Acerca de este Curso
4.3
216 calificaciones
60 revisiones
100 % en línea

100 % en línea

Comienza de inmediato y aprende a tu propio ritmo.
Fechas límite flexibles

Fechas límite flexibles

Restablece las fechas límite en función de tus horarios.
Nivel principiante

Nivel principiante

Horas para completar

Aprox. 8 horas para completar

Sugerido: 9 hours/week...
Idiomas disponibles

Inglés (English)

Subtítulos: Inglés (English), Rumano

Habilidades que obtendrás

Artificial Intelligence (AI)Artificial Neural NetworkMachine LearningDeep Learning
100 % en línea

100 % en línea

Comienza de inmediato y aprende a tu propio ritmo.
Fechas límite flexibles

Fechas límite flexibles

Restablece las fechas límite en función de tus horarios.
Nivel principiante

Nivel principiante

Horas para completar

Aprox. 8 horas para completar

Sugerido: 9 hours/week...
Idiomas disponibles

Inglés (English)

Subtítulos: Inglés (English), Rumano

Programa - Qué aprenderás en este curso

Semana
1
Horas para completar
1 hora para completar

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....
Reading
5 videos (Total 34 min), 2 quizzes
Video5 videos
1.1 Future Industry Evolution & Artificial Intelligence11m
1.2 IBM Watson7m
1.3 Amazon Echo, Echo Dot, Alexa5m
1.4 LettuceBot / 1.5 Athelas / 1.6 AIVA (Artificial Intelligence Virtual Artist) / 1.7 Apple watchOS 4, HomePod speaker5m
Quiz2 ejercicios de práctica
Ungraded Quiz8m
Graded Quiz14m
Semana
2
Horas para completar
1 hora para completar

Business with Deep Learning & Machine Learning

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....
Reading
4 videos (Total 32 min), 2 quizzes
Video4 videos
2.2 Business Strategy with Machine Learning & Deep Learning8m
2.3 Why is Deep Learning Popular Now?6m
2.4 Characteristics of Businesses with DL & ML7m
Quiz2 ejercicios de práctica
Ungraded Quiz8m
Graded Quiz20m
Semana
3
Horas para completar
1 hora para completar

Deep Learning Computing Systems & Software

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....
Reading
4 videos (Total 28 min), 2 quizzes
Video4 videos
3.3 Microsoft CNTK (Cognitive Toolkit) / 3.4 NVIDIA DGX-13m
3.5 Google AlphaGo8m
3.6 ILSVRC (ImageNet Large Scale Visual Recognition Challenge)8m
Quiz2 ejercicios de práctica
Ungraded Quiz8m
Graded Quiz20m
Semana
4
Horas para completar
1 hora para completar

Basics of Deep Learning Neural Networks

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....
Reading
3 videos (Total 28 min), 2 quizzes
Video3 videos
4.2 NN (Neural Network)7m
4.3 Neural Network Learning (Backpropagation)10m
Quiz2 ejercicios de práctica
Ungraded Quiz10m
Graded Quiz20m
4.3
60 revisionesChevron Right
Dirección de la carrera

14%

comenzó una nueva carrera después de completar estos cursos
Beneficio de la carrera

83%

consiguió un beneficio tangible en su carrera profesional gracias a este curso

Principales revisiones

por RNOct 2nd 2018

Amazing lectures! Detailed description of each topic coupled with mind blowing graded assignments! :)\n\nThanks a real bunch, Coursera for offering this courses & of course, scholarship!

por IIFeb 28th 2018

Thank you for providing very clear ideas for the deep learning! With the understanding from course, we could keep going on further deep learning related topics.

Instructor

Avatar

Jong-Moon Chung

Professor, School of Electrical & Electronic Engineering
Director, Communications & Networking Laboratory

Acerca de Yonsei University

Yonsei University was established in 1885 and is the oldest private university in Korea. Yonsei’s main campus is situated minutes away from the economic, political, and cultural centers of Seoul’s metropolitan downtown. Yonsei has 3,500 eminent faculty members who are conducting cutting-edge research across all academic disciplines. There are 18 graduate schools, 22 colleges and 133 subsidiary institutions hosting a selective pool of students from around the world. Yonsei is proud of its history and reputation as a leading institution of higher education and research in Asia....

Preguntas Frecuentes

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