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.
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課程信息
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第 5 門課程（共 6 門）
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中級
完成時間大約為8 小時
建議：4-5 hours/week
英語（English）
字幕：英語（English）
提供方
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.
教學大綱 - 您將從這門課程中學到什麼
完成時間為 4 小時
Introduction
In this module, you will learn about TensorFlow, and use it to create Linear and Logistic Regression models.
完成時間為 4 小時
4 個視頻 （總計 24 分鐘）, 1 個閱讀材料, 4 個測驗
4 個視頻
Welcome2分鐘
Introduction to TensorFlow7分鐘
Introduction to Deep Learning2分鐘
Deep Neural Networks11分鐘
1 個閱讀材料
Syllabus10分鐘
1 個練習
Deep Neural Networks and TensorFlow30分鐘
完成時間為 3 小時
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.
完成時間為 3 小時
3 個視頻 （總計 22 分鐘）
3 個視頻
Convolutional Neural Networks (CNNs) for Classification4分鐘
Convolutional Neural Networks (CNNs) Architecture13分鐘
1 個練習
Convolutional Neural Networks30分鐘
完成時間為 3 小時
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.
完成時間為 3 小時
4 個視頻 （總計 22 分鐘）
4 個視頻
Recurrent Neural Networks (RNNs)5分鐘
The Long Short Term Memory (LSTM) Model5分鐘
Language Modelling7分鐘
1 個練習
Recurrent Neural Networks30分鐘
完成時間為 3 小時
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.
完成時間為 3 小時
2 個視頻 （總計 10 分鐘）
1 個練習
Restricted Boltzmann Machines30分鐘
4.3
40 條評論來自Building Deep Learning Models with TensorFlow的熱門評論
course needed to be updated for labs. Now Google moved to Tensorflow 2.0 this year.
It helped me to understand how TensorFlow can be used to build the neural networks
關於 IBM AI Engineering 專業證書
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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