Deep Learning Using Keras Practice Exam

Deep Learning Using Keras Practice Exam

About the Deep Learning Using Keras Exam

Deep Learning Using Keras teaches how to build, train, and optimize neural networks with Keras, a high-level API of TensorFlow. Learners will explore key deep learning concepts, including activation functions, loss functions, backpropagation, and optimization techniques. The course covers building models for applications like image recognition, natural language processing, and predictive analytics. By the end, learners will gain hands-on experience in developing efficient deep learning models for real-world AI applications.

Skills Required to learn 

• Basic understanding of Python programming
• Familiarity with machine learning concepts
• Knowledge of fundamental mathematics, including linear algebra and probability
• Understanding of neural networks and deep learning basics
• Experience with Python libraries like NumPy and Pandas
• Basic knowledge of TensorFlow (helpful but not mandatory)
• Familiarity with data preprocessing and handling datasets

Knowledge Gained

• Understanding of deep learning fundamentals and neural network architectures
• Hands-on experience with Keras for building and training deep learning models
• Knowledge of activation functions, loss functions, and optimization techniques
• Ability to implement image classification, NLP, and predictive analytics models
• Proficiency in data preprocessing and model evaluation techniques
• Experience in tuning hyperparameters for better model performance
• Understanding of deploying deep learning models for real-world applications

Who should take the Exam?

• Students and professionals looking to build expertise in deep learning
• Machine learning engineers and data scientists wanting to enhance their skills
• AI researchers and developers working on neural network-based applications
• Software engineers interested in integrating deep learning into projects
• Data analysts aiming to transition into deep learning and AI roles
• Professionals in industries like healthcare, finance, and robotics using AI solutions
• Anyone preparing for AI and deep learning-related job roles or certifications

Course Outline

Course Introduction

• Course Introduction and Table of Contents

Introduction

• Introduction to AI (Artificial Intelligence) and Machine Learning
• Introduction to Deep learning

Setting Up Computer

• Installing Anaconda

Python Basics

• Assignment
• Flow Control - Part 1
• Flow Control - Part 2
• List and Tuples
• Dictionary and Functions - part 1
• Dictionary and Functions - part 2

NumPy Basics

• NumPy Basics - Part 1
• NumPy Basics - Part 2

Matplotlib Basics

• Matplotlib Basics - part 1
• Matplotlib Basics - part 2

Pandas Basics

• Pandas Basics - Part 1
• Pandas Basics - Part 2

Installing Libraries

• Installing Deep Learning Libraries

Artificial Neuron and Neural Network

• Basic Structure

Activation Functions

• Introduction

Popular Activation Functions

• Popular Types of Activation Functions

Popular Types of Loss Functions

• Popular Types of Loss Functions

Popular Types of Optimizers

• Popular Optimizers

Popular Neural Network Types

• Popular Neural Network Types

King County House Sales Regression Model

• Step 1 - Fetch and Load Dataset
• Step 2 and 3 - EDA (Exploratory Data Analysis) and Data Preparation - Part 1
• Step 2 and 3 - EDA and Data Preparation - Part 2
• Step 4 - Defining the Keras Model - Part 1
• Step 4 - Defining the Keras Model - Part 2
• Step 5 and 6 - Compile and Fit Model
• Step 7 - Visualize Training and Metrics
• Step 8 - Prediction Using the Model

Heart Disease Binary Classification Model

• Heart Disease Binary Classification Model - Introduction
• Step 1 - Fetch and Load Data
• Step 2 and 3 - EDA and Data Preparation - Part 1
• Step 2 and 3 - EDA and Data Preparation - Part 2
• Step 4 - Defining the Model
• Step 5 and 6 - Compile Fit and Plot the Model
• Step 7 - Predicting Heart Disease Using Model

Red Wine Quality Multiclass Classification Model

• Introduction
• Step 1 - Fetch and Load Data
• Step 2 and 3 - EDA and Data Visualization
• Step 4 - Defining the Model
• Step 5 and 6 - Compile Fit and Plot the Model
• Step 7 - Predicting Wine Quality using Model
• Serialize and Save Trained Model for Later Use

Digital Image Basics

• Digital Image
• Basic Image Processing Using Keras Functions - Part 1
• Basic Image Processing Using Keras Functions - Part 2
• Basic Image Processing Using Keras Functions - Part 3

Image Augmentation

• Keras Single Image Augmentation - Part 1
• Keras Single Image Augmentation - Part 2
• Keras Directory Image Augmentation
• Keras Data Frame Augmentation

Convolutional Neural Network

• CNN (Convolutional Neural Networks) Basics
• Stride Padding and Flattening Concepts of CNN
• Flowers CNN Image Classification Model
• Fetch Load and Prepare Data
• Create Test and Train Folders
• Defining the Model - Part 1
• Defining the Model - Part 2
• Defining the Model - Part 3
• Training and Visualization
• Save Model for Later Use
• Load Saved Model and Predict
• Improving Model - Optimization Techniques
• Dropout Regularization
• Padding and Filter Optimization
• Augmentation Optimization
• Hyper Parameter Tuning - Part 1
• Hyper Parameter Tuning - Part 2

Transfer Learning Using Pretrained Models

• VGG Introduction
• VGG16 and VGG19 Prediction
• VGG16 and VGG19 Prediction - Part 1
• VGG16 and VGG19 Prediction - Part 2

ResNet50

• ResNet50 Prediction

Transfer Learning Training Flowers Dataset

• VGG16 - Part 1
• VGG16 - Part 2

Transfer Learning Flower Prediction

• VGG16 Transfer Learning Flower Prediction

VGG16 Transfer Learning Using Google Colab GPU

• Preparing and Uploading Dataset
• Training and Prediction

VGG19 Transfer Learning using Google Colab GPU

• Training and Prediction

ResNet-50 Transfer Learning using Google Colab GPU

• Training and Prediction