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 https://codebasics.io/ for my affordable video courses. 🤝 Support my youtube channel by buying a data science, coding 👕 T-shirt: https://kaaipo.com/collections/coding-collection/?utm_source=youtube&utm_medium=post&utm_campaign=codebasics-community Deep learning playlist: https://www.youtube.com/playlist?list=PLeo1K3hjS3uu7CxAacxVndI4bE_o3BDtO Machine learning playlist : https://www.youtube.com/playlist?list=PLeo1K3hjS3uvCeTYTeyfe0-rN5r8zn9rw Here are some good articles on CNN, Is CNN scale/rotation invariant? https://stats.stackexchange.com/questions/239076/about-cnn-kernels-and-scale-rotation-invariance#:~:text=22-,1)%20The%20features%20extracted%20using%20CNN%20are%20scale%20and%20rotation,details%2C%20see%3A%20Deep%20Learning.&text=Convolution%20is%20not%20naturally%20equivariant,or%20rotation%20of%20an%20image. https://ujjwalkarn.me/2016/08/11/intuitive-explanation-convnets/ PDF Paper on cnn: http://www.deeplearningbook.org/contents/convnets.html 🌎 My Website For Video Courses: https://codebasics.io/ Need help building software or data analytics and AI solutions? My company https://www.atliq.com/ can help. Click on the Contact button on that website. Discord: https://discord.gg/r42Kbuk Website: https://codebasics.io/ Facebook: https://www.facebook.com/codebasicshub Twitter: https://twitter.com/codebasicshub Linkedin: https://www.linkedin.com/company/codebasics/ DISCLAIMER: All opinions expressed in this video are of my own and not that of my employers’.
🔥 Enroll for FREE Artificial Intelligence Course & Get your Completion Certificate: https://www.simplilearn.com/learn-ai-basics-skillup?utm_campaign=AI&utm_medium=DescriptionFirstFold&utm_source=youtube This Convolutional neural network tutorial (CNN) will help you understand what is a convolutional neural network, how CNN recognizes images, what are layers in the convolutional neural network and at the end, you will see a use case implementation using CNN. A CNN is also known as a “ConvNet”. Convolutional networks can also perform optical character recognition to digitize text and make natural-language processing possible on analog and hand-written documents. CNN can also be applied to sound when it is represented visually as a spectrogram. Now, let’s deep dive into this video to understand what is CNN and how do they actually work. Start learning today’s most in-demand skills for FREE. Visit us at https://www.simplilearn.com/skillup-free-online-courses?utm_campaign=AI&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. Below topics are explained in this CNN tutorial (Convolutional Neural Network Tutorial) 1. Introduction to CNN 2. What is a convolutional neural network? 3. How CNN recognizes images? 4. Layers in convolutional neural network 5. Use case implementation using CNN To learn more about Deep Learning, subscribe to our YouTube channel: https://www.youtube.com/user/Simplilearn?sub_confirmation=1 You can also go through the slides here: https://goo.gl/ZNcp9n Watch more videos on Deep Learning: https://www.youtube.com/watch?v=FbxTVRfQFuI&list=PLEiEAq2VkUUIYQ-mMRAGilfOKyWKpHSip #DeepLearning #Datasciencecourse #DataScience #SimplilearnMachineLearning #DeepLearningCourse We’ve partnered with Purdue University and collaborated with IBM to offer you the unique Post Graduate Program [More]
#emotiondetection #opencv #cnn #python Code – https://github.com/akmadan/Emotion_Detection_CNN Telegram Channel- https://t.me/akshitmadan Instagram- https://www.instagram.com/akshitmadan_/?hl=en LinkedIn- https://www.linkedin.com/in/akshit-madan-394a82a6 Books for Reference – Python for Beginners – https://amzn.to/3oZmqSm Complete Data Science – https://amzn.to/3nTZkuV Data Science Handbook – https://amzn.to/3oYHHvt Book for Computer Vision – Learning OpenCV by O’Reilly – https://amzn.to/391GJJo
In this lesson, we will learn how to perform image classification using Convolutional Neural Network (CNN) in MATLAB. Convolutional Neural Network (CNN) is a powerful machine learning technique from the deep learning domain. A collection of diverge image is required to train CNNs. The larger the collection the richer the features that CNN learns. These features often outperform features such as HOG, LBP or SURF. Training a CNN with large collection of diverse images is not an easy task. However, there is an easy way. We can use pertained CNN to leverage the power of CNN. It saves a huge amount of time and effort when we use pretrained CNN as feature extractor. In this lesson, I used ‘ResNet-50’ as pretrained CNN and Caltech101 image dataset. Image classification using convolutional neural network is a very exciting topic. Once you will have learned how to classify images using CNN, you can do what ever you want. For example – you can train classifier to identify brain tumor, cancer cell and skin diseases. Object recognition is another excellent field where you can use the method shown in this lesson. Image classification using CNN in MATLAB is not a straightforward approach. However, the strategy used in this lecture has made it simple. Each function used here, the role and outcome of each line and explanation with example where needed have made this lesson the best lesson on image classification using convolutional neural network in MATLAB. After completing this lesson, you will learn: 1. [More]
