Deep Learning & Artificial Intelligence with Apache Spark
- Created By raju2006
- Last Updated November 24th, 2023
- Overview
- Prerequisites
- Audience
- Audience
- Curriculum
Description:
This course teaches doing Deep Learning and Artificial Intelligence at Scale with the popular Apache Spark framework. Get hands-on experience and dive deep into world of Deep Learning and Artificial Intelligence. This Deep Learning & Artificial Intelligence With Apache Spark course is intended for data scientists and software engineers. We assume no previous knowledge of Machine Learning – We teach popular Deep Learning and Artificial Intelligence Algorithms from scratch. Please note the following: This is an intermediate to advance course An In-depth coverage of Math / Stats behind Machine Learning is beyond the scope of this course. Course is taught using Spark & Python. Python Programming knowledge is essential
Course Code/Duration:
BDT253 / 3 – Days
Learning Objectives:
In this course, participants will:
- Describe the role of Deep Learning & AI and where it fits into Information Technology strategies
- Explain the technical and business drivers that result from using Deep Learning & AI
- Discuss how to identify which kinds of technique to be applied for specific use case
- Understand the popular Deep Learning & AI offerings like Amazon Machine Learning, TensorFlow, Azure Machine Learning, Spark mlib, Python and R etc.
- Install and Setup Anaconda
- Perform hands-on activity using Jupyter Notebooks
- Install TensorFlow
- Understand deep learning in the context of machine learning and AI
- Understand neural networks
- Understand the architectural differences between shallow and deep neural networks
- Understand deep Convolutional and Recurrent Neural Networks
- Understand use cases for Convolutional Neural Networks
- Understand use cases for Recurrent Neural Networks
- Understand the relationship between TensorFlow and Keras for applying deep learning
- Understand Apache Spark Deep Learning offerings in the industry
- Understand Apache Spark AI offerings in the industry
- Python Programming knowledge is essential
- Machine Learning with Apache Spark Course or its equivalent
- This course is designed for anyone interested to get started with the domain of Machine Learning and Artificial Intelligence including Data Analysts, Data Engineers,DevOps Engineer, Database Professional, Software Engineers, or Quality Assurance Engineers.
- This course is designed for anyone interested to get started with the domain of Machine Learning and Artificial Intelligence including Data Analysts, Data Engineers,DevOps Engineer, Database Professional, Software Engineers, or Quality Assurance Engineers.
Course Outline:
Spark ML Overview
- Course Introduction
- History and background of Machine Learning
- Compare Traditional Programming Vs Machine Leaning
- Supervised and Unsupervised Learning Overview
- Brief discussion of the Spark ML Libraries
AI Overview
- Types of AI systems
- Deep Learning applications
- Compare AI vs ML vs DL
Aritificial Neural Networks
- Perceptrons – Single and Multilayer
- Activation functions
- Softmax
- Back propagation, loss function and Gradient Descent
- Handls-on lab
Introduction to Deep Learning
- Hidden layers in deep learning
- Forward and Backward propogation
- Distributed training with tensorflow
- Vanishing Gradient and ReLU
- Hands-on Lab
Graph Frame and Graph Processing
- Understanding GraphX, GraphFrames in Spark
- Using neo4j and graph databases
- Hands-on lab
TensorFlow and Keras
- Using TensorFlow and Keras
- Developing model with tensor flow
Convolutional Neural Network
- Introduction to CNN
- Image processing
- Using CNN with Spark
- Hands-on lab
Recurrent Neural Networks
- Introducing RNN
- RNN in Tensorflow
- Hands-on lab
Spark Natural Language processing
- Spark-NLP library
- TF-IDF
- Tokenizers, n-grams
- Word2Vec
- Stopword removal
Save and Load model
- Save model and weight
- Load model and weigh
- Save and load pipelines
Training material provided: Yes (Digital format)
The curriculum is empty
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