Overview
The Deep Learning with Tensorflow Certificate from EdX is offered in partnership with IBMx.
Traditional neural networks rely on shallow nets, composed of one input, one hidden layer and one output layer. Deep-learning networks are distinguished from these ordinary neural networks having more hidden layers, or so-called more depth. These kind of nets are capable of discovering hidden structures within unlabeled and unstructured data (i.e. images, sound, and text), which consitutes the vast majority of data in the world.
TensorFlow is one of the best libraries to implement deep learning. TensorFlow is a software library for numerical computation of mathematical expressional, using data flow graphs. Nodes in the graph represent mathematical operations, while the edges represent the multidimensional data arrays (tensors) that flow between them. It was created by Google and tailored for Machine Learning. In fact, it is being widely used to develop solutions with Deep Learning.
In this TensorFlow course, you will learn the basic concepts of TensorFlow, the main functions, operations and the execution pipeline. Starting with a simple “Hello Word” example, throughout the course you will be able to see how TensorFlow can be used in curve fitting, regression, classification and minimization of error functions.
This concept is then explored in the Deep Learning world. You will learn how to apply TensorFlow for backpropagation to tune the weights and biases while the Neural Networks are being trained. Finally, the course covers different types of Deep Architectures, such as Convolutional Networks, Recurrent Networks and Autoencoders.
What you will learn
- 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.
Get more details
Visit programme websiteProgramme Structure
Courses include:
Module 1 – TensorFlow
- HelloWorld with TensorFlow
- Linear Regression
- Nonlinear Regression
- Logistic Regression
Module 2 – Convolutional Neural Networks (CNN)
- CNN Application
- Understanding CNNs
Module 3 – Recurrent Neural Networks (RNN)
- RNN Model
- Long Short-Term memory (LSTM)
Module 4 - Restricted Boltzmann Machine
- Restricted Boltzmann Machine
- Collaborative Filtering with RBM
Module 5 - Autoencoders
- Autoencoders and Applications
- Autoencoders
- Deep Belief Network
Check out the full curriculum
Visit programme websiteKey information
Duration
- Part-time
- 2 months
- 2 hrs/week
Start dates & application deadlines
Language
Delivered
- Self-paced
Disciplines
Human Computer Interaction Artificial Intelligence View 147 other Short Courses in Artificial Intelligence in United StatesExplore more key information
Visit programme websiteAcademic requirements
We are not aware of any specific GRE, GMAT or GPA grading score requirements for this programme.
English requirements
We are not aware of any English requirements for this programme.
Other requirements
General requirements
Prerequisites:
- Python & Jupyter notebooks
- Machine Learning concepts
- Deep Learning concepts
Make sure you meet all requirements
Visit programme websiteTuition Fee
-
International
99 USD/fullTuition FeeBased on the tuition of 99 USD for the full programme during 2 months. -
National
99 USD/fullTuition FeeBased on the tuition of 99 USD for the full programme during 2 months.
- Add a Verified Certificate for $99 USD
- Limited access:free