ (00:18:39) Create an Artificial Neural Network with TensorFlow's Keras API ⌨️ (00:24:36) Train an Artificial Neural Network with TensorFlow's Keras API ⌨️ (00:30:07) Build a Validation Set With TensorFlow's Keras API ⌨️ (00:39:28) Neural Network Predictions with TensorFlow's Keras API ⌨️ (00:47:48) Create a Confusion Matrix for Neural Network Predictions ⌨️ (00:52:29) Save and Load a Model with TensorFlow's Keras API ⌨️ (01:01:25) Image Preparation for CNNs with TensorFlow's Keras API ⌨️ (01:19:22) Build and Train a CNN with TensorFlow's Keras API ⌨️ (01:28:42) CNN Predictions with TensorFlow's Keras API ⌨️ (01:37:05) Build a Fine-Tuned Neural Network with TensorFlow's Keras API ⌨️ (01:48:19) Train a Fine-Tuned Neural Network with TensorFlow's Keras API ⌨️ (01:52:39) Predict with a Fine-Tuned Neural Network with TensorFlow's Keras API ⌨️ (01:57:50) MobileNet Image Classification with TensorFlow's Keras API ⌨️ (02:11:18) Process Images for Fine-Tuned MobileNet with TensorFlow's Keras API ⌨️ (02:24:24) Fine-Tuning MobileNet on Custom Data Set with TensorFlow's Keras API ⌨️ (02:38:59) Data Augmentation with TensorFlow' Keras API ⌨️ (02:47:24) Collective Intelligence and the DEEPLIZARD HIVEMIND ⭐️🦎 DEEPLIZARD COMMUNITY RESOURCES 🦎⭐️ 👉 Check out the blog post and other resources for this course: 🔗 🤍 💻 DOWNLOAD ACCESS TO CODE FILES 🤖 Available for members of the deeplizard hivemind: 🔗 🤍 🧠 Support collective intelligence, join the deeplizard hivemind: 🔗 🤍 👋 Hey, we're Chris and Mandy, the creators of deeplizard! 👀 CHECK OUT OUR VLOG: 🔗 🤍 👀 Follow deeplizard: YouTube: 🤍 Our vlog: 🤍 Facebook: 🤍 Instagram: 🤍 Twitter: 🤍 Patreon: 🤍 🎵 deeplizard uses music by Kevin MacLeod 🔗 🤍 🔗 🤍 ❤️ Please use the knowledge gained from deeplizard content for good, not evil. Learn to code for free and get a developer job: 🤍 Read hundreds of articles on programming: 🤍
Welcome to the most beginner-friendly place on the internet to learn PyTorch for deep learning. All code on GitHub - 🤍 Ask a question - 🤍 Read the course materials online - 🤍 Sign up for the full course on Zero to Mastery (20+ hours more video) - 🤍 Below are the timestamps/outline of the video. The video you're watching is comprised of 162 smaller videos but YouTube limits timestamps at 100 so some have been left out. 00:00 Hello :) 🛠 Chapter 0: PyTorch Fundamentals 01:17 0. Welcome and "what is deep learning?" 07:13 1. Why use machine/deep learning? 10:47 2. The number one rule of ML 16:27 3. Machine learning vs deep learning 22:34 4. Anatomy of neural networks 31:56 5. Different learning paradigms 36:28 6. What can deep learning be used for? 42:50 7. What is/why PyTorch? 53:05 8. What are tensors? 57:24 9. Outline 1:03:28 10. How to (and how not to) approach this course 1:08:37 11. Important resources 1:14:00 12. Getting setup 1:21:40 13. Introduction to tensors 1:35:07 14. Creating tensors 1:53:33 17. Tensor datatypes 2:02:58 18. Tensor attributes (information about tensors) 2:11:22 19. Manipulating tensors 2:17:22 20. Matrix multiplication 2:47:50 23. Finding the min, max, mean and sum 2:57:20 25. Reshaping, viewing and stacking 3:11:03 26. Squeezing, unsqueezing and permuting 3:23:00 27. Selecting data (indexing) 3:32:33 28. PyTorch and NumPy 3:41:42 29. Reproducibility 3:52:30 30. Accessing a GPU 4:04:21 31. Setting up device agnostic code 🗺 Chapter 1: PyTorch Workflow 4:16:59 33. Introduction to PyTorch Workflow 4:19:46 34. Getting setup 4:27:02 35. Creating a dataset with linear regression 4:36:44 36. Creating