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Deep Learning Crash Course for Beginners

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Lec 01. Introduction to Deep Learning

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Deep Learning Explained Simply (In 14 Minutes)

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Machine Learning vs Deep Learning

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PyTorch for Deep Learning & Machine Learning – Full Course

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Deep Learning Full Course - Learn Deep Learning - 10 Hours [2026] | Deep Learning Tutorial | Edureka

Deep Learning Full Course - Learn Deep Learning - 10 Hours [2026] | Deep Learning Tutorial | Edureka

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🎓 Featured Course Guide & Lesson Notes: Deep Learning Crash Course for Beginners

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Instructor: freeCodeCamp.org  |  Runtime: 03:30 mins  |  Students / Views: 910,972

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 work on Github: https://github.com/jasmcaus ❤️ Try interactive AI courses we love, right in your browser: https://scrimba.com/freeCodeCamp-AI (Made possible by a grant from our friends at Scrimba) ⭐️ 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: https://www.freecodecamp.org Read hundreds of articles on programming: https://freecodecamp.org/news Key Highlights & Overview: Learning <strong>Deep Learning Basic</strong> requires clear step-by-step guidance and practical hands-on visual demonstrations. Follow along with top-rated video tutorials from industry experts today on <strong>TutorTube</strong>.