TensorFlow Hub: Deep Learning, Computer Vision and NLP

TensorFlow Hub: Deep Learning, Computer Vision and NLP, available at $64.99, has an average rating of 4.7, with 46 lectures, based on 67 reviews, and has 1075 subscribers.

You will learn about Use pre-trained TensorFlow models to solve Computer Vision and Natural Language Processing problems Classify images of flowers using Convolutional Neural Networks Detect over 80 different objects in images Apply style transfer to images Build a GAN to complete the missing parts of images Recognize actions in videos Classify sentiments in texts Use information retrieval techniques to return similar documents Classify over 500 audio events This course is ideal for individuals who are People interested in increasing their knowledge in Deep Learning or Undergraduate and graduate students who are taking courses related to Artificial Intelligence or Data Scientists who want to increase their project portfolio or People interested in building commercial applications quickly and easily using TensorFlow Hub's pre-trained models It is particularly useful for People interested in increasing their knowledge in Deep Learning or Undergraduate and graduate students who are taking courses related to Artificial Intelligence or Data Scientists who want to increase their project portfolio or People interested in building commercial applications quickly and easily using TensorFlow Hub's pre-trained models.

Enroll now: TensorFlow Hub: Deep Learning, Computer Vision and NLP

Summary

Title: TensorFlow Hub: Deep Learning, Computer Vision and NLP

Price: $64.99

Average Rating: 4.7

Number of Lectures: 46

Number of Published Lectures: 46

Number of Curriculum Items: 46

Number of Published Curriculum Objects: 46

Original Price: $19.99

Quality Status: approved

Status: Live

What You Will Learn

Use pre-trained TensorFlow models to solve Computer Vision and Natural Language Processing problems
Classify images of flowers using Convolutional Neural Networks
Detect over 80 different objects in images
Apply style transfer to images
Build a GAN to complete the missing parts of images
Recognize actions in videos
Classify sentiments in texts
Use information retrieval techniques to return similar documents
Classify over 500 audio events

Who Should Attend

People interested in increasing their knowledge in Deep Learning
Undergraduate and graduate students who are taking courses related to Artificial Intelligence
Data Scientists who want to increase their project portfolio
People interested in building commercial applications quickly and easily using TensorFlow Hub's pre-trained models

Target Audiences

People interested in increasing their knowledge in Deep Learning
Undergraduate and graduate students who are taking courses related to Artificial Intelligence
Data Scientists who want to increase their project portfolio
People interested in building commercial applications quickly and easily using TensorFlow Hub's pre-trained models

Deep Learning is the application of artificial neural networks to solve complex problems and commercial problems. There are several practical applications that have already been built using these techniques, such as: self-driving cars, development of new medicines, diagnosis of diseases, automatic generation of news, facial recognition, product recommendation, forecast of stock prices, and many others! The technique used to solve these problems is artificial neural networks, which aims to simulate how the human brain works. They are considered to be the most advanced techniques in the Machine Learning area.

One of the most used libraries to implement this type of application is Google TensorFlow, which supports advanced architectures of artificial neural networks. There is also a repository called TensorFlow Hub which contains pre-trained neural networks for solving many kinds of problems, mainly in the area of Computer Vision and Natural Language Processing. The advantage is that you do not need to train a neural network from scratch! Google itself provides hundreds of ready-to-use models, so you just need to load and use them in your own projects. Another advantage is that few lines of code are needed to get the results!

In this course you will have a practical overview of some of the main TensorFlow Hub models that can be applied to the development of Deep Learning projects! At the end, you will have all the necessary tools to use TensorFlow Hub to build complex solutions that can be applied to business problems. See below the projects that you are going to implement:

Classification of five species of flowers
Detection of over 80 different objects
Creating new images using style transfer
Use of GAN (generative adversarial network) to complete missing parts of images
Recognition of actions in videos
Text polarity classification (positive and negative)
Use of a question and answer (Q&A) dataset to find similar document
Audio classification

All implementations will be done step by step using Google Colab online, so you do not need to worry about installing and configuring the tools on your own machine! There are more than 50 classes and more than 7 hours of videos!

Course Curriculum

Chapter 1: Introduction

Lecture 1: Course content

Lecture 2: Course materials

Chapter 2: Computer vision

Lecture 1: Plan of attack

Lecture 2: Image classification 1

Lecture 3: Image classification 2

Lecture 4: Image classification 3

Lecture 5: Image classification 4

Lecture 6: Image classification 5

Lecture 7: Object detection 1

Lecture 8: Object detection 2

Lecture 9: Object detection 3

Lecture 10: Style transfer 1

Lecture 11: Style transfer 2

Lecture 12: Style transfer 3

Lecture 13: Image extension using GAN 1

Lecture 14: Image extension using GAN 2

Lecture 15: Action recognition 1

Lecture 16: Action recognition 2

Lecture 17: Action recognition 3

Lecture 18: Action recognition 4

Chapter 3: Natural language processing

Lecture 1: Plan of attack

Lecture 2: Text classification 1

Lecture 3: Text classification 2

Lecture 4: Text classification 3

Lecture 5: Q&A and information retrieval 1

Lecture 6: Q&A and information retrieval 2

Lecture 7: Q&A and information retrieval 3

Lecture 8: Q&A and information retrieval 4

Lecture 9: Q&A and information retrieval 5

Lecture 10: Audio classification 1

Lecture 11: Audio classification 2

Chapter 4: Extra content 1: Artificial neural networks

Lecture 1: Biological fundamentals

Lecture 2: Single layer perceptron

Lecture 3: Multilayer perceptron – sum and activation functions

Lecture 4: Multilayer perceptron – error calculation

Lecture 5: Gradient descent

Lecture 6: Delta parameter

Lecture 7: Updating weights with backpropagation

Lecture 8: Bias, error, stochastic gradient descent, and more parameters

Chapter 5: Extra content 2: Convolutional neural networks

Lecture 1: Introduction to convolutional neural networks

Lecture 2: Convolutional operation

Lecture 3: Pooling

Lecture 4: Flattening

Lecture 5: Dense neural network

Chapter 6: Final remarks

Lecture 1: Final remarks

Lecture 2: BONUS

Instructors

Jones Granatyr
Professor
AI Expert Academy
Instructor

Rating Distribution

1 stars: 1 votes
2 stars: 0 votes
3 stars: 5 votes
4 stars: 19 votes
5 stars: 42 votes

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