training and test sets (the most important concept in ML) 4:52:50 38. Creating our first PyTorch model 5:13:13 40. Discussing important model building classes 5:19:41 41. Checking out the internals of our model 5:29:33 42. Making predictions with our model 5:40:47 43. Training a model with PyTorch (intuition building) 5:49:03 44. Setting up a loss function and optimizer 6:01:56 45. PyTorch training loop intuition 6:39:37 48. Running our training loop epoch by epoch 6:49:03 49. Writing testing loop code 7:15:25 51. Saving/loading a model 7:44:00 54. Putting everything together 🤨 Chapter 2: Neural Network Classification 8:31:32 60. Introduction to machine learning classification 8:41:14 61. Classification input and outputs 8:50:22 62. Architecture of a classification neural network 9:09:13 64. Turing our data into tensors 9:25:30 66. Coding a neural network for classification data 9:43:27 68. Using torch.nn.Sequential 9:56:45 69. Loss, optimizer and evaluation functions for classification 10:11:37 70. From model logits to prediction probabilities to prediction labels 10:27:45 71. Train and test loops 10:57:27 73. Discussing options to improve a model 11:27:24 76. Creating a straight line dataset 11:45:34 78. Evaluating our model's predictions 11:50:58 79. The missing piece: non-linearity 12:42:04 84. Putting it all together with a multiclass problem 13:23:41 88. Troubleshooting a mutli-class model 😎 Chapter 3: Computer Vision 14:00:20 92. Introduction to computer vision 14:12:08 93. Computer vision input and outputs 14:22:18 94. What is a convolutional neural network? 14:27:21 95. TorchVision 14:36:42 96. Getting a computer vision dataset 15:01:06 98. Mini-batches 15:08:24 99. Creating DataLoaders 15:51:33 103. Training and testing loops for batched data 16:25:59 105. Running experiments on the GPU 16:29:46 106. Creating a model with non-linear functions 16:41:55 108. Creating a train/test loop 17:13:04 112. Convolutional neural networks (overview) 17:21:29 113. Coding a CNN 17:41:18 114. Breaking down nn.Conv2d/nn.MaxPool2d 18:28:34 118. Training our first CNN 18:43:54 120. Making predictions on random test samples 18:55:33 121. Plotting our best model predictions 19:19:06 123. Evaluating model predictions with a confusion matrix 🗃 Chapter 4: Custom Datasets 19:43:37 126. Introduction to custom datasets 19:59:26 128. Downloading a custom dataset of pizza, steak and sushi images 20:13:31 129. Becoming one with the data 20:38:43 132. Turning images into tensors 21:15:48 136. Creating image DataLoaders 21:24:52 137. Creating a custom dataset class (overview) 21:42:01 139. Writing a custom dataset class from scratch 22:21:22 142. Turning custom datasets into DataLoaders 22:28:22 143. Data augmentation 22:42:46 144. Building a baseline model 23:10:39 147. Getting a summary of our model with torchinfo 23:17:18 148. Creating training and testing loop functions 23:50:31 151. Plotting model 0 loss curves 23:59:34 152. Overfitting and underfitting 24:32:03 155. Plotting model 1 loss curves 24:35:25 156. Plotting all the loss curves 24:46:22 157. Predicting on custom data #pytorch #machinelearning #deeplearning
The role of deep learning in the development of artificial intelligence. This talk was given at a TEDx event using the TED conference format but independently organized by a local community. Learn more at 🤍
