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Deep Learning

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Syllabus

Deep Learning - Lecture 1.1 (Introduction: Organization).
Deep Learning - Lecture 1.2 (Introduction: History of Deep Learning).
Deep Learning - Lecture 1.3 (Introduction: Machine Learning Basics).
Deep Learning - Lecture 2.1 (Computation Graphs: Logistic Regression).
Deep Learning - Lecture 2.2 (Computation Graphs: Computation Graphs).
Deep Learning - Lecture 2.3 (Computation Graphs: Backpropagation).
Deep Learning - Lecture 2.4 (Computation Graphs: Educational Framework).
Deep Learning - Lecture 3.1 (Deep Neural Networks: Backpropagation with Tensors).
Deep Learning - Lecture 3.2 (Deep Neural Networks: The XOR Problem).
Deep Learning - Lecture 3.3 (Deep Neural Networks: Multi-Layer Perceptrons).
Deep Learning - Lecture 3.4 (Deep Neural Networks: Universal Approximation).
Deep Learning - Lecture 4.1 (Deep Neural Networks II: Output and Loss Functions).
Deep Learning - Lecture 4.2 (Deep Neural Networks II: Activation Functions).
Deep Learning - Lecture 4.3 (Deep Neural Networks II: Preprocessing and Initialization).
Deep Learning - Lecture 5.1 (Regularization: Parameter Penalties).
Deep Learning - Lecture 5.2 (Regularization: Early Stopping).
Deep Learning - Lecture 5.3 (Regularization: Ensemble Methods).
Deep Learning - Lecture 5.4 (Regularization: Dropout).
Deep Learning - Lecture 5.5 (Regularization: Data Augmentation).
Deep Learning - Lecture 6.1 (Optimization: Optimization Challenges).
Deep Learning - Lecture 6.2 (Optimization: Optimization Algorithms).
Deep Learning - Lecture 6.3 (Optimization: Optimization Strategies).
Deep Learning - Lecture 6.4 (Optimization: Debugging Strategies).
Deep Learning - Lecture 7.1 (Convolutional Neural Networks: Convolution).
Deep Learning - Lecture 7.2 (Convolutional Neural Networks: Downsampling).
Deep Learning - Lecture 7.3 (Convolutional Neural Networks: Upsampling).
Deep Learning - Lecture 7.4 (Convolutional Neural Networks: Architectures).
Deep Learning - Lecture 7.5 (Convolutional Neural Networks: Visualization).
Deep Learning - Lecture 8.1 (Sequence Models: Recurrent Networks).
Deep Learning - Lecture 8.2 (Sequence Models: Recurrent Network Applications).
Deep Learning - Lecture 8.3 (Sequence Models: Gated Recurrent Networks).
Deep Learning - Lecture 8.4 (Sequence Models: Autoregressive Models).
Deep Learning - Lecture 9.1 (Natural Language Processing: Language Models).
Deep Learning - Lecture 9.2 (Natural Language Processing: Traditional Language Models).
Deep Learning - Lecture 9.3 (Natural Language Processing: Neural Language Models).
Deep Learning - Lecture 9.4 (Natural Language Processing: Neural Machine Translation).
Deep Learning - 10.1 (Graph Neural Networks: Machine Learning on Graphs).
Deep Learning - 10.2 (Graph Neural Networks: Graph Convolution Filters).
Deep Learning - 10.3 (Graph Neural Networks: Graph Convolution Networks).
Deep Learning - Lecture 11.1 (Autoencoders: Latent Variable Models).
Deep Learning - Lecture 11.2 (Autoencoders: Principal Component Analysis.
Deep Learning - Lecture 11.3 (Autoencoders: Autoencoders).
Deep Learning - Lecture 11.4 (Autoencoders: Variational Autoencoders).
Deep Learning - Lecture 12.1 (Generative Adversarial Networks: Generative Adversarial Networks).
Deep Learning - Lecture 12.2 (Generative Adversarial Networks: GAN Developments).
Deep Learning - Lecture 12.3 (Generative Adversarial Networks: Research at AVG).

Taught by

Tübingen Machine Learning

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