Complete Deep Learning Course: From Basics to GANs & CNNs
Master Deep Learning: From Basics to Advanced AI Models
5.0(41)
326 learners
What you'll learn
Design and implement feedforward and convolutional neural networks using modern frameworks
Apply backpropagation and gradient-based optimization algorithms to train deep learning models
Implement and compare regularization techniques to improve model generalization
Construct and apply sequence models like RNNs for natural language processing tasks
This course includes
21.5 hours of video
Certificate of completion
Access on mobile and TV
Course content
1 modules
• 46 lessons
• 21.5 hours of video
Complete Deep Learning Course: From Basics to GANs & CNNs
46 lessons
• 21.5 hours
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Deep Learning - Lecture 1.1 (Introduction: Organization)04:53
Deep Learning - Lecture 1.2 (Introduction: History of Deep Learning)49:03
Deep Learning - Lecture 1.3 (Introduction: Machine Learning Basics)54:44
Deep Learning - Lecture 2.1 (Computation Graphs: Logistic Regression)39:53
Deep Learning - Lecture 2.2 (Computation Graphs: Computation Graphs)13:55
Deep Learning - Lecture 2.3 (Computation Graphs: Backpropagation)39:34
Deep Learning - Lecture 2.4 (Computation Graphs: Educational Framework)16:39
Deep Learning - Lecture 3.1 (Deep Neural Networks: Backpropagation with Tensors)33:32
Deep Learning - Lecture 3.2 (Deep Neural Networks: The XOR Problem)36:03
Deep Learning - Lecture 3.3 (Deep Neural Networks: Multi-Layer Perceptrons)28:34
Deep Learning - Lecture 3.4 (Deep Neural Networks: Universal Approximation)18:34
Deep Learning - Lecture 4.1 (Deep Neural Networks II: Output and Loss Functions)01:08:27