You can learn more about CuriosityStream at 🤍 Today, we're going to combine the artificial neuron we created last week into an artificial neural network. Artificial neural networks are better than other methods for more complicated tasks like image recognition, and the key to their success is their hidden layers. We'll talk about how the math of these networks work and how using many hidden layers allows us to do deep learning. Neural networks are really powerful at finding patterns in data which is why they've become one of the most dominant machine learning technologies used today. Crash Course is on Patreon! You can support us directly by signing up at 🤍 Thanks to the following patrons for their generous monthly contributions that help keep Crash Course free for everyone forever: Eric Prestemon, Sam Buck, Mark Brouwer, Timothy J Kwist, Brian Thomas Gossett, Haxiang N/A Liu, Jonathan Zbikowski, Siobhan Sabino, Zach Van Stanley, Bob Doye, Jennifer Killen, Nathan Catchings, Brandon Westmoreland, dorsey, Indika Siriwardena, Kenneth F Penttinen, Trevin Beattie, Erika & Alexa Saur, Justin Zingsheim, Jessica Wode, Tom Trval, Jason Saslow, Nathan Taylor, Khaled El Shalakany, SR Foxley, Sam Ferguson, Yasenia Cruz, Eric Koslow, Caleb Weeks, Tim Curwick, David Noe, Shawn Arnold, William McGraw, Andrei Krishkevich, Rachel Bright, Jirat, Ian Dundore Want to find Crash Course elsewhere on the internet? Facebook - 🤍 Twitter - 🤍 Tumblr - 🤍 Support Crash Course on Patreon: 🤍 CC Kids: 🤍 #CrashCourse #ArtificialIntelligence #MachineLearning
A small 2D simulation in which cars learn to maneuver through a course by themselves, using a neural network and evolutionary algorithms. Also check out my other project "AI Learns to Park": 🤍 Two AI fight for the same Parking Spot: 🤍 Interested in how Neural Networks work? Have a look at my one-minute-explanation: 🤍 This simulation was implemented in Unity. You can find detailed information about how this simulation works, as well as a link to the entire source code on my website: 🤍 Don't miss any future videos, by subscribing to my channel. Follow me on Twitter: 🤍 #MachineLearning #Evolution #GeneticAlgorithm
Machine Learning is the process of teaching a computer how perform a task with out explicitly programming it. The process feeds algorithms with large amounts of data to gradually improve predictive performance. #ai #python #100SecondsOfCode 🔗 Resources Machine Learning Tutorials 🤍 What is ML 🤍 Neural Networks 🤍 ML Wiki 🤍 🔥 Watch more with Fireship PRO Upgrade to Fireship PRO at 🤍 Use code lORhwXd2 for 25% off your first payment. 🎨 My Editor Settings - Atom One Dark - vscode-icons - Fira Code Font Topics Covered - Convolutional Neural Networks - Machine Learning Basics - How Data Science Works - Big Data and Feature Engineering - Artificial Intelligence History - Supervised Machine Learning
MIT Introduction to Deep Learning 6.S191: Lecture 1 Foundations of Deep Learning Lecturer: Alexander Amini January 2020 For all lectures, slides, and lab materials: 🤍 Lecture Outline 0:00 - Introduction 4:14 - Course information 8:10 - Why deep learning? 11:01 - The perceptron 13:07 - Activation functions 15:32 - Perceptron example 18:54 - From perceptrons to neural networks 25:23 - Applying neural networks 28:16 - Loss functions 31:14 - Training and gradient descent 35:13 - Backpropagation 39:25 - Setting the learning rate 43:43 - Batched gradient descent 46:46 - Regularization: dropout and early stopping 51:58 - Summary Subscribe to 🤍stay up to date with new deep learning lectures at MIT, or follow us on 🤍MITDeepLearning on Twitter and Instagram to stay fully-connected!!
