Deep Learning for Business

Go to Course: https://www.coursera.org/learn/deep-learning-business

Introduction

### Course Review: Deep Learning for Business In a world increasingly dominated by artificial intelligence and machine learning applications, the "Deep Learning for Business" course on Coursera stands out as an essential resource for anyone looking to leverage these technologies to enhance their corporate strategies. Offered by esteemed instructors with relevant industry experience, this course provides a comprehensive introduction to the core concepts of deep learning (DL) and machine learning (ML), making it ideal for professionals who wish to understand how these technologies can transform their businesses. #### Course Overview The course is segmented into three main parts, each designed to build upon the knowledge gained in the previous section. The first module introduces the foundational products and services powered by DL and ML. Here, learners explore notable technologies such as IBM Watson and Amazon Alexa, gaining insight into how these applications function and their implications for industries moving forward. Not only does this module cover existing technologies, but it also speculates on future innovations that are set to redefine our interaction with AI. #### Module Breakdown 1. **Deep Learning Products & Services** - This module kicks off with the context of AI within various industries and goes on to dissect high-impact use cases such as automated agricultural technologies and medical diagnostics. Critics and proponents alike will find value in the in-depth analysis of tools like LettuceBot and Athelas, which exemplify the real-world benefits of DL technologies. 2. **Business with Deep Learning & Machine Learning** - Here, the course addresses strategic business considerations in the AI era. It provides a framework on how to prepare for the ML-driven future by discussing business modeling and strategy adaptation. The insights on why DL is surging in popularity—most notably due to technological advancements—are particularly eye-opening, making this module a vital segment for leaders looking to stay ahead of the curve. 3. **Deep Learning Computing Systems & Software** - This module dives into the technological backbone of DL, featuring software like TensorFlow and Keras. By the end of this section, you should have a solid foundation in the software tools that power DL, empowering you to make informed decisions regarding their implementation in your business. 4. **Basics of Deep Learning Neural Networks** - The course takes a more technical turn in this section, offering a deep dive into neural networks, including their structure and function. Understanding these baselines is critical for anyone intending to delve into advanced deep learning topics. 5. **Deep Learning with CNN & RNN** - By focusing on Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), this module illustrates how DL applies in various domains, from image processing to natural language processing. This understanding is crucial for professionals seeking to utilize DL for competitive advantage. 6. **Deep Learning Project with TensorFlow Playground** - The hands-on project portion allows students to apply their knowledge creatively and practically. Through the TensorFlow Playground, learners engage in real-world challenges, helping to solidify their understanding of neural network design principles. #### Pros and Cons **Pros:** - Comprehensive Material: Covers both theoretical foundations and practical applications. - Expert Instructors: The course boasts instructors who bring real-world experience and knowledge. - Flexible Learning: The online format allows for self-paced learning, accommodating busy professionals. - Hands-On Projects: Practical experience with TensorFlow adds substantial value. **Cons:** - Technical Depth: Some beginners might find the technical sections challenging without prior programming or data science knowledge. - Limited Real-Time Interaction: While the course materials are excellent, the online format limits direct interaction with instructors. #### Conclusion and Recommendation The "Deep Learning for Business" course on Coursera is a treasure trove of knowledge for business professionals keen on integrating deep learning technologies into their organizations. Whether you own a startup or are leading a large enterprise, understanding the principles of DL and ML can provide you with a competitive edge in your industry. I highly recommend this course to business leaders, entrepreneurs, and professionals who are willing to embrace the future of technology. As we move deeper into the era of AI, having a firm grasp of both strategic and technical components will be invaluable. Enroll today and equip yourself with the skills needed to lead your business successfully into the future of AI.

Syllabus

Deep Learning Products & Services

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.

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.

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.

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.

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.

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.