Data de entrada: 12 de mai. de 2022


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19. Machine Learning & Deep Learning: An Overview This course will cover the basics of machine learning and deep learning for creating AI. You will learn the how’s and why’s of machine learning and deep learning techniques, specifically unsupervised learning, transfer learning, and some implementations. We’ll cover an introduction to neural networks and deep learning, and work with pre-trained neural network models, to demonstrate how to use these techniques in practice. We’ll look at several models, covering linear models, logistic regression, and LSTM and RNN models. 20. Deep Learning Practicum: Reinforcement Learning A hands-on introduction to reinforcement learning, which builds upon the knowledge of machine learning and AI covered in previous courses. You will learn about the reinforcement learning algorithms used in DeepMind’s Atari game playing system and in DeepMind’s Watson systems for AlphaGo. We’ll also learn the basics of providing feedback to our reinforcement learning agents, including how to programmatically change the behaviour of an agent. By the end of the course you will be comfortable with the basics of reinforcement learning, and be able to read and write simple reinforcement learning algorithms, and use them to create new RL agent. 21. Introduction to Deep Learning (Dive Insight) Intended for anyone who is interested in starting to learn the basics of deep learning, this course is a structured dive into the key ideas of deep learning. We’ll start with an introduction to neural networks and deep learning. We’ll cover how neural networks are structured, how they learn, and how we can train them. We’ll introduce the fundamental concepts of deep learning, including learning representations, hierarchical structures, gradient descent, and backpropagation. We’ll focus on a few of the most popular deep learning models: convolutional networks, recurrent networks, and autoencoders. Finally, we’ll examine and discuss practical concerns with deep learning, including model interpretability, overfitting, and model selection. 22. PyData Training: Data Modelling, Visualisation & Machine Learning Learn to model data using Python and libraries like Pandas, Numpy, scikit-learn, matplotlib, and seaborn. We’ll begin by looking at some of the available data sets and unsupervised techniques for exploratory data analysis. We’ll then build on this to



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