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( Tensorflow Training - 🤍 ) This Edureka "What is Deep Learning" video (Blog: 🤍 will help you to understand about the relationship between Deep Learning, Machine Learning and Artificial Intelligence and how Deep Learning came into the picture. This tutorial will be discussing about Artificial Intelligence, Machine Learning and its limitations, how Deep Learning overcame Machine Learning limitations and different real-life applications of Deep Learning. Below are the topics covered in this tutorial: 1. What Is Artificial Intelligence? 2. What Is Machine Learning? 3. Limitations Of Machine Learning 4. Deep Learning To The Rescue 5. What Is Deep Learning? 6. Deep Learning Applications Subscribe to our channel to get video updates. Hit the subscribe button above. Check our complete Deep Learning With TensorFlow playlist here: 🤍 PG in Artificial Intelligence and Machine Learning with NIT Warangal : 🤍 Post Graduate Certification in Data Science with IIT Guwahati - 🤍 (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 24x7 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 Tensorflow course will help you to learn the basic concepts of TensorFlow, the main functions, operations and the execution pipeline. Starting with a simple “Hello Word” example, throughout the course you will be able to see how TensorFlow can be used in curve fitting, regression, classification and minimization of error functions. This concept is then explored in the Deep Learning world. You will evaluate the common, and not so common, deep neural networks and see how these can be exploited in the real world with complex raw data using TensorFlow. In addition, you will learn how to apply TensorFlow for backpropagation to tune the weights and biases while the Neural Networks are being trained. Finally, the course covers different types of Deep Architectures, such as Convolutional Networks, Recurrent Networks and Autoencoders. Delve into neural networks, implement Deep Learning algorithms, and explore layers of data abstraction with the help of this Deep Learning with TensorFlow course. - - - - - - - - - - - - - - Who should go for this course? The following professionals can go for this course: 1. Developers aspiring to be a 'Data Scientist' 2. Analytics Managers who are leading a team of analysts 3. Business Analysts who want to understand Deep Learning (ML) Techniques 4. Information Architects who want to gain expertise in Predictive Analytics 5. Professionals who want to captivate and analyze Big Data 6. Analysts wanting to understand Data Science methodologies However, Deep learning is not just focused to one particular industry or skill set, it can be used by anyone to enhance their portfolio. - - - - - - - - - - - - - - Why Learn Deep Learning With TensorFlow? TensorFlow is one of the best libraries to implement Deep Learning. TensorFlow is a software library for numerical computation of mathematical expressions, using data flow graphs. Nodes in the graph represent mathematical operations, while the edges represent the multidimensional data arrays (tensors) that flow between them. It was created by Google and tailored for Machine Learning. In fact, it is being widely used to develop solutions with Deep Learning. Machine learning is one of the fastest-growing and most exciting fields out there, and Deep Learning represents its true bleeding edge. Deep learning is primarily a study of multi-layered neural networks, spanning over a vast range of model architectures. Traditional neural networks relied on shallow nets, composed of one input, one hidden layer and one output layer. Deep-learning networks are distinguished from these ordinary neural networks having more hidden layers, or so-called more depth. These kinds of nets are capable of discovering hidden structures within unlabeled and unstructured data (i.e. images, sound, and text), which constitutes the vast majority of data in the world. For more information, please write back to us at sales🤍edureka.co or call us at IND: 9606058406 / US: 18338555775 (toll-free). Facebook: 🤍 Twitter: 🤍 LinkedIn: 🤍
