Tensorflow Hub: Deep Learning, Computer Vision And Nlp

Build computer vision and natural language processing projects quickly, easily and with few lines of code!

What you'll 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
Requirements
Programming logic
Basic Python programming
Knowledge about Deep Learning and TensorFlow library is desirable, although it is possible to follow the course without advanced knowledge on this subjects
Description
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 flowersDetection of over 80 different objectsCreating new images using style transferUse of GAN (generative adversarial network) to complete missing parts of imagesRecognition of actions in videosText polarity classification (positive and negative)Use of a question and answer (Q&A) dataset to find similar documentAudio classificationAll 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!

Overview

Section 1: Introduction

Lecture 1 Course content

Lecture 2 Course materials

Section 2: Computer vision

Lecture 3 Plan of attack

Lecture 4 Image classification 1

Lecture 5 Image classification 2

Lecture 6 Image classification 3

Lecture 7 Image classification 4

Lecture 8 Image classification 5

Lecture 9 Object detection 1

Lecture 10 Object detection 2

Lecture 11 Object detection 3

Lecture 12 Style transfer 1

Lecture 13 Style transfer 2

Lecture 14 Style transfer 3

Lecture 15 Image extension using GAN 1

Lecture 16 Image extension using GAN 2

Lecture 17 Action recognition 1

Lecture 18 Action recognition 2

Lecture 19 Action recognition 3

Lecture 20 Action recognition 4

Section 3: Natural language processing

Lecture 21 Plan of attack

Lecture 22 Text classification 1

Lecture 23 Text classification 2

Lecture 24 Text classification 3

Lecture 25 Q&A and information retrieval 1

Lecture 26 Q&A and information retrieval 2

Lecture 27 Q&A and information retrieval 3

Lecture 28 Q&A and information retrieval 4

Lecture 29 Q&A and information retrieval 5

Lecture 30 Audio classification 1

Lecture 31 Audio classification 2

Section 4: Extra content 1: Artificial neural networks

Lecture 32 Biological fundamentals

Lecture 33 Single layer perceptron

Lecture 34 Multilayer perceptron – sum and activation functions

Lecture 35 Multilayer perceptron – error calculation

Lecture 36 Gradient descent

Lecture 37 Delta parameter

Lecture 38 Updating weights with backpropagation

Lecture 39 Bias, error, stochastic gradient descent, and more parameters

Section 5: Extra content 2: Convolutional neural networks

Lecture 40 Introduction to convolutional neural networks

Lecture 41 Convolutional operation

Lecture 42 Pooling

Lecture 43 Flattening

Lecture 44 Dense neural network

Section 6: Final remarks

Lecture 45 Final remarks

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