What Will You 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.
About This Course
Provider: Edx
Format: Online
Duration: 20 hours to complete [Approx]
Target Audience: Intermediate
Learning Objectives: By the enf of this free course you will be able to see how TensorFlow can be used in curve fitting, regression, classification and minimization of error functions.
Course Prerequisites: Python & Jupyter notebooks, Machine Learning concepts, Deep Learning concepts
Assessment and Certification: Earn a Certificate upon completion from the relevant Provider
Instructor: IBM
Key Topics: Numerical Analysis, Nodes (Networking), Unstructured Data, Artificial Neural Networks, Curve Fitting, Machine Learning, TensorFlow, Deep Learning, Dataflow
Topic Covered:
- - Introduction to TensorFlow
- - Linear Regression
- - Nonlinear Regression
- - Logistic Regression
- - Convolutional Neural Networks (CNN)
- - CNN Application
- - Understanding CNNs
- - Recurrent Neural Networks (RNN)
- - Intro to RNN Model
- - Long Short-Term memory (LSTM)
- - Restricted Boltzmann Machine
- - Restricted Boltzmann Machine
- - Collaborative Filtering with RBM
- - Autoencoders
- - Introduction to Autoencoders and Applications
- - Autoencoders
- - Deep Belief Network
0 Comments