Course Overview
Deep learning states to a set of techniques by which we can achieve varying degrees of artificial intelligence by representing the working of a human brain
Deep learning is a subset of Machine Learning techniques that aim to achieve Artificial Intelligence. The unique feature of Deep Learning is its usage of various Artificial Neural Networks, that replicate the human brain
Just as in the brain, Artificial Neural Networks or ANNs also consist of neurons and synapses among them Deep Learning is one of the most highly desirable skills in AI
Post completion of this course participant will learn the foundations of Deep Learning, they will understand how to build neural networks, and also learn how to lead successful machine learning projects
Requirements
- A working knowledge of Python
Curriculum
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Convolution Neural Networks (CNN)
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Recurrent Neural Network (RNN)
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Introduction
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Neural networks with Tensor flow
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Neural networks with Tensor flow - Advanced
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Python libraries for Data science
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RBM and Autoencoders
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Keras & TFlearn
- Define Keras
- How to compose Models in Keras
- Sequential Composition
- Functional Composition
- Predefined Neural Network Layers
- What is Batch Normalization
- Saving and Loading a model with Keras
- Customizing the Training Process
- Using TensorBoard with Keras
- Use-Case Implementation with Keras
- Define TFlearn
- Composing Models in TFlearn
- Sequential Composition
- Functional Composition
- Predefined Neural Network Layers
- What is Batch Normalization?
- Saving and Loading a model with TFlearn
- Customizing the Training Process
- Using Tensor Board with TFlearn
- Use-Case Implementation with TFlearn
- Sample Capstone Project
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Cloud - Deep Learning with Amazon Web Service
