Deep Learning with Keras and Practical Applications

Packt via Coursera

Go to class Write review

Details

Go to class

Provider

Coursera
Pricing

Free Online Course (Audit)
Languages

English
Certificate

Paid Certificate Available
Duration & workload

9 hours 29 minutes
Sessions

On-Demand
Level

Intermediate
Subtitles

English

Found in

Overview

Save Big on Coursera Plus. 7,000+ courses at $160 off. Limited Time Only!

Grab it

Embark on a comprehensive journey into deep learning with Keras through this meticulously crafted course. The course begins with an engaging introduction to creating a multiclass classification model for assessing red wine quality. You'll learn to fetch, load, and prepare data, followed by exploratory data analysis (EDA) and visualization to uncover insights and patterns. As you progress, you'll delve into defining, compiling, fitting, and optimizing your model, ultimately using it for accurate wine quality predictions. Building on this foundation, the course transitions into the fascinating world of digital image processing. You'll explore the basics of digital images, followed by practical sessions on image processing using Keras functions. Advanced techniques such as image augmentation, both single image and directory-based, are covered in detail. The course also introduces Convolutional Neural Networks (CNNs), guiding you through model building, training, and optimization, specifically for flower image classification. The journey doesn't stop there. You'll venture into transfer learning with pre-trained models like VGG16 and VGG19, leveraging their power for enhanced model performance. Practical sessions on utilizing Google Colab's GPU for transfer learning ensure you gain hands-on experience in modern deep learning workflows. By the end of this course, you'll have a robust understanding of applying Keras to real-world problems, from data preprocessing to model deployment. This course is ideal for data scientists, machine learning engineers, and technical professionals with a basic understanding of Python programming and machine learning concepts. No prior experience with Keras is required, though familiarity with neural networks and deep learning frameworks will be beneficial.

Syllabus

Redwine Quality Multiclass Classification Model - Introduction

In this module, we will introduce you to the concept of multiclass classification for red wine quality assessment. You will gain insights into the project's goals, the methodologies employed, and an overview of the steps we will follow throughout this engaging machine learning journey.

Step1 - Fetch and Load Data

In this module, we will guide you through the crucial first step of fetching and loading data. You will learn how to acquire and prepare your dataset, setting a solid foundation for the machine learning process ahead.

Step 2 - EDA and Data Visualization

In this module, we will dive into Exploratory Data Analysis (EDA) and data visualization. By leveraging visual tools and techniques, you will gain a deeper understanding of your dataset, uncovering crucial insights before proceeding to model creation.

Step 3 - Defining the Model

In this module, we will define the model's architecture. You will witness the construction of layers, activation functions, and connections, understanding how each component contributes to the overall machine learning journey.

Step 4 - Compile, Fit, and Plot the Model

In this module, we will guide you through the compilation, fitting, and plotting of the model. You will learn how to optimize model training and visualize performance metrics, ensuring a well-tuned classification model.

Step 5 - Predicting Wine Quality Using Model

In this module, we will demonstrate how to use the trained model for predicting wine quality. You will see the model in action, applying it to real-world data and analyzing the results to understand its predictive power.

Serialize and Save Trained Model for Later Usage

In this module, you will learn how to serialize and save your trained model. This essential process will ensure that your model's weights, architecture, and configuration are preserved for future use and deployment.

Digital Image Basics

In this module, we will cover the basics of digital images. You will gain a solid grasp of pixel representation, color channels, resolution, and image formats, forming the foundation for more advanced image processing tasks.

Basic Image Processing Using Keras Functions

In this module, we will introduce basic image processing using Keras functions. You will learn how to manipulate images, convert between formats, and handle color channels using Keras preprocessing utilities.

Keras Single Image Augmentation

In this module, we will delve into image augmentation using Keras. You will learn how to enhance single images using the ImageDataGenerator class, a crucial step in improving model generalization and accuracy.

Keras Directory Image Augmentation

In this module, we will explore directory-based image augmentation with Keras. You will learn how to enhance your entire image dataset, a vital skill for improving model generalization and accuracy.

Keras Data Frame Augmentation

In this module, we will delve into data frame augmentation using Keras. You will discover how to amplify your dataset's diversity using advanced augmentation techniques, improving your model's training and performance.

CNN Basics

In this module, we will demystify the basics of Convolutional Neural Networks (CNNs). You will explore their architecture, layers, and the fundamental principles that power image recognition and classification.

