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Python Deep Learning Projects

By : Lamons, Kumar, Nagaraja

3 (4)

Python Deep Learning Projects

3 (4)

By: Lamons, Kumar, Nagaraja

Overview of this book

Deep learning has been gradually revolutionizing every field of artificial intelligence, making application development easier. Python Deep Learning Projects imparts all the knowledge needed to implement complex deep learning projects in the field of computational linguistics and computer vision. Each of these projects is unique, helping you progressively master the subject. You’ll learn how to implement a text classifier system using a recurrent neural network (RNN) model and optimize it to understand the shortcomings you might experience while implementing a simple deep learning system. Similarly, you’ll discover how to develop various projects, including word vector representation, open domain question answering, and building chatbots using seq-to-seq models and language modeling. In addition to this, you’ll cover advanced concepts, such as regularization, gradient clipping, gradient normalization, and bidirectional RNNs, through a series of engaging projects. By the end of this book, you will have gained knowledge to develop your own deep learning systems in a straightforward way and in an efficient way

Preface

Preface

Who this book is for

What this book covers

To get the most out of this book

Get in touch

Free Chapter

Building Deep Learning Environments

Building Deep Learning Environments

Building a common DL environment

DL environment setup locally

Setting up a DL environment in the cloud

Cloud platforms for deployment

Automating the setup process

Summary

Training NN for Prediction Using Regression

Training NN for Prediction Using Regression

Building a regression model for prediction using an MLP deep neural network

Exploring the MNIST dataset

Intuition and preparation

Let's code the implementation!

Concluding the project

Summary

Word Representation Using word2vec

Word Representation Using word2vec

Learning word vectors

Visualizing the embedding space by plotting the model on TensorBoard

Building language models using CNN and word2vec

Summary

Building an NLP Pipeline for Building Chatbots

Building an NLP Pipeline for Building Chatbots

Basics of NLP pipelines

Building conversational bots

Serving chatbots

Summary

Sequence-to-Sequence Models for Building Chatbots

Sequence-to-Sequence Models for Building Chatbots

Introducing RNNs

Implementing basic RNNs

LSTM architecture

Implementing an LSTM model

Sequence-to-sequence models

Summary

Generative Language Model for Content Creation

Generative Language Model for Content Creation

LSTM for text generation

Generating lyrics using deep (multi-layer) LSTM

Generating music using a multi-layer LSTM

Summary

Building Speech Recognition with DeepSpeech2

Building Speech Recognition with DeepSpeech2

Data preprocessing

DS2 model description and intuition

Training the model

Testing and evaluating the model

Summary

Handwritten Digits Classification Using ConvNets

Handwritten Digits Classification Using ConvNets

Code implementation

Conclusion

Summary

Object Detection Using OpenCV and TensorFlow

Object Detection Using OpenCV and TensorFlow

Object detection intuition

Object detection using OpenCV

Object detection using deep learning

Image segmentation

Conclusion

Summary

Building Face Recognition Using FaceNet

Building Face Recognition Using FaceNet

Setup environment

Building the pipeline

Preprocessing of images

Training the classifier

Evaluation

Summary

Automated Image Captioning

Automated Image Captioning

Data preparation

Defining the captioning model

Training the captioning model

Evaluating the captioning model

Deploying the captioning model

Summary

Pose Estimation on 3D models Using ConvNets

Pose Estimation on 3D models Using ConvNets

Code implementation

Importing the dependencies

Conclusion

Summary

Image Translation Using GANs for Style Transfer

Image Translation Using GANs for Style Transfer

Let's code the implementation!

The conclusion to the project

Summary

Develop an Autonomous Agent with Deep R Learning

Develop an Autonomous Agent with Deep R Learning

Let's get to the code!

Deep Q-learning

Deep SARSA learning

The conclusion to the project

Summary

Summary and Next Steps in Your Deep Learning Career

Summary and Next Steps in Your Deep Learning Career

Python deep learning – building the foundation – two projects

Python deep learning – NLP – 5 projects

Deep learning – computer vision – 6 projects

Python deep learning – autonomous agents – 1 project

Next steps – AI strategy and platforms

Conclusion and thank you!

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Code implementation

In this exercise, we will be using the Keras deep learning library, which is a high-level neural network API capable of running on top of TensorFlow, Theano, and CNTK.

If you ever have a question related to Keras, refer to this easy-to-understand Keras documentation at https://keras.io.

Please download the Chapter12 folder from GitHub before moving forward with this chapter.

This project involves downloading files from various sources that will be called inside the scripts. To make sure that the Python scripts or the Jupyter Notebook have no issues locating the downloaded files, follow these steps:

Open a Terminal and change your directory by using the cd command in the Chapter12 folder.
Download the FLIC-full data file with the following command:

wget http://vision.grasp.upenn.edu/video/FLIC-full.zip

Unzip the ZIP file with the following command:

unzip...

Visually different images

CONTINUE READING

83

Tech Concepts

36

Programming languages

73

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