In this course you learn all the fundamentals to get started with PyTorch and Deep Learning. ⭐ Check out Tabnine, the FREE AI-powered code completion tool I use to help me code faster: 🤍 * Find Python and ML jobs: 🤍 Get my Free NumPy Handbook: 🤍 If you enjoyed this video, please subscribe to the channel: 🤍 Code: 🤍 Playlist with single videos: 🤍 ~~~~~~~~~~~~~~~ CONNECT ~~~~~~~~~~~~~~~ 🖥️ Website: 🤍 🐦 Twitter - 🤍 📸 Instagram - 🤍 🦾 Discord: 🤍 💻 GitHub: 🤍 ~~~~~~~~~~~~~~ SUPPORT ME ~~~~~~~~~~~~~~ 🅿 Patreon - 🤍 #Python #PyTorch Timeline: 00:00 - Intro 01:42 - 1 Installation 07:30 - 2 Tensor Basics 26:02 - 3 Autograd 42:00 - 4 Backpropagation 55:18 - 5 Gradient Descent 1:12:53 - 6 Training Pipeline 1:27:14 - 7 Linear Regression 1:39:30 - 8 Logistic Regression 1:57:56 - 9 Dataset and Dataloader 2:13:28 - 10 Dataset Transforms 2:24:14 - 11 Softmax and Crossentropy 2:42:36 - 12 Activation Functions 2:52:40 - 13 Feed Forward Net 3:14:18 - 14 CNN 3:36:30 - 15 Transfer Learning 3:51:30 - 16 Tensorboard 4:17:14 - 17 Save & Load Models * This is a sponsored 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! 🙏
Not too sure where or how to start? In this video, you'll learn how to setup your machine and begin using some of the most common tools and libraries when it comes to deep learning. You'll go from absolute beginner to successfully running your own image classification model by the end of this tutorial. Get the Code 🤍 Links Anaconda: 🤍 Git: 🤍 Venv Documentation: 🤍 Tensorflow CUDA/cuDNN Versions: 🤍 CUDA 11.2: 🤍 cuDNN Archive: 🤍 Visual Studio 2019: 🤍 Chapters 0:00 - Start 1:58 - PART 1: Setting up Python and Jupyter with Anaconda 3:38 - Installing Anaconda 7:38 - Working with Jupyter 12:32 - PART 2: Environment Creation Workflows 13:44 - Working with Git and GitHub 19:12 - Creating Environments for DL 24:11 - Activating a Virtual Environment 32:05 - PART 3: Installing Tensorflow for Deep Learning 39:23 - Running the Image Classifier Pipeline 41:32 - PART 4: Configuring your GPU 57:06 - PART 5: Training a Deep Image Classifier Oh, and don't forget to connect with me! LinkedIn: 🤍 Facebook: 🤍 GitHub: 🤍 Patreon: 🤍 Join the Discussion on Discord: 🤍 Happy coding! Nick P.s. Let me know how you go and drop a comment if you need a hand! #deeplearning #python
MIT Introduction to Deep Learning 6.S191: Lecture 2 Recurrent Neural Networks Lecturer: Ava Soleimany January 2022 For all lectures, slides, and lab materials: 🤍 Lecture Outline 0:00 - Introduction 1:59 - Sequence modeling 4:16 - Neurons with recurrence 10:09 - Recurrent neural networks 11:42 - RNN intuition 14:44 - Unfolding RNNs 16:43 - RNNs from scratch 19:49 - Design criteria for sequential modeling 21:00 - Word prediction example 27:49 - Backpropagation through time 30:02 - Gradient issues 33:53 - Long short term memory (LSTM) 35:35 - RNN applications 40:22 - Attention fundamentals 43:12 - Intuition of attention 44:53 - Attention and search relationship 47:16 - Learning attention with neural networks 54:52 - Scaling attention and applications 56:09 - Summary Subscribe to stay up to date with new deep learning lectures at MIT, or follow us 🤍MITDeepLearning on Twitter and Instagram to stay fully-connected!!
