The majority of data in the world is unlabeled and unstructured. Shallow neural networks cannot easily capture relevant structure in, for instance, images, sound, and textual data. Deep networks are capable of discovering hidden structures within this type of data. In this course you’ll use TensorFlow library to apply deep learning to different data types in order to solve real world problems.
Этот курс входит в специализацию ''Профессиональная сертификация 'IBM AI Engineering''
Building Deep Learning Models with TensorFlow
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Курс 5 из 6 в программе
Гибкие сроки
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Промежуточный уровень
Прибл. 8 часа на выполнение
Предполагаемая нагрузка: 4-5 hours/week
Английский
Субтитры: Английский
Программа курса: что вы изучите
4 ч. на завершение
Introduction
In this module, you will learn about TensorFlow, and use it to create Linear and Logistic Regression models.
4 ч. на завершение
4 видео ((всего 24 мин.)), 1 материал для самостоятельного изучения, 4 тестов
4 видео
Welcome2мин
Introduction to TensorFlow7мин
Introduction to Deep Learning2мин
Deep Neural Networks11мин
1 материал для самостоятельного изучения
Syllabus10мин
1 практическое упражнение
Deep Neural Networks and TensorFlow30мин
3 ч. на завершение
Supervised Learning Models
In this module, you will learn about about Convolutional Neural Networks, and the building blocks of a convolutional neural network, such as convolution and feature learning. You will also learn about the popular MNIST database. Finally, you will learn how to build a Multi-layer perceptron and convolutional neural networks in Python and using TensorFlow.
3 ч. на завершение
3 видео ((всего 22 мин.))
3 видео
Convolutional Neural Networks (CNNs) for Classification4мин
Convolutional Neural Networks (CNNs) Architecture13мин
1 практическое упражнение
Convolutional Neural Networks30мин
3 ч. на завершение
Supervised Learning Models (Cont'd)
In this module, you will learn about the recurrent neural network model, and special type of a recurrent neural network, which is the Long Short-Term Memory model. Also, you will learn about the Recursive Neural Tensor Network theory, and finally, you will apply recurrent neural networks to language modelling.
3 ч. на завершение
4 видео ((всего 22 мин.))
4 видео
Recurrent Neural Networks (RNNs)5мин
The Long Short Term Memory (LSTM) Model5мин
Language Modelling7мин
1 практическое упражнение
Recurrent Neural Networks30мин
3 ч. на завершение
Unsupervised Deep Learning Models
In this module, you will learn about the applications of unsupervised learning. You will learn about Restricted Boltzmann Machines (RBMs), and how to train an RBM. Finally, you will apply Restricted Boltzmann Machines to build a recommendation system.
3 ч. на завершение
2 видео ((всего 10 мин.))
1 практическое упражнение
Restricted Boltzmann Machines30мин
Рецензии
4.3
Лучшие отзывы о курсе BUILDING DEEP LEARNING MODELS WITH TENSORFLOW
от партнера PDJan 4, 2020
Very clear explanation and well organized course. I give 4 stars because videos of Week 5 are missing the audio and subtitles.
от партнера MKNov 21, 2019
Good content. A bit too fast on some complex concepts and missing audio for the last lecture but great lecturer.
от партнера PCMar 31, 2020
The coding part was hard to understand. If that part could also be covered in videos as a tutorial.
от партнера LDNov 5, 2019
course needed to be updated for labs. Now Google moved to Tensorflow 2.0 this year.
от партнера SAFeb 3, 2020
It helped me to understand how TensorFlow can be used to build the neural networks
от партнера NKDec 30, 2019
Week 5 lecture video no audio\n\nLab is not update for tensorflow 2
от партнера JAJan 22, 2020
Teaches more on Deep Learning models but less in TensorFlow
от партнера TSMar 1, 2020
Thank a lot for this course! It quick and really useful.
от партнера TZMar 23, 2020
Very informative, could use some more room for practice.
О IBM
IBM offers a wide range of technology and consulting services; a broad portfolio of middleware for collaboration, predictive analytics, software development and systems management; and the world's most advanced servers and supercomputers. Utilizing its business consulting, technology and R&D expertise, IBM helps clients become "smarter" as the planet becomes more digitally interconnected. IBM invests more than $6 billion a year in R&D, just completing its 21st year of patent leadership. IBM Research has received recognition beyond any commercial technology research organization and is home to 5 Nobel Laureates, 9 US National Medals of Technology, 5 US National Medals of Science, 6 Turing Awards, and 10 Inductees in US Inventors Hall of Fame.
Профессиональная сертификация 'IBM AI Engineering': общие сведения
The rapid pace of innovation in Artificial Intelligence (AI) is creating enormous opportunity for transforming entire industries and our very existence. After competing this comprehensive 6 course Professional Certificate, you will get a practical understanding of Machine Learning and Deep Learning.
You will master fundamental concepts of Machine Learning and Deep Learning, including supervised and unsupervised learning. You will utilize popular Machine Learning and Deep Learning libraries such as SciPy, ScikitLearn, Keras, PyTorch, and Tensorflow applied to industry problems involving object recognition and Computer Vision, image and video processing, text analytics, Natural Language Processing, recommender systems, and other types of classifiers.
You will be able to scale Machine Learning on Big Data using Apache Spark. You will build, train, and deploy different types of Deep Architectures, including Convolutional Networks, Recurrent Networks, and Autoencoders.
By the end of this Professional Certificate, you will have completed several projects showcasing your proficiency in Machine Learning and Deep Learning, and become armed with skills for a career as an AI Engineer.
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