Deep Learning for Business

所在平台: Coursera

课程主页: https://www.coursera.org/learn/deep-learning-business

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课程简介

课程名称：深度学习与商业 课程概述：随着AI（人工智能）技术的迅速发展，深度学习（DL）和机器学习（ML）正被运用到日常生活及各个行业中。本课程旨在帮助学员理解DL和ML的基础知识及其在商业中的应用，以应对未来商业战略的变化。课程分为三个部分，分别聚焦于未来商业策略、DL和ML的核心技术，以及使用TensorFlow Playground进行项目实践。 课程大纲： 1. **深度学习产品与服务**：本模块探讨未来行业的演变及AI应用，介绍IBM Watson、Amazon Echo、LettuceBot、Athelas等热门DL和ML产品及服务，以及苹果的watchOS 4和HomePod。 2. **深度学习与机器学习的商业应用**：分析DL和ML对商业的影响，讨论如何调整商业策略，以更好地利用这两种技术的优势，包括模型建立与调整的实例。 3. **深度学习计算系统与软件**：介绍主流的DL和ML系统与开源软件，如TensorFlow和CNTK，并探讨NVIDIA的超级计算机DGX-1及其在DL领域的领先地位。 4. **深度学习神经网络基础**：阐述人工智能、机器学习和深度学习的技术差异，介绍神经元模型、人工神经网络及相关学习方法，包括监督学习和强化学习。 5. **CNN与RNN的深度学习**：聚焦卷积神经网络（CNN）和递归神经网络（RNN）的技术，探讨其在图像识别、语音识别等领域的应用，以及如何提升效率和准确性。 6. **使用TensorFlow Playground进行深度学习项目**：通过四个神经网络设计项目，学员将学习如何利用TensorFlow Playground进行DL网络的设计与挑战，综合应用前面模块的知识。 本课程将帮助您制定商业策略，并为新兴的DL和ML服务及产品进行技术规划。

课程大纲

Name:Deep Learning Products & Services

Description:For the course “Deep Learning for Business,” the first module is “Deep Learning Products & Services,” which starts with the lecture “Future Industry Evolution & Artificial Intelligence” that explains past, current, and future industry evolutions and how DL (Deep Learning) and ML (Machine Learning) technology will be used in almost every aspect of future industry in the near future. The following lectures look into the hottest DL and ML products and services that are exciting the business world. First, the “Jeopardy!” winning versatile IBM Watson is introduced along with its DeepQA and AdaptWatson systems that use DL technology. Then the Amazon Echo and Echo Dot products are introduced along with the Alexa cloud based DL personal assistant that uses ASR (Automated Speech Recognition) and NLU (Natural Language Understanding) technology. The next lecture focuses on LettuceBot, which is a DL system that plants lettuce seeds with automatic fertilizer and herbicide nozzles control. Then the computer vision based DL blood cells analysis diagnostic system Athelas is introduced followed by the introduction of a classical and symphonic music composing DL system named AIVA (Artificial Intelligence Virtual Artist). As the last topic of module 1, the upcoming Apple watchOS 4 and the HomePod speaker that was presented at Apple's 2017 WWDC (World Wide Developers Conference) is introduced.

Name:Business with Deep Learning & Machine Learning

Description:The second module “Business with Deep Learning & Machine Learning” first focuses on various business considerations based on changes to come due to DL (Deep Learning) and ML (Machine Learning) technology in the lecture “Business Considerations in the Machine Learning Era.” In the following lecture “Business Strategy with Machine Learning & Deep Learning” explains the changes that are needed to be more successful in business, and provides an example of business strategy modeling based on the three stages of preparation, business modeling, and model rechecking & adaptation. The next lecture “Why is Deep Learning Popular Now?” explains the changes in recent technology and support systems that enable the DL systems to perform with amazing speed, accuracy, and reliability. The last lecture “Characteristics of Businesses with DL & ML” first explains DL and ML based business characteristics based on data types, followed by DL & ML deployment options, the competitive landscape, and future opportunities are also introduced.

