IBM - Building Deep Learning Models with TensorFlow
- Offered byCoursera
Building Deep Learning Models with TensorFlow at Coursera Overview
Duration | 13 hours |
Start from | Start Now |
Total fee | Free |
Mode of learning | Online |
Difficulty level | Intermediate |
Official Website | Explore Free Course  |
Credential | Certificate |
Building Deep Learning Models with TensorFlow at Coursera Highlights
- Shareable Certificate Earn a Certificate upon completion
- 100% online Start instantly and learn at your own schedule.
- Course 5 of 6 in the IBM AI Engineering
- Flexible deadlines Reset deadlines in accordance to your schedule.
- Intermediate Level
- Approx. 13 hours to complete
- English Subtitles: French, Portuguese (European), Russian, English, Spanish
Building Deep Learning Models with TensorFlow at Coursera Course details
- 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.
- Learning Outcomes:
- After completing this course, learners will be able to:
- ? explain foundational TensorFlow concepts such as the main functions, operations and the execution pipelines.
- ? describe how TensorFlow can be used in curve fitting, regression, classification and minimization of error functions.
- ? understand different types of Deep Architectures, such as Convolutional Networks, Recurrent Networks and Autoencoders.
- ? apply TensorFlow for backpropagation to tune the weights and biases while the Neural Networks are being trained.
Building Deep Learning Models with TensorFlow at Coursera Curriculum
Introduction
Welcome
Introduction to TensorFlow
TensorFlow 2.x and Eager Execution
Introduction to Deep Learning
Deep Neural Networks
Syllabus
Deep Neural Networks and TensorFlow
Supervised Learning Models
Introduction to Convolutional Neural Networks (CNNs)
Convolutional Neural Networks (CNNs) for Classification
Convolutional Neural Networks (CNNs) Architecture
Convolutional Neural Networks
Supervised Learning Models (Cont'd)
The Sequential Problem
Recurrent Neural Networks (RNNs)
The Long Short Term Memory (LSTM) Model
Language Modelling
Recurrent Neural Networks
Unsupervised Deep Learning Models
Introduction to Restricted Boltzmann Machines
Restricted Boltzmann Machines (RBMs)
Restricted Boltzmann Machines
Unsupervised Deep Learning Models (Cont'd) and scaling
Introduction to Autoencoders
Autoencoders
Scaling of neural networks
Autoencoders
Building Deep Learning Models with TensorFlow at Coursera Admission Process
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