Overview of Unsupervised Approaches in Deep Learning - Unsupervised Approaches in Deep Learning | Coursera

Overview of Unsupervised Approaches in Deep Learning

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Universidad de Colorado en Boulder

Introduction to Deep Learning

Universidad de Colorado en Boulder

Curso 3 de 3 en Machine Learning: Theory and Hands-on Practice with Python Programa Especializado

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Deep Learning is the go-to technique for many applications, from natural language processing to biomedical. Deep learning can handle many different types of data such as images, texts, voice/sound, graphs and so on. This course will cover the basics of DL including how to build and train multilayer perceptron, convolutional neural networks (CNNs), recurrent neural networks (RNNs), autoencoders (AE) and generative adversarial networks (GANs). The course includes several hands-on projects, including cancer detection with CNNs, RNNs on disaster tweets, and generating dog images with GANs. Prior coding or scripting knowledge is required. We will be utilizing Python extensively throughout the course. We recommend taking the two previous courses in the specialization, Introduction to Machine Learning: Supervised Learning and Unsupervised Algorithms in Machine Learning, but they are not required. College-level math skills, including Calculus and Linear Algebra, are needed. Some parts of the class will be relatively math intensive. This course can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder. Course logo image by Ryan Wallace on Unsplash.

Destrezas que aprenderás

Deep Learning, Artificial Neural Network, Convolutional Neural Network, Unsupervised Deep Learning, Recurrent Neural Network

De la lección

Unsupervised Approaches in Deep Learning

This module will focus on neural network models trained via unsupervised Learning. We will cover autoencoders and GAN as examples. We will consider the famous AI researcher Yann LeCun's cake analogy for Reinforcement Learning, Supervised Learning, and Unsupervised Learning. Supervised Deep Learning has had tremendous success, mainly due to the availability of massive datasets like ImageNet. However, it is expensive and challenging to obtain labeled data for areas like biomedical images. There is great motivation to continue developing unsupervised Deep Learning approaches to harness abundant unlabeled data sources. This week is the last week of new course material. There is no quiz or Jupyter notebook lab. Generative adversarial networks (GANs) learn to generate new data with the same statistics as the training set. This week, you will wrap up one final Kaggle mini-project. This time, you will experiment with creating a network to generate images of puppies.

Overview of Unsupervised Approaches in Deep Learning 10:40

Impartido por:

Geena Kim
Assistant Teaching Professor

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