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.
โ๏ธ Course developed by Jason Dsouza. Check out his YouTube channel: http://youtube.com/jasmcaus
โญ๏ธ Course Contents โญ๏ธ
โจ๏ธ (0:00) Introduction
โจ๏ธ (1:18) What is Deep Learning
โจ๏ธ (5:25) Introduction to Neural Networks
โจ๏ธ (6:12) How do Neural Networks LEARN?
โจ๏ธ (12:06) Core terminologies used in Deep Learning
โจ๏ธ (12:11) Activation Functions
โจ๏ธ (22:36) Loss Functions
โจ๏ธ (23:42) Optimizers
โจ๏ธ (30:10) Parameters vs Hyperparameters
โจ๏ธ (32:03) Epochs, Batches & Iterations
โจ๏ธ (34:24) Conclusion to Terminologies
โจ๏ธ (35:18) Introduction to Learning
โจ๏ธ (35:34) Supervised Learning
โจ๏ธ (40:21) Unsupervised Learning
โจ๏ธ (43:38) Reinforcement Learning
โจ๏ธ (46:25) Regularization
โจ๏ธ (51:25) Introduction to Neural Network Architectures
โจ๏ธ (51:37) Fully-Connected Feedforward Neural Nets
โจ๏ธ (54:05) Recurrent Neural Nets
โจ๏ธ (1:04:40) Convolutional Neural Nets
โจ๏ธ (1:08:07) Introduction to the 5 Steps to EVERY Deep Learning Model
โจ๏ธ (1:08:23) 1. Gathering Data
โจ๏ธ (1:11:27) 2. Preprocessing the Data
โจ๏ธ (1:19:05) 3. Training your Model
โจ๏ธ (1:19:33) 4. Evaluating your Model
โจ๏ธ (1:19:55) 5. Optimizing your Model’s Accuracy
โจ๏ธ (1:25:15) Conclusion to the Course
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