Deep Learning
Overview
Deep learning conditions 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 subcategory of Machine Learning techniques that aim to achieve Artificial Intelligence. The unique feature of Deep Learning is its usage of various Artificial Neural Networks (ANN), that imitate the human 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 just as in the human brain. After the completion of this course participant will learn the foundations of Deep Learning they will also understand how to build neural networks, and also learn how to lead successful machine learning projects.Duration
4 Days
Pre-Requisites
Basic working knowledge on Python
Course Outline
- What is deep learning?
- How it is different from Machine Learning?
- Deep Learning – Use cases
- Packages and libraries available for implementing Deep learning
- Where does Deep Leaning fit into Data Science Ecosystem?
- Quick Review of Machine Learning
- How Deep Learning Works?
- Activation Functions
- Illustrate Perceptron
- Training a Perceptron
- Important Parameters of Perceptron
- What is TensorFlow?
- TensorFlow code-basics
- Graph Visualization
- Constants, Placeholders, Variables
- Creating a Model
- Step by Step – Use-Case Implementation
- Understand limitations of A Single Perceptron
- Understand Neural Networks in Detail
- Illustrate Multi-Layer Perceptron
- Backpropagation – Learning Algorithm
- Understand backpropagation using Neural Network Example
- MLP Digit-Classifier using TensorFlow
- Tensor Board
- Summary
- Installation & setup Python IDE – Anaconda
- NumPy
- SciPy
- Pandas,
- Matplotlib
- SciKit-Learn
- NLTK
- Introduction to CNNs
- CNNs Application
- Architecture of a CNN
- Convolution and pooling layers in a CNN
- Understanding and visualizing a CNN
- Transfer learning and fine-tuning convolutional neural networks
- Intro to RNN Model
- Application use cases of RNN
- Modelling sequences
- Training RNNs with Backpropagation
- Long Short-Term memory (LSTM)
- Recursive Neural Tensor Network Theory
- Recurrent Neural Network Model
- Restricted Boltzmann Machine
- Applications of RBM
- Collaborative Filtering with RBM
- Introduction to Autoencoders
- Autoencoders applications
- Understanding Autoencoders
- 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 model with TFlearn
- Customizing the Training Process
- Using Tensor Board with TFlearn
- Use-Case Implementation with TFlearn
- Sample Capstone Project
- Deep Learning AMIs available
- Image Recognition API
- Common Practice to setup Deep Learning Project in cloud