Understand the essential concepts and terminology of Deep Finding out, a sub-department of Machine Learning. This study course is created for complete newcomers with no knowledge in programming. You will find out the critical suggestions behind deep finding out devoid of any code.
You can expect to study about Neural Networks, Machine Learning constructs like Supervised, Unsupervised and Reinforcement Learning, the several types of Neural Community architectures, and much more.
✏️ Program formulated by Jason Dsouza. Check out out his YouTube channel: http://youtube.com/jasmcaus
⭐️ Study course Contents ⭐️
⌨️ (:00) Introduction
⌨️ (1:18) What is Deep Learning
⌨️ (5:25) Introduction to Neural Networks
⌨️ (6:12) How do Neural Networks Discover?
⌨️ (12:06) Main terminologies employed in Deep Finding out
⌨️ (12:11) Activation Capabilities
⌨️ (22:36) Reduction Features
⌨️ (23:42) Optimizers
⌨️ (30:10) Parameters vs Hyperparameters
⌨️ (32:03) Epochs, Batches & Iterations
⌨️ (34:24) Summary 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 Community Architectures
⌨️ (51:37) Completely-Linked Feedforward Neural Nets
⌨️ (54:05) Recurrent Neural Nets
⌨️ (1:04:40) Convolutional Neural Nets
⌨️ (1:08:07) Introduction to the 5 Methods to Just about every Deep Learning Model
⌨️ (1:08:23) 1. Gathering Data
⌨️ (1:11:27) 2. Preprocessing the Knowledge
⌨️ (1:19:05) 3. Schooling your Model
⌨️ (1:19:33) 4. Evaluating your Model
⌨️ (1:19:55) 5. Optimizing your Model’s Accuracy
⌨️ (1:25:15) Conclusion to the Program
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