Deep Learning Using Keras and TensorFlow - eLearning

Deep Learning Using Keras and TensorFlow - eLearning

Step into the future of Artificial Intelligence with Deep Learning Certification Training and gain the skills needed to build intelligent, data-driven systems. This comprehensive program is designed to help you understand how neural networks work and how they power real-world applications such as image recognition, natural language processing, and predictive analytics.

Through a combination of theory, hands-on coding, and real-world case studies, you will learn how to design, train, and optimize deep learning models using industry-standard frameworks like TensorFlow and Keras. The course takes you from foundational concepts to advanced architectures such as CNNs and RNNs, enabling you to solve complex business problems with AI.

By the end of the training, you will have the practical expertise to build deep learning models and apply them across industries such as healthcare, finance, cybersecurity, and e-commerce.

Key Features

• Course and material in English
• Intermediate level
• 2+ Hours of On-Demand Videos
• 7 Auto-Graded Assessments
• 3 Comprehensive Assignments
• 30 Recall Quizzes
• 7 Ebooks
• 10+ hours recommended study time
• 1 Year access to the learning platform
• Program completion certification included

Learning Outcomes

• Understand the fundamentals of deep learning and neural networks
• Build and train artificial neural networks from scratch
• Apply optimization techniques such as gradient descent and backpropagation
• Implement convolutional neural networks (CNNs) for image processing tasks
• Work with recurrent neural networks (RNNs) for sequential data
• Use TensorFlow and Keras to build and deploy deep learning models
• Apply deep learning techniques to real-world domains like NLP and computer vision
• Develop practical, production-ready AI solutions using Python

Target Audience

• Aspiring data scientists and AI engineers
• Software engineers transitioning into machine learning roles
• Data analysts and data engineers
• Big data professionals
• Deep learning enthusiasts
• Professionals looking to upskill in AI and machine learning

Prerequisites

• Basic understanding of Python programming is recommended
• Familiarity with statistics, algebra, and probability is helpful
• Exposure to data analysis concepts is an advantage
• Interest in artificial intelligence and machine learning

Course Content

Foundations of Deep Learning

• Introduction to Deep Learning
• Basics of Deep Learning
• Importance of Deep Learning

TensorFlow

• Getting Started With TensorFlow
• TensorFlow and Keras
• The Keras API
• Boston House Prices
• Training a Model
• Evaluating Deep Learning Models

Convolutional Neural Networks

• Introduction to CNNs
• How Do CNNs Work?
• Image Classification

Advanced CNNs

• Advanced CNNs
• Revisiting Convolutions
• Depth-Wise Convolutions
• MobileNetV2
• Autoencoders
• Transpose Convolutions
• Sub-Classing keras.Model
• Denoising Images
• Types of Image Segmentation
• COCO Dataset
• U-Net
• Custom Data Generators
• Building an Image Segmentation Model

Natural Language Processing

• Introduction to Natural Language Processing (NLP)
• Recurrent Neural Networks (RNNs)
• Text Classification

Generative Adversarial Networks (GANs)

• What Are Generative Adversarial Networks (GANs)
• Autoencoders Revisited
• How GANs Work?
• Examples of GANs
• Challenges With GANs
• DCGAN
• Building a Generator
• Building a Discriminator
• Building the GAN
• The Training Loop

AI in the Real World

• Getting Started With AI in the Real World
• AI in Production
• The Issues With AI (Technology) – Adversarial Attacks
• The Issues With AI (Technology) – Confusion Matrices
• The Issues With AI (Technology) – Model Accuracy
• The Issues With AI (Ethics) – Algorithms Gone Wrong
• The Issues With AI (Ethics) – What Can We Do Differently?

FAQ

Will there be any learning material beyond self-paced videos?

Absolutely! The on-demand learning experience goes beyond videos to provide a fully immersive learning environment, including:

• LEARN: Interactive eBooks, recall quizzes, and real-world case studies to reinforce concepts
• ASSESS: Diagnostic, module-level, and final assessments to track your progress
• PRACTICE: Hands-on exercises with real-world simulations and Cloud Labs
• GAIN INSIGHTS: Real-time analytics and reports highlighting your learning progress, challenges, and suggested areas to revisit for mastering key skills

Can I pursue this course alongside my full-time job?

Yes! This course is designed for maximum flexibility. Delivered in a self-paced online format, it allows you to learn and upskill at your own convenience, making it easy to balance with your full-time job.

What is this Deep Learning course about?

This course provides in-depth training on deep learning concepts, neural networks, and AI model development using tools like TensorFlow and Keras.

What skills will I gain after completing this course?

You will be able to design and train neural networks, build CNN and RNN models, and apply deep learning techniques to real-world problems.

How will this course help my career?

It prepares you for in-demand roles in AI and data science by giving you strong practical and theoretical knowledge of deep learning systems.