Deep Learning with Tensorflow

Description

In this course, you will learn:

• Explain the fundamental ideas of TensorFlow, such as the major functions, operations, and execution pipelines.
• Explain how TensorFlow may be used to fit curves, perform regression, classify data, and minimise error functions.
• Learn about Convolutional Networks, Recurrent Networks, and Autoencoders, as well as other forms of Deep Architectures.
• While the Neural Networks are being trained, use TensorFlow for backpropagation to fine-tune the weights and biases.

Syllabus:

1. Introduction to TensorFlow

• HelloWorld with TensorFlow
• Linear Regression
• Nonlinear Regression
• Logistic Regression
   

2. Convolutional Neural Networks (CNN)

• CNN Application
• Understanding CNNs

3. Recurrent Neural Networks (RNN)

• Intro to RNN Model
• Long Short-Term Memory (LSTM)

4. Restricted Boltzmann Machine

• Restricted Boltzmann Machine
• Collaborative Filtering with RBM

5.Autoencoders

• Introduction to Autoencoders and Applications
• Autoencoders
• Deep Belief Network

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