Introduction to Deep Learning

所在平台: Coursera

课程主页: https://www.coursera.org/learn/introduction-to-deep-learning-boulder

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课程简介

课程名称：深度学习导论 课程概述：深度学习是众多应用的首选技术，涉及自然语言处理、生物医学等多个领域。它能够处理图片、文本、语音、图形等多种数据类型。本课程将涵盖深度学习的基础知识，包括多层感知器、卷积神经网络（CNN）、递归神经网络（RNN）、自编码器（AE）和生成对抗网络（GAN）的构建和训练。课程中包含多个实践项目，包括使用CNN进行癌症检测、利用RNN分析灾难推文以及用GAN生成狗的图像。 课程要求：需具备一定的编程或脚本知识，课程将广泛使用Python。建议先修阅读机器学习入门的两门课程，但并不是强制要求。课程需要大学水平的数学基础，包括微积分和线性代数，部分内容将涉及较多数学推导。 该课程可作为科罗拉多大学博尔德分校数据科学硕士学位下的学分课程。该学位课程是一个跨学科的项目，汇集了应用数学、计算机科学、信息科学等多个系的教职工。该学位具有基于表现的录取系统，没有申请流程，适合具有广泛计算机科学、信息科学、数学和统计等背景的个人。 课程大纲： 1. **深度学习导论与多层感知器** 学习深度学习在自动驾驶、医学成像和机器人方面的应用，了解人工神经元（感知器）以及训练简单感知器和复杂的多层感知器的方法，掌握反向传播算法以及深度学习库如何创建计算图。 2. **神经网络的训练** 学习优化方法，从随机梯度下降（SGD）开始，了解设计参数并探索更高级的梯度下降方法，掌握减少过拟合的技巧，如正则化技术。 3. **图像深度学习** 学习卷积神经网络（CNN），适用于图像分析任务，并参与与癌症相关的Kaggle挑战项目，建立模型，分类数字病理图像。 4. **序列数据的深度学习** 学习递归神经网络（RNN）的结构，解决序列数据问题，讨论渐消和爆炸梯度问题，并进行与灾难推文相关的Kaggle项目。 5. **深度学习中的无监督方法** 探讨自编码器和生成对抗网络（GAN）等无监督学习的模型，进行图像生成项目，结束新的课程内容。 这门课程将提供全面的深度学习基础，与实际项目结合，使学习者能够掌握深度学习的核心技术。

课程大纲

Name:Deep Learning Introduction, Multilayer Perceptron

Description:We are starting off the course with a busy week. This week's module has two parts. In the first part, after a quick introduction to Deep Learning's exciting applications in self-driving cars, medical imaging, and robotics, we will learn about artificial neurons called perceptrons. Interestingly, neural networks are loosely modeled on the human brain with perceptrons mimicking neurons. After we learn to train a simple perceptron (and become aware of its limitations), we will move on to more complex multilayer perceptrons. The second part of the module introduces the backpropagation algorithm, which trains a neural network through the chain rule. We will finish by learning how deep learning libraries like Tensorflow create computation graphs for gradient computation. This week, you will have two short quizzes, a Jupyter lab programming assignment, and an accompanying Peer Review assignment. This material, notably the backpropagation algorithm, is so foundational to Deep Learning that it is essential to take the time necessary to work through and understand it.

Name: Training Neural Networks

Description:Last week, we built our Deep Learning foundation, learning about perceptrons and the backprop algorithm. This week, we are learning about optimization methods. We will start with Stochastic Gradient Descent (SGD). SGD has several design parameters that we can tweak, including learning rate, momentum, and decay. Then we will turn our attention to advanced gradient descent methods like learning rate scheduling and Nesterov momentum. Besides vanilla gradient descent, other optimization algorithms include AdaGrad, AdaDelta, RMSprop, and Adam. We will cover general tips to reduce overfitting while training neural networks, including regularization methods like dropout and batch normalization. This week, you will build your DL toolkit, gaining experience with the Python library Keras. Assessments for the week include a quiz and a Jupyter lab notebook with an accompanying Peer Review. This assignment is your last Jupyter lab notebook for the course. For the next three weeks, you will build hands-on experience and complete weekly mini-projects that incorporate Kaggle challenges.

Name:Deep Learning on Images

Description:This module will teach a type of neural network called convolutional neural networks, suitable for image analysis tasks. We will learn about definitions, design parameters, operations, hyperparameter tuning, and applications. There is no Jupyter lab notebook this week. You will have a brief quiz and participate in a clinically relevant Kaggle challenge mini-project. It is critical to evaluate whether cancer has spread to the sentinel lymph node for staging breast cancer. You will build a CNN model to classify whether digital pathology images show that cancer has spread to the lymph nodes. This project utilizes the PCam dataset, which has an approachable size, with the authors noting that "Models can easily be trained on a single GPU in a couple of hours, and achieve competitive scores." As you prepare for the week, look over the rubric and develop a plan for how you will complete it. It will be necessary for a project like this to work on a timeframe that allows you to run experiments. The expectation is not that you will cram the equivalent of a final project into a single week or that you need to have a top leaderboard score to receive a good grade for this project. Hopefully, you will have time to achieve some exciting results to show off in your portfolio.

Name:Deep Learning on Sequential Data

Description:This module will teach you another neural network called recurrent neural networks (RNNs) to handle sequential data. So far, we have covered feed-forward neural networks, including Multi-layer Perceptrons and CNNs. However, in biological systems, information can flow backward and forwards. RNNs do a backward pass closer to biological systems. Using RNNs has excellent benefits, especially for text data, since RNN architectures reduce the number of parameters. We will learn about the vanishing and exploding gradient problems that can arise when working with vanilla RNNs and remedies for those problems, including GRU and LSTM cells. We don't have a quiz this week, but we have a Kaggle challenge mini-project on NLP with Disaster Tweets. The project is a Getting Started competition designed for learners building their machine learning background. The challenge is very doable in a week, but make sure to start early to run experiments and iterate a bit.

Name:Unsupervised Approaches in Deep Learning

Description: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.

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