Deep Learning — Andreas Geiger
Master Deep Learning: From Basics to Advanced AI Models
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6 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
Complete Deep Learning Course: From Basics to GANs & CNNs
46 lessons
• 21.5 hours
- Deep Learning - Lecture 1.1 (Introduction: Organization) 04:54
- Deep Learning - Lecture 1.2 (Introduction: History of Deep Learning) 49:03
- Deep Learning - Lecture 1.3 (Introduction: Machine Learning Basics) 54:45
- 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) 39:35
- Deep Learning - Lecture 2.4 (Computation Graphs: Educational Framework) 16:40
- Deep Learning - Lecture 3.1 (Deep Neural Networks: Backpropagation with Tensors) 33:33
- Deep Learning - Lecture 3.2 (Deep Neural Networks: The XOR Problem) 36:04
- Deep Learning - Lecture 3.3 (Deep Neural Networks: Multi-Layer Perceptrons) 28:35
- 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) 01:08:28
- Deep Learning - Lecture 4.2 (Deep Neural Networks II: Activation Functions) 25:20
- Deep Learning - Lecture 4.3 (Deep Neural Networks II: Preprocessing and Initialization) 21:29
- Deep Learning - Lecture 5.1 (Regularization: Parameter Penalties) 44:13
- Deep Learning - Lecture 5.2 (Regularization: Early Stopping) 09:25
- Deep Learning - Lecture 5.3 (Regularization: Ensemble Methods) 11:07
- Deep Learning - Lecture 5.4 (Regularization: Dropout) 12:25
- Deep Learning - Lecture 5.5 (Regularization: Data Augmentation) 30:38
- Deep Learning - Lecture 6.1 (Optimization: Optimization Challenges) 13:26
- Deep Learning - Lecture 6.2 (Optimization: Optimization Algorithms) 49:15
- Deep Learning - Lecture 6.3 (Optimization: Optimization Strategies) 23:07
- Deep Learning - Lecture 6.4 (Optimization: Debugging Strategies) 21:52
- Deep Learning - Lecture 7.1 (Convolutional Neural Networks: Convolution) 44:20
- Deep Learning - Lecture 7.2 (Convolutional Neural Networks: Downsampling) 19:04
- Deep Learning - Lecture 7.3 (Convolutional Neural Networks: Upsampling) 07:41
- Deep Learning - Lecture 7.4 (Convolutional Neural Networks: Architectures) 12:35
- Deep Learning - Lecture 7.5 (Convolutional Neural Networks: Visualization) 20:05
- Deep Learning - Lecture 8.1 (Sequence Models: Recurrent Networks) 30:00
- Deep Learning - Lecture 8.2 (Sequence Models: Recurrent Network Applications) 23:42
- Deep Learning - Lecture 8.3 (Sequence Models: Gated Recurrent Networks) 41:22
- Deep Learning - Lecture 8.4 (Sequence Models: Autoregressive Models) 14:04
- Deep Learning - Lecture 9.1 (Natural Language Processing: Language Models) 43:43
- Deep Learning - Lecture 9.2 (Natural Language Processing: Traditional Language Models) 17:43
- Deep Learning - Lecture 9.3 (Natural Language Processing: Neural Language Models) 21:34
- Deep Learning - Lecture 9.4 (Natural Language Processing: Neural Machine Translation) 32:30
- Deep Learning - 10.1 (Graph Neural Networks: Machine Learning on Graphs) 17:09
- Deep Learning - 10.2 (Graph Neural Networks: Graph Convolution Filters) 42:16
- Deep Learning - 10.3 (Graph Neural Networks: Graph Convolution Networks) 27:21
- Deep Learning - Lecture 11.1 (Autoencoders: Latent Variable Models) 23:49
- Deep Learning - Lecture 11.2 (Autoencoders: Principal Component Analysis 33:26
- Deep Learning - Lecture 11.3 (Autoencoders: Autoencoders) 10:48
- Deep Learning - Lecture 11.4 (Autoencoders: Variational Autoencoders) 35:28
- Deep Learning - Lecture 12.1 (Generative Adversarial Networks: Generative Adversarial Networks) 01:11:54
- Deep Learning - Lecture 12.2 (Generative Adversarial Networks: GAN Developments) 20:17
- Deep Learning - Lecture 12.3 (Generative Adversarial Networks: Research at AVG) 14:46
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