Deep Learning for Data Architects: Unleash the power of Python's deep learning algorithms 9789355515391

Table of contents :
Table of Contents

1. Python for Data Science

Structure

Objectives

Setting up the development environment

Installing Anaconda

Advance Python libraries for data science

Numpy

Pandas

Reading and writing data to and from different file formats

Format - csv

Format - Excel

Format - JSON

Format - clipboard

Format - HTML tables

Format - PDF

Format - Web scraping

Improving efficiency with the pandas read_csv method

Parameter - dtype

Parameter - usecols

Parameter - chunksize

Conclusion

Questions

Answers

2. Real-World Challenges for Data Professionals in Converting Data Into Insights

Structure

Objectives

Pandas profiling

Analyzing Pandas profile report

Saving the Pandas profile report to a HTML file

Creating a Jupyter Notebook widget

Pandas profile report for big datasets

Sweetviz

Installing and getting started with Sweetviz

Analyzing Sweetviz report

Generating a report to compare two DataFrames using Sweetviz

AutoViz

Installing and getting started with AutoViz

Analyzing AutoViz report

Lux

Installing and getting started with Lux

Analyzing Lux report

Generating Lux visualizations based on intent

Saving Lux report to a HTML file

Advanced features in Lux reports

Lazy Predict

Analyzing Lazy Predict experimentation results

PyCaret

Installing and getting started with PyCaret

Analyzing PyCaret experimentation results

Advanced features of PyCaret

Conclusion

Questions

Answers

3. Build a Neural Network-Based Predictive Model

Structure

Objectives

Artificial neural network and its components

Neurons

Feed forward

Activation functions

Loss function

Backward propagation

Epoch

Batch

Iteration

Optimizer

Learning rate

Building a classification model using neural network

Problem statement

Dataset

Implementation

Load Python libraries

Load data

Descriptive analytics

Data pre-processing

Modeling

Experiment 1 - Hidden layer -1, epoch-100 – shallow neural network

Experiment 2 - hidden layer -2, epoch-100 – deep neural network

Building a regression model using neural network

Problem statement

Dataset

Data pre-processing

Modeling

Model evaluation

Conclusion

Questions

Answers

4. Convolutional Neural Networks

Structure

Objectives

Convolutional neural networks components

Load required libraries

Digital image as a numpy array

Kernels/filters and convolution process

Stride

Padding

Convolution on RGB image

Convolution operation with multiple filters

One convolution layer

Pooling

Flattening

Dense layers

Image classification using CNN

Problem statement

Dataset - MNIST

Implementation

Data pre-processing

Modeling

Plot confusion matrix

Create a confusion matrix and plot

Hyperparameters tuning using KerasTuner

Dataset – Fashion MNIST

Implementation

Install KerasTuner

Conclusion

Questions

Answers

5. Optical Character Recognition

Introduction

Structure

Objectives

Optical character recognition

OCR Python libraries and their implementation

Tesseract OCR

Tesseract OCR demo

keras-ocr

keras-ocr demo

EasyOCR

EasyOCR demo

TrOCR

TrOCR demo

Conclusion

Questions

Answers

6. Object Detection

Structure

Objectives

Object localization and detection

Object detection algorithms and their comparison

Single shot detector Python implementation

YOLO v3 Python implementation

Experiment 2

Experiment 3

Experiment 4

Experiment 5

Conclusion

Questions

Answers

7. Image Segmentation

Structure

Objectives

Difference between image classification, detection and segmentation

Image segmentation architectures

U-Net Python implementation

FCN-8 Python implementation

Mask R-CNN Python implementation

Conclusion

Questions

Answers

8. Recurrent Neural Networks

Structure

Objectives

Algorithms for RNN implementation

RNN implementation

Long short-term memory implementation

Gated Recurrent Unit implementation

Conclusion

Multiple choice questions

Answers

9. Generative Adversarial Networks

Structure

Objectives

Types of GAN

Vanilla GAN Python implementation

Key difference between Vanilla GAN and DCGAN

DCGAN Python implementation

StyleGAN Python implementation

Setup environment

Conclusion

Questions

Answers

10. Transformers

Structure

Objectives

Introduction to Transformers in deep learning

Various transformers architectures

Difference between contextual and non-contextual embeddings

BERT Python implementation

GPT Python implementation

Conclusion

Questions

Answers

Index