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Deep Learning with Keras and Practical Applications · LearnSpace
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Deep Learning with Keras and Practical Applications

Курс от Packt
Средний≈ 18 чАнглийский
О курсеНавыкиПрограммаПреподаватели

О курсе

Updated in May 2025. This course now features Coursera Coach! A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the course. 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.

Навыки, которые вы освоите

Transfer LearningModel TrainingKeras (Neural Network Library)Model EvaluationModel OptimizationData PreprocessingDeep LearningPredictive ModelingExploratory Data AnalysisApplied Machine LearningImage AnalysisModel DeploymentMachine LearningClassification AlgorithmsFine-tuningApplication DeploymentData ProcessingComputer VisionArtificial Neural NetworksData Analysis

Программа курса

33 модулей · 90 учебных материалов

01Redwine Quality Multiclass Classification Model - Introduction4 материалов

Redwine Quality Multiclass Classification Model - Introduction

Introduction to the Course 'Deep Learning with Keras and Practical Applications'ЧтениеFull Specialization ResourcesЧтениеRedwine Quality Multiclass Classification Model - IntroductionВидеоBuilding a Multi-Class Classification ModelDIALOGUE

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Packt - Course Instructors

Преподаватель курса

Deep Learning with Keras and Practical Applications
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Обучение откроется на Coursera
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Обучение на Coursera

≈ 18 ч

33 модулей

Язык: Английский

Субтитры: Арабский, Французский, Итальянский, Бразильский португальский, Корейский, Немецкий, Испанский, Японский, Венгерский

Часть программы вашего университета
02Step1 - Fetch and Load Data2 материалов

Step1 - Fetch and Load Data

Step1 - Fetch and Load DataВидеоLoading and Exploring Red Wine Quality DatasetDIALOGUE
03Step 2 - EDA and Data Visualization3 материалов

Step 2 - EDA and Data Visualization

Step 2 - EDA and Data VisualizationВидеоAssessment 1ЗаданиеExploratory Data Analysis Techniques in PythonDIALOGUE
04Step 3 - Defining the Model2 материалов

Step 3 - Defining the Model

Step 3 - Defining the ModelВидеоDefining a Keras Sequential ModelDIALOGUE
05Step 4 - Compile, Fit, and Plot the Model2 материалов

Step 4 - Compile, Fit, and Plot the Model

Step 4 - Compile, Fit, and Plot the ModelВидеоCompiling and Training a Neural NetworkDIALOGUE
06Step 5 - Predicting Wine Quality Using Model3 материалов

Step 5 - Predicting Wine Quality Using Model

Step 5 - Predicting Wine Quality Using ModelВидеоAssessment 2ЗаданиеUnderstanding Multiclass Classification with ArgmaxDIALOGUE
07Serialize and Save Trained Model for Later Usage2 материалов

Serialize and Save Trained Model for Later Usage

Serialize and Save Trained Model for Later UsageВидеоUnderstanding Model SerializationDIALOGUE
08Digital Image Basics2 материалов

Digital Image Basics

Digital Image BasicsВидеоRepresenting Images with Arrays in Deep LearningDIALOGUE
09Basic Image Processing Using Keras Functions5 материалов

Basic Image Processing Using Keras Functions

Basic Image Processing Using Keras Functions - Part 1ВидеоBasic Image Processing Using Keras Functions - Part 2ВидеоBasic Image Processing using Keras Functions - Part 3ВидеоAssessment 3ЗаданиеBasic Image Manipulation with Keras PreprocessingDIALOGUE
10Keras Single Image Augmentation3 материалов

Keras Single Image Augmentation

Keras Single Image Augmentation - Part 1ВидеоKeras Single Image Augmentation - Part 2ВидеоImage Augmentation with KerasDIALOGUE
11Keras Directory Image Augmentation2 материалов

Keras Directory Image Augmentation

Keras Directory Image AugmentationВидеоUsing Flow From Directory in Image AugmentationDIALOGUE
12Keras Data Frame Augmentation3 материалов

Keras Data Frame Augmentation

Keras Data Frame AugmentationВидеоAssessment 4ЗаданиеImplementing Keras Flow From DataFrame MethodDIALOGUE
13CNN Basics2 материалов

CNN Basics

CNN BasicsВидеоUnderstanding Convolutional Neural Networks (CNNs)DIALOGUE
14Stride, Padding, and Flattening Concepts of CNN2 материалов

Stride, Padding, and Flattening Concepts of CNN

Stride, Padding, and Flattening Concepts of CNNВидеоUnderstanding Padding and Stride in CNNsDIALOGUE
15Flowers CNN Image Classification Model - Fetch, Load, and Prepare Data3 материалов

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

Flowers CNN Image Classification Model - Fetch, Load, and Prepare DataВидеоAssessment 5ЗаданиеDesigning a CNN for Image ClassificationDIALOGUE
16Flowers Classification CNN - Create Test and Train Folders2 материалов

Flowers Classification CNN - Create Test and Train Folders

Flowers Classification CNN - Create Test and Train FoldersВидеоDividing a Dataset for Machine LearningDIALOGUE
17Flowers Classification CNN - Defining the Model4 материалов

