3-opt COMP5329 – Deep Learning¶ Tutorial 3 – Optimization¶ Semester 1, 2021 Objectives: To learn about gradient descent optimization. To understand the algorithm of Momentum. To understand the algorithm of AdaGrad. To understand the algorithm of Adam. (Exercise) Instructions: For more details about AdaGrad or Adam, please refer to Chapter 8 of Goodfellow, I., Bengio, […]
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2-MLP COMP5329 – Deep Learning¶ Tutorial 2 – Multilayer Neural Network¶ Semester 1, 2021 Objectives: To understand the multi-layer perceptron. To become familiar with backpropagation. Instructions: Go to File->Open. Drag and drop “lab2MLP_student.ipynb” file to the home interface and click upload. Read the code and complete the exercises. To run the cell you can press
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COMP5329 – Deep Learning¶ Tutorial 1 – Python and PyTorch¶ Semester 1, 2021 Objectives: • Reviewing Python syntax • Get familiar with scientific computing libraries, such as NumPy. • Get started on PyTorch Instructions: • Exercises to be completed on Python 3.7 • We recommend using virtual environment or conda locally, or Google Colab on
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1-python_pytorch-checkpoint COMP5329 – Deep Learning¶ Tutorial 1 – Python and PyTorch¶ Semester 1, 2021 Objectives: Reviewing Python syntax Get familiar with scientific computing libraries, such as NumPy. Get started on PyTorch Instructions: Exercises to be completed on Python 3.7 We recommend using virtual environment or conda locally, or Google Colab on the cloud. How To
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1-python_pytorch COMP5329 – Deep Learning¶ Tutorial 1 – Python and PyTorch¶ Semester 1, 2021 Objectives: Reviewing Python syntax Get familiar with scientific computing libraries, such as NumPy. Get started on PyTorch Instructions: Exercises to be completed on Python 3.7 We recommend using virtual environment or conda locally, or Google Colab on the cloud. How To
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COMP5329 – Deep Learning¶ Tutorial 4 – Regularization¶ Semester 1, 2021 Objectives: • To learn about regularization. • To be familiar with how the regularization methods work, i.e., L2 regularization, dropout, batch normalization, early stopping, etc. • To learn how to implement regularization methods with deep learning frameworks (in this tutorial we use pytorch). Instructions:
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4-reg COMP5329 – Deep Learning¶ Tutorial 4 – Regularization¶ Semester 1, 2021 Objectives: To learn about regularization. To be familiar with how the regularization methods work, i.e., L2 regularization, dropout, batch normalization, early stopping, etc. To learn how to implement regularization methods with deep learning frameworks (in this tutorial we use pytorch). Instructions: Install pytorch
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COMP5329 – Deep Learning¶ Tutorial 2 – Multilayer Neural Network¶ Semester 1, 2021 Objectives: • To understand the multi-layer perceptron. • To become familiar with backpropagation. Instructions: • Go to File->Open. Drag and drop “lab2MLP_student.ipynb” file to the home interface and click upload. • Read the code and complete the exercises. • To run the
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COMP5329 – Deep Learning¶ Tutorial 2 – Multilayer Neural Network¶ Semester 1, 2021 Objectives: • To understand the multi-layer perceptron. • To become familiar with backpropagation. Instructions: • Go to File->Open. Drag and drop “lab2MLP_student.ipynb” file to the home interface and click upload. • Read the code and complete the exercises. • To run the
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Agent-based Systems Paolo Turrini www.dcs.warwick.ac.uk/~pturrini R p.turrini@warwick.ac.uk Today We are going to look at the very basics of game theory, in particular: Pure and mixed strategies Nash equilibria We are also going to play a game. K. Leyton-Brown and Y. Shoham Essentials of Game Theory: A Concise, Multidisciplinary Introduction Morgan & Claypool Publishers, 2008.
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