Stride, Padding, and Flattening Concepts of CNN

In this module, we will unravel the core concepts of stride, padding, and flattening in CNNs. You will understand how these elements shape convolutions and feature extraction, enhancing your deep learning models.

Flowers CNN Image Classification Model - Fetch, Load, and Prepare Data

In this module, we will dive into building a CNN model for flower image classification. You will learn how to fetch, load, and meticulously prepare your data, ensuring robust model training and accuracy.

Flowers Classification CNN - Create Test and Train Folders

In this module, we will address the fundamental step of creating dedicated test and train folders for flower classification using CNNs. You will learn how to organize your dataset meticulously, enhancing the training and testing process.

Flowers Classification CNN - Defining the Model

In this module, we will define the CNN model for flower classification. You will learn how to design a baseline model using the Sequential class, building the architecture layer by layer for effective image classification.

Flowers Classification CNN - Training and Visualization

In this module, we will delve into the training and visualization of the CNN model for flower classification. You will learn the intricate steps that transform data into predictions, enhancing your understanding of model training.

Flowers Classification CNN - Save Model for Later Use

In this module, you will learn how to save your trained CNN model for future use in flower classification tasks. Master the essential skill of model persistence and serialization, ensuring seamless deployment whenever needed.

Flowers Classification CNN - Load Saved Model and Predict

In this module, we will dive into loading a pre-trained CNN model for flower classification. You will learn how to harness the power of saved models to make precise predictions, elevating your understanding of model deployment.

Flowers Classification CNN - Optimization Techniques - Introduction

In this module, we will lay the foundation for optimization techniques in flower classification using CNNs. You will understand the importance of optimization and learn about various methods to enhance your model's performance.

Flowers Classification CNN - Dropout Regularization

In this module, we will delve into the world of dropout regularization in flower classification using CNNs. You will learn how to implement dropout to prevent overfitting and enhance your model's performance and generalization.

Flowers Classification CNN - Padding and Filter Optimization

In this module, we will explore padding and filter optimization techniques in flower classification using CNNs. You will learn how to optimize these elements to improve model accuracy and performance.

Flowers Classification CNN - Augmentation Optimization

In this module, we will delve into the optimization of data augmentation techniques in flower classification using CNNs. You will learn how to enhance your model's performance by implementing effective augmentation strategies.

Hyperparameter Tuning

In this module, we will embark on the journey of hyperparameter tuning for your CNN model. You will learn how to manually adjust parameters and implement strategies to enhance model performance and accuracy.

Transfer Learning Using Pre-Trained Models - VGG Introduction

In this module, we will introduce you to transfer learning using pre-trained models, focusing on the VGG architecture. You will understand the benefits and applications of transfer learning in enhancing your flower classification tasks.

VGG16 and VGG19 Prediction

In this module, we will explore predictions using the pre-trained VGG16 and VGG19 models. You will learn how to use these state-of-the-art models to achieve reliable predictions and interpret the results for flower classification.

ResNet50 Prediction

In this module, we will dive into the world of AI prediction using the ResNet50 model. You will learn how to apply ResNet50 to achieve reliable predictions and evaluate its performance in flower classification tasks.

VGG16 Transfer Learning Training Flowers Dataset

In this module, we will focus on transfer learning using the VGG16 model for training on a flower dataset. You will learn how to harness the power of pre-trained models to enhance your flower classification tasks.

VGG16 Transfer Learning Flower Prediction

In this module, we will delve into transfer learning with the VGG16 model, focusing on flower prediction. You will learn how to apply transfer learning to make precise predictions and evaluate its effectiveness in improving model performance.

VGG16 Transfer Learning Using Google Colab GPU - Preparing and Uploading Dataset

In this module, we will guide you through utilizing transfer learning with the VGG16 model on Google Colab's GPU. You will learn the essential procedures for preparing and uploading your dataset, harnessing the power of pre-trained models for efficient image classification tasks.

VGG16 Transfer Learning Using Google Colab GPU - Training and Prediction

In this module, we will guide you through transfer learning using the VGG16 model on Google Colab's GPU. You will learn how to train the model and make predictions, leveraging the power of pre-trained models for your image classification tasks.

VGG19 Transfer Learning Using Google Colab GPU - Training and Prediction

In this module, we will walk you through utilizing transfer learning with the VGG19 model on Google Colab's GPU. You will learn the step-by-step procedure for leveraging pre-trained models to tackle image classification tasks, ensuring enhanced model performance and accuracy.