Deep Learning Using Keras and TensorFlow - eLearning
450,00 EUR
- 10 hours
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.
Key Features
Language
Course and material in English
Level
Intermediate level
Access
1 Year access to the learning platform
2 Hours of On-Demand Videos
with 10+ hours recommended study time
7 Auto-Graded Assessments
3 Comprehensive Assignments
7 Ebooks
30 Recall Quizzes
Certification
Program completion certification included
Learning Outcomes
At the end of this Course, you will be able to:
Fundamentals
Understand the fundamentals of deep learning and neural networks
Train
Build and train artificial neural networks from scratch
Apply
Apply optimization techniques such as gradient descent and backpropagation
CNNs
Implement convolutional neural networks (CNNs) for image processing tasks
RNNs
Work with recurrent neural networks (RNNs) for sequential data
TensorFlow
Use TensorFlow and Keras to build and deploy deep learning models
Techniques
Apply deep learning techniques to real-world domains like NLP and computer vision
Develop
Develop practical, production-ready AI solutions using Python
Course timeline
Foundations of Deep Learning
Lesson 01
- Introduction to Deep Learning
- Basics of Deep Learning
- Importance of Deep Learning
TensorFlow
Lesson 02
- Getting Started With TensorFlow
- TensorFlow and Keras
- The Keras API
- Boston House Prices
- Training a Model
- Evaluating Deep Learning Models
Convolutional Neural Networks
Lesson 03
- Introduction to CNNs
- How Do CNNs Work?
- Image Classification
Advanced CNNs
Lesson 04
- 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
Lesson 05
- Introduction to Natural Language Processing (NLP)
- Recurrent Neural Networks (RNNs)
- Text Classification
Generative Adversarial Networks (GANs)
Lesson 06
- 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
Lesson 07
- 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?
Who Should Enroll in This Program?
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
Aspiring data scientists and AI engineers
Software engineers transitioning into machine learning roles
Data analysts and data engineers
Big data professionals
Statements
Licensing and accreditation
This course is offered according to Partner Program Agreement and complies with the License Agreement requirements
Equity Policy
Candidates are encouraged to reach out to AVC for guidance and support throughout the accommodation process.
Frequently Asked Questions
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