Name:Deep Learning Computing Systems & Software

Description:The third module “Deep Learning Computing Systems & Software” focuses on the most significant DL (Deep Learning) and ML (Machine Learning) systems and software. Except for the NVIDIA DGX-1, the introduced DL systems and software in this module are not for sale, and therefore, may not seem to be important for business at first glance. But in reality, the companies that created these systems and software are indeed the true leaders of the future DL and ML business era. Therefore, this module introduces the true state-of-the-art level of DL and ML technology. The first lecture introduces the most popular DL open source software TensorFlow, CNTK (Cognitive Toolkit), Keras, Caffe, Theano, and their characteristics. Due to their popularly, strong influence, and diverse capabilities, the following lectures introduce the details of Google TensorFlow and Microsoft CNTK. Next, NVIDIA’s supercomputer DGX-1, that has fully integrated customized DL hardware and software, is introduced. In the following lectures, the most interesting competition of human versus machine is introduced in the Google AlphaGo lecture, and in the ILSVRC (ImageNet Large Scale Visual Recognition Challenge) lecture, the results of competition between cutting edge DL systems is introduced and the winning performance for each year is compared.

Name:Basics of Deep Learning Neural Networks

Description:The module “Basics of Deep Learning Neural Networks” first focuses on explaining the technical differences of AI (Artificial Intelligence), ML (Machine Learning), and DL (Deep Learning) in the first lecture titled “What is DL (Deep Learning) and ML (Machine Learning).” In addition, the characteristics of CPUs (Central Processing Units) and GPUs (Graphics Processing Units) used in DL as well as the representative computer performance units of FLOPS (FLoating-Point Operations Per Second) and IPS (Instructions Per Second) are introduced. Next, in the NN (Neural Network) lecture, the biological neuron (nerve cell) and its signal transfer is introduced followed by an ANN (Artificial Neural Network) model of a neuron based on a threshold logic unit and soft output activation functions is introduced. Then the extended NN technologies that uses MLP (Multi-Layer Perceptron), SoftMax, and AutoEncoder are explained. In the last lecture of the module, NN learning based on backpropagation is introduced along with the learning method types, which include supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning.

Name:Deep Learning with CNN & RNN

Description:The module “Deep Learning with CNN & RNN” focuses on CNN (Convolutional Neural Network) and RNN (Recurrent Neural Network) technology that enable DL (Deep Learning). First the lectures introduce how CNNs used in image/video recognition, recommender systems, natural language processing, and games (like Chess and Go) are made possible through processing in the convolutional layer and feature maps. The lecture also introduces how CNNs use subsampling (pooling), LCN (Local Contrast Normalization), dropout, ensemble, and bagging technology to become more efficient, reliable, robust, and accurate. Next, the lectures introduce how DL with RNN is used in speech recognition (as in Apple's Siri, Google’s Voice Search, and Samsung's S Voice), handwriting recognition, sequence data analysis, and program code generation. Then the details of RNN technologies are introduced, which include S2S (Sequence to Sequence) learning, forward RNN, backward RNN, representation techniques, context based projection, and representation with attention. As the last part of the module, the early model of RNN, which is the FRNN (Fully Recurrent NN), and the currently popular RNN model LSTM (Long Short-Term Memory) is introduced.

Name:Deep Learning Project with TensorFlow Playground

Description:The module “Deep Learning Project with TensorFlow Playground” focuses on four NN (Neural Network) design projects, where experience on designing DL (Deep Learning) NNs can be gained using a fun and powerful application called the TensorFlow Playground. The lectures first teach how to use the TensorFlow Playground, which is followed by guidance on three projects so you can easily buildup experience on using the TensorFlow Playground system. Then in Project 4 a “DL NN Design Challenge” is given, where you will need to make the NN “Deeper” by adding hidden layers and neurons to satisfy the classification objectives. The knowledge you obtained in the lecture of Modules 1~5 will be used in these projects.

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