Flowers Classification CNN - Defining the Model

Flowers Classification CNN - Defining the Model - Part 1ВидеоFlowers Classification CNN - Defining the Model - Part 2ВидеоFlowers Classification CNN - Defining the Model - Part 3ВидеоBuilding a Baseline CNN ModelDIALOGUE
18Flowers Classification CNN - Training and Visualization3 материалов

Flowers Classification CNN - Training and Visualization

Flowers Classification CNN - Training and VisualizationВидеоAssessment 6ЗаданиеTraining CNN Models and Visualizing ResultsDIALOGUE
19Flowers Classification CNN - Save Model for Later Use2 материалов

Flowers Classification CNN - Save Model for Later Use

Flowers Classification CNN - Save Model for Later UseВидеоWorking with Model Class LabelsDIALOGUE
20Flowers Classification CNN - Load Saved Model and Predict2 материалов

Flowers Classification CNN - Load Saved Model and Predict

Flowers Classification CNN - Load Saved Model and PredictВидеоLoading and Using a CNN Model for Image PredictionDIALOGUE
21Flowers Classification CNN - Optimization Techniques - Introduction3 материалов

Flowers Classification CNN - Optimization Techniques - Introduction

Flowers Classification CNN - Optimization Techniques - IntroductionВидеоAssessment 7ЗаданиеEnhancing Neural Network Model AccuracyDIALOGUE
22Flowers Classification CNN - Dropout Regularization2 материалов

Flowers Classification CNN - Dropout Regularization

Flowers Classification CNN - Dropout RegularizationВидеоExploring Dropout Regularization in Neural NetworksDIALOGUE
23Flowers Classification CNN - Padding and Filter Optimization2 материалов

Flowers Classification CNN - Padding and Filter Optimization

Flowers Classification CNN - Padding and Filter OptimizationВидеоUnderstanding Deep Learning Regularization TechniquesDIALOGUE
24Flowers Classification CNN - Augmentation Optimization3 материалов

Flowers Classification CNN - Augmentation Optimization

Flowers Classification CNN - Augmentation OptimizationВидеоAssessment 8ЗаданиеApplying Image Data AugmentationDIALOGUE
25Hyperparameter Tuning3 материалов

Hyperparameter Tuning

Hyperparameter Tuning - Part 1ВидеоHyperparameter Tuning - Part 2ВидеоHyperparameter Tuning with Keras TunerDIALOGUE
26Transfer Learning Using Pre-Trained Models - VGG Introduction2 материалов

Transfer Learning Using Pre-Trained Models - VGG Introduction

Transfer Learning Using Pre-Trained Models - VGG IntroductionВидеоLeveraging Pretrained CNN Models with Transfer LearningDIALOGUE
27VGG16 and VGG19 Prediction4 материалов

VGG16 and VGG19 Prediction

VGG16 and VGG19 Prediction- Part 1ВидеоVGG16 and VGG19 Prediction- Part 2ВидеоAssessment 9ЗаданиеWorking with Pretrained Deep Learning Models in KerasDIALOGUE
28ResNet50 Prediction2 материалов

ResNet50 Prediction

ResNet50 PredictionВидеоExploring ResNet in Deep LearningDIALOGUE
29VGG16 Transfer Learning Training Flowers Dataset3 материалов

VGG16 Transfer Learning Training Flowers Dataset

VGG16 Transfer Learning Training Flowers Dataset - part 1ВидеоVGG16 Transfer Learning Training Flowers Dataset - Part 2ВидеоTransfer Learning with Pretrained ModelsDIALOGUE
30VGG16 Transfer Learning Flower Prediction3 материалов

VGG16 Transfer Learning Flower Prediction

VGG16 Transfer Learning Flower PredictionВидеоAssessment 10ЗаданиеUsing Transfer Learning for Flower ClassificationDIALOGUE
31VGG16 Transfer Learning Using Google Colab GPU - Preparing and Uploading Dataset2 материалов

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

VGG16 Transfer Learning Using Google Colab GPU - Preparing and Uploading DatasetВидеоUsing Google Colab for Deep Learning with GPUDIALOGUE
32VGG16 Transfer Learning Using Google Colab GPU - Training and Prediction2 материалов

VGG16 Transfer Learning Using Google Colab GPU - Training and Prediction

VGG16 Transfer Learning Using Google Colab GPU - Training and PredictionВидеоConfiguring Google Colab for Deep LearningDIALOGUE
33VGG19 Transfer Learning Using Google Colab GPU - Training and Prediction6 материалов

VGG19 Transfer Learning Using Google Colab GPU - Training and Prediction

VGG19 Transfer Learning Using Google Colab GPU - Training and PredictionВидеоConclusion to the Course 'Deep Learning with Keras and Practical Applications'ЧтениеImplementing Transfer Learning with VGG 19 in Google ColabDIALOGUEAssessment 11ЗаданиеFull Course Practice AssessmentЗаданиеFull Course AssessmentЗадание