Convolutional Neural Network Tutorial (CNN): How CNN Works in Deep Learning

"️‍🔥Purdue - Professional Certificate in AI and Machine Learning - https://www.simplilearn.com/pgp-ai-machine-learning-certification-training-course?utm_campaign=Jy9-aGMB_TE&utm_medium=DescriptionFFF&utm_source=Youtube ️‍🔥IITK - Professional Certificate Course in Generative AI and Machine Learning - https://www.simplilearn.com/iitk-professional-certificate-course-ai-machine-learning?utm_campaign=Jy9-aGMB_TE&utm_medium=DescriptionFFF&utm_source=Youtube ️‍🔥Microsoft AI Engineer Program - https://www.simplilearn.com/ai-engineer-course?utm_campaign=Jy9-aGMB_TE&utm_medium=DescriptionFFF&utm_source=Youtube ️‍🔥Purdue - Applied Generative AI Specialization - https://www.simplilearn.com/applied-ai-course?utm_campaign=Jy9-aGMB_TE&utm_medium=DescriptionFFF&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 in AI and Machine Learning. Learn more about it here - https://www.simplilearn.com/ai-and-machine-learning-post-graduate-certificate-program-purdue?utm_campaign=Convolutional-Neural-Network-Tutorial-CNN-Tutorial-Jy9-aGMB_TE&utm_medium=Tutorials&utm_source=youtube 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. 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