🔥 Enroll for FREE Artificial Intelligence Course & Get your Completion Certificate: 🤍 This video on "What is a Neural Network" delivers an entertaining and exciting introduction to the concepts of Neural Network. We will learn the different layers present in a Neural Network and understand how these layers process data. We will get an idea of the different parameters used in a Neural Network such as weights, bias, and activation functions. We will also understand how to train a Neural Network using forward propagation and then adjust to the errors in the network using the backpropagation method. This video also covers a few popular Neural Network applications. Now, let us jump straight into learning what is a Neural Network. 0:00 What is a Neural Network? 0:33 How Neural Networks work? 03:43 Neural Network examples 04:21 Quiz 04:52 Neural Network applications Don't forget to take the quiz at 04:21 Comment below what you think is the right answer, to be one of the 3 lucky winners who can win Amazon vouchers worth INR 500 or $10 (depending on your location). What are you waiting for? Winners will be announced on Jun 26, 2019. To learn more about Deep Learning, subscribe to our YouTube channel: 🤍 Download the Artificial Intelligence Career Guide and take a sneak peek into the world that awaits you: 🤍 Watch more videos on Deep Learning: 🤍 #NeuralNetwork #WhatIsANeuralNetwork #WhatAreNeuralNetworks #DeepLearningAndNeuralNetworks #DeepLearning #ArtificalNeuralNetwork #NeuralNetworkExplained #WhatIsDeepLearning #DeepLearningTutorial #DeepLearningCourse #DeepLearningExplained #Simplilearn Simplilearn’s Deep Learning course will transform you into an expert in Deep Learning techniques using TensorFlow, the open-source software library designed to conduct machine learning & deep neural network research. With our Deep Learning course, you'll master Deep Learning and TensorFlow concepts, learn to implement algorithms, build artificial neural networks and traverse layers of data abstraction to understand the power of data and prepare you for your new role as Deep Learning scientist. Why Deep Learning? It is one of the most popular software platforms used for Deep Learning and contains powerful tools to help you build and implement artificial neural networks. Advancements in Deep Learning are being seen in smartphone applications, creating efficiencies in the power grid, driving advancements in healthcare, improving agricultural yields, and helping us find solutions to climate change. You can gain in-depth knowledge of Deep Learning by taking our Deep Learning certification training course. With Simplilearn’s Deep Learning course, you will prepare for a career as a Deep Learning engineer as you master concepts and techniques including supervised and unsupervised learning, mathematical and heuristic aspects, and hands-on modeling to develop algorithms. Those who complete the course will be able to: 1. Understand the concepts of TensorFlow, its main functions, operations and the execution pipeline 2. Implement Deep Learning algorithms, understand neural networks and traverse the layers of data abstraction which will empower you to understand data like never before 3. Master and comprehend advanced topics such as convolutional neural networks, recurrent neural networks, training deep networks and high-level interfaces 4. Build Deep Learning models in TensorFlow and interpret the results We recommend this Deep Learning online course particularly for the following professionals: 1. Software engineers 2. Data scientists 3. Data analysts 4. Statisticians with an interest in Deep Learning Learn more at: 🤍 For more information about Simplilearn’s courses, visit: - Facebook: 🤍 - Twitter: 🤍 - LinkedIn: 🤍 - Website: 🤍 Get the Android app: 🤍 Get the iOS app: 🤍
Got lots of data? Machine learning can help! In this episode of Cloud AI Adventures, Yufeng Guo explains machine learning from the ground up, using concrete examples. Learn more through our hands-on labs → 🤍 Associated article "What is Machine Learning?" → 🤍 Qwiklabs → 🤍 Watch more episodes of AI Adventures here → 🤍 TensorFlow → 🤍 Cloud ML Engine → 🤍 Hands-on intro level lab Baseline: Data, ML, AI → 🤍 Don't forget to subscribe to the channel! → 🤍 #AIAdventures
