Building iOS Question Answering App with BERT
Building iOS Question Answering App with BERT, available at $54.99, has an average rating of 4.56, with 24 lectures, based on 8 reviews, and has 69 subscribers.
You will learn about Learn to develop machine learning based question answering iOS application Learn to convert speech into text Learn to convert text into speech Learn state of the art Natural Language Processing language modeling technology called BERT This course is ideal for individuals who are Beginner iOS developers who would like to build mobile apps using deep learning It is particularly useful for Beginner iOS developers who would like to build mobile apps using deep learning.
Enroll now: Building iOS Question Answering App with BERT
Summary
Title: Building iOS Question Answering App with BERT
Price: $54.99
Average Rating: 4.56
Number of Lectures: 24
Number of Published Lectures: 24
Number of Curriculum Items: 24
Number of Published Curriculum Objects: 24
Original Price: $22.99
Quality Status: approved
Status: Live
What You Will Learn
- Learn to develop machine learning based question answering iOS application
- Learn to convert speech into text
- Learn to convert text into speech
- Learn state of the art Natural Language Processing language modeling technology called BERT
Who Should Attend
- Beginner iOS developers who would like to build mobile apps using deep learning
Target Audiences
- Beginner iOS developers who would like to build mobile apps using deep learning
This course teaches you step by step on how tp build iOS question answering application. It explores the world of machine learning from application developer’s perspective. It explains the world of word embeddings which is fundamental technology behind text processing. As Andrew Ng has said “AI is new electricity”. The course highlight difference among AI (Artificial Intelligence, Machine learning and deep learning. It also teaches few embedding technologies like glove, word2vec and BERT.
BERT is state of art transformer model developed by Google and has proven to be equivalent of CNN in computer vision technology. This course uses pretrained BERT model and explains how to use it in IOS question answering app.
The students once armed with this knowledge will be able to demonstrate their command on machine learning and can use this technology for several different apps.
The author assumes that the student does not have any background in machine learning.
The course is structured as follows
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App Preview : Shows preview of app that we are going to build
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Embeddings : Explains what word embeddings are and why are they important
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Deep Neural Network : It covers fundamentals of deep learning, and multi layer perceptron
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BERT, Glove, Word2Vec : Popular word embedding technologies
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Build UI from scratch : Shows how to build UI by using basic controls in iOS swift
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Step by Step Coding : Each function is explained in details with step by step walkthrough of the code
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Text to Speech and Speech to text : This sections explains how to use test to speech and speech top text conversion libraries in iOS app so that user can speak question into the app and hear the answer . This is extremely useful for physically challenged users who can not type using keyboard
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Run the app on iPhone : Shows the flow of the app on the phone.
Course Curriculum
Chapter 1: Introduction
Lecture 1: Introduction
Lecture 2: About Author
Lecture 3: Preview of app that we are going to build
Chapter 2: World of word embeddings
Lecture 1: What are word embeddings?
Lecture 2: One hot encodings
Lecture 3: Deep neural network
Lecture 4: Word2vec
Lecture 5: Glove embeddings
Lecture 6: BERT
Chapter 3: Mobile App
Lecture 1: Source Code
Lecture 2: Creating Single View App
Chapter 4: BERT Module
Lecture 1: BERT model
Lecture 2: BERT Vocabulary
Lecture 3: Tokenizer Part I
Lecture 4: Tokenizer Part II
Lecture 5: BERT Input
Lecture 6: BERT output
Lecture 7: BERT Facade
Chapter 5: Speech
Lecture 1: Converting speech to text
Lecture 2: Converting text to speech
Chapter 6: Bringing it all together
Lecture 1: Finding answer to question
Lecture 2: Building App
Lecture 3: Running app
Lecture 4: Next Steps
Instructors
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Evergreen Technologies
Software Mentor
Rating Distribution
- 1 stars: 0 votes
- 2 stars: 1 votes
- 3 stars: 0 votes
- 4 stars: 1 votes
- 5 stars: 6 votes
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