Learn to build a Keras model for speech classification. Audio is the field that ignited industry interest in deep learning. Although the data doesn’t look like the images and text we’re used to processing, we can use similar techniques to take short speech sound bites and identify what someone is saying. Follow along with Lukas using the Python scripts here: https://github.com/lukas/ml-class/tree/master/videos/cnn-audio This is part of a long, free series of tutorials teaching engineers to do deep learning. Leave questions below, and check out more of our class videos: Class Videos: http://wandb.com/classes Weights & Biases: http://wandb.com
In Lecture 5 we move from fully-connected neural networks to convolutional neural networks. We discuss some of the key historical milestones in the development of convolutional networks, including the perceptron, the neocognitron, LeNet, and AlexNet. We introduce convolution, pooling, and fully-connected layers which form the basis for modern convolutional networks. Keywords: Convolutional neural networks, perceptron, neocognitron, LeNet, AlexNet, convolution, pooling, fully-connected layers Slides: http://cs231n.stanford.edu/slides/2017/cs231n_2017_lecture5.pdf ————————————————————————————– Convolutional Neural Networks for Visual Recognition Instructors: Fei-Fei Li: http://vision.stanford.edu/feifeili/ Justin Johnson: http://cs.stanford.edu/people/jcjohns/ Serena Yeung: http://ai.stanford.edu/~syyeung/ Computer Vision has become ubiquitous in our society, with applications in search, image understanding, apps, mapping, medicine, drones, and self-driving cars. Core to many of these applications are visual recognition tasks such as image classification, localization and detection. Recent developments in neural network (aka “deep learning”) approaches have greatly advanced the performance of these state-of-the-art visual recognition systems. This lecture collection is a deep dive into details of the deep learning architectures with a focus on learning end-to-end models for these tasks, particularly image classification. From this lecture collection, students will learn to implement, train and debug their own neural networks and gain a detailed understanding of cutting-edge research in computer vision. Website: http://cs231n.stanford.edu/ For additional learning opportunities please visit: http://online.stanford.edu/
This tutorial would help you understand Deep learning frameworks, such as convolutional neural networks (CNNs), which have almost completely replaced other machine learning techniques for specific tasks such as image recognition using large training datasets. In this webinar, we will go over how CNNs, their training methods, and hardware evolved since LeNet first appeared in the late 1990’s. We will examine the challenges that came along, and some key innovations that helped overcome these challenges. We will also look at a guide on how to get started with CNNs, some common pitfalls, and tips and tricks in training CNNs. Advanced Technology Group (ATG) of the CTO Office at NetApp. The ATG group is responsible for investigations, through early product prototypes, and leveraging technologies expected to become mainstream in 3+ years. About us: HackerEarth is the most comprehensive developer assessment software that helps companies to accurately measure the skills of developers during the recruiting process. More than 500 companies across the globe use HackerEarth to improve the quality of their engineering hires and reduce the time spent by recruiters on screening candidates. Over the years, we have also built a thriving community of 2.5M+ developers that come to HackerEarth to participate in hackathons and coding challenges to assess their skills and compete in the community.
Announcement: New Book by Luis Serrano! Grokking Machine Learning. bit.ly/grokkingML A friendly explanation of how computer recognize images, based on Convolutional Neural Networks. All the math required is knowing how to add and subtract 1’s. (Bonus if you know calculus, but not needed.) For a brush up on Neural Networks, check out this video: https://www.youtube.com/watch?v=BR9h47Jtqyw
Lecture 1 gives an introduction to the field of computer vision, discussing its history and key challenges. We emphasize that computer vision encompasses a wide variety of different tasks, and that despite the recent successes of deep learning we are still a long way from realizing the goal of human-level visual intelligence. Keywords: Computer vision, Cambrian Explosion, Camera Obscura, Hubel and Wiesel, Block World, Normalized Cut, Face Detection, SIFT, Spatial Pyramid Matching, Histogram of Oriented Gradients, PASCAL Visual Object Challenge, ImageNet Challenge Slides: http://cs231n.stanford.edu/slides/2017/cs231n_2017_lecture1.pdf ————————————————————————————– Convolutional Neural Networks for Visual Recognition Instructors: Fei-Fei Li: http://vision.stanford.edu/feifeili/ Justin Johnson: http://cs.stanford.edu/people/jcjohns/ Serena Yeung: http://ai.stanford.edu/~syyeung/ Computer Vision has become ubiquitous in our society, with applications in search, image understanding, apps, mapping, medicine, drones, and self-driving cars. Core to many of these applications are visual recognition tasks such as image classification, localization and detection. Recent developments in neural network (aka “deep learning”) approaches have greatly advanced the performance of these state-of-the-art visual recognition systems. This lecture collection is a deep dive into details of the deep learning architectures with a focus on learning end-to-end models for these tasks, particularly image classification. From this lecture collection, students will learn to implement, train and debug their own neural networks and gain a detailed understanding of cutting-edge research in computer vision. Website: http://cs231n.stanford.edu/ For additional learning opportunities please visit: http://online.stanford.edu/