Deep Learning ِ(in Arabic) التعلم العميق او التعلم بعمق بالعربي مع تحيات : حسام الحوراني
0:00 Intro 1:52 Deep Learning Definition 1 13:17 Deep Learning Definition 2 20:00 Deep Learning Vs Machine Learning 34:45 Factors Behind Deep Learning's Success 1:06:00 Outro
Everyone tells you why you should be learning machine learning. It is the next 'big thing' after all. But in this video I'm going to be telling you why you should NOT learn machine learning. There are many reasons for not starting machine learning. Some of these are: 1. Don't get into ML just because it is popular. Basically do not FOMO into it. 2. Machine learning is a huge field, there are so many things to do. You might start it but eventually give up because you don't know what exactly you want to build using ML/AI. 3. Just because you saw people getting paid more for machine learning jobs doesn't mean you should start it. As you can still polish your skills in other computer science related fields. Even though I mention why you should not be get into machine learning, this doesn't mean that you should not at least give it a go. This video is only made for some people who want to get into ML/AI without having any major goal. Let me know what you think about this in the comment section below. Follow me: - Subscribe: 🤍 LinkedIn: 🤍 Instagram: 🤍 Music: 🤍
A very simple explanation of convolutional neural network or CNN or ConvNet such that even a high school student can understand it easily. This video involves very less math and is perfect for total beginner who doesn't have any idea on what CNN is and how it works. We will cover different topics such as, 1. Why traditionally humans are better at image recognition than computers? 2. Disadvantages of using traditional artificial neural network (ANN) for image classification. 3. How human brain recognizes images? 4. How computers can use filters for feature detection 5. What is convolution operation and how it works 6. Importance of ReLU activation in CNN 7. Importance of pooling operation in CNN 8. How to handle rotation and scale in CNN 🔖 Hashtags 🔖 #convolutionalneuralnetwork #cnndeeplearning #cnntutorial #cnnmachinelearning #cnnalgorithm #cnnpython #cnntensorflow Do you want to learn technology from me? Check 🤍 for my affordable video courses. 🤝 Support my youtube channel by buying a data science, coding 👕 T-shirt: 🤍 Deep learning playlist: 🤍 Machine learning playlist : 🤍 Here are some good articles on CNN, Is CNN scale/rotation invariant? 🤍 🤍 PDF Paper on cnn: 🤍 🌎 My Website For Video Courses: 🤍 Need help building software or data analytics and AI solutions? My company 🤍 can help. Click on the Contact button on that website. Discord: 🤍 Website: 🤍 Facebook: 🤍 Twitter: 🤍 Linkedin: 🤍 DISCLAIMER: All opinions expressed in this video are of my own and not that of my employers'.
An updated deep learning introduction using Python, TensorFlow, and Keras. Text-tutorial and notes: 🤍 TensorFlow Docs: 🤍 Keras Docs: 🤍 Discord: 🤍
MarI/O is a program made of neural networks and genetic algorithms that kicks butt at Super Mario World. Source Code: 🤍 "NEAT" Paper: 🤍 Some relevant Wikipedia links: 🤍 🤍 🤍 BizHawk Emulator: 🤍 SethBling Twitter: 🤍 SethBling Twitch: 🤍 SethBling Facebook: 🤍 SethBling Website: 🤍 SethBling Shirts: 🤍 Suggest Ideas: 🤍 Music at the end is Cipher by Kevin MacLeod
Even if you are completely new to neural networks, this course will get you comfortable with the concepts and math behind them. Neural networks are at the core of what we are calling Artificial Intelligence today. They can seem impenetrable, even mystical, if you are trying to understand them for the first time, but they don't have to. ⭐️ Contents ⭐️ ⌨️ (0:00:00) How neural networks work ⌨️ (0:24:13) What neural networks can learn and how they learn it ⌨️ (0:51:37) How convolutional neural networks (CNNs) work ⌨️ (1:16:55) How recurrent neural networks (RNNs) and long-short-term memory (LSTM) work ⌨️ (1:42:49) Deep learning demystified ⌨️ (2:03:33) Getting closer to human intelligence through robotics ⌨️ (2:49:18) How CNNs work, in depth 🎥 Lectures by Brandon Rohrer. Check out his YouTube channel: 🤍 🔗 Find more courses from Brandon at 🤍 Learn to code for free and get a developer job: 🤍 Read hundreds of articles on programming: 🤍 And subscribe for new videos on technology: 🤍