Deep Learning Specialization Training
450,00 EUR
- 50 hours
This comprehensive course provides the knowledge and skills to deploy deep learning tools using AI/ML frameworks effectively. You will explore the fundamental concepts and practical applications of deep learning while gaining a clear understanding of the distinctions between deep learning and machine learning. The course covers a wide range of topics, including neural networks, forward and backward propagation, TensorFlow 2, Keras, performance optimization techniques, model interpretability, Convolutional Neural Networks (CNNs), transfer learning, object detection, Recurrent Neural Networks (RNNs), autoencoders, and creating neural networks in PyTorch. By the end of the course, you will have a solid foundation in deep learning principles and the ability to build and optimize deep learning models effectively using Keras and TensorFlow.
Key Features
Language
Course and material in English
Level
Intermediate - advanced level
Access
1 year access to the platform & class recordings
6 hours of video lessons
and 40 hours online live class
Study time
50 hours of study time recommendation
Virtual Lab included to practice
3 course-end project, and 1 Assessment test
No exam
but certification of completion included
Learning Outcomes
At the end of this course, you will be able to:
Deep Learning
Differentiate between deep learning and machine learning and understand their respective applications.
Neural networks
Gain a thorough understanding of various types of neural networks.
DNNs
Master the concepts of forward propagation and backward propagation in Deep Neural Networks (DNNs).
Modeling
Gain insight into modeling techniques and performance improvement in deep learning.
Parameter
Understand the principles of hyperparameter tuning and model interpretability.
Techniques
Learn about essential techniques such as dropout and early stopping and implement them effectively.
CNNs
Develop expertise in Convolutional Neural Networks (CNNs) and object detection.
PyTorch
Gain familiarity with PyTorch and learn how to create neural networks using this framework.
RNNs
Acquire a solid understanding of Recurrent Neural Networks (RNNs).
Course timeline
Introduction to Deep Learning
Lesson 1
Artificial Neural Networks
Lesson 2
Deep Neural Networks
Lesson 3
TensorFlow
Lesson 4
Model Optimization and Performance Improvement
Lesson 5
Convolutional Neural Networks (CNNs)
Lesson 6
Transfer Learning
Lesson 7
Object Detection
Lesson 8
Recurrent Neural Networks (RNNs)
Lesson 9
Transformer Models for Natural Language Processing (NLP)
Lesson 10
Getting Started with Autoencoders
Lesson 11
PyTorch
Lesson 12
Who Should Enroll in this Program?
Prerequisites:
Basic Python programming, knowledge of linear algebra, probability, and some machine learning fundamentals are highly recommended.
Software Engineers & Developers
Data Scientists & Analysts
AI/ML Enthusiasts
Students & Researchers
IT & Cloud Professionals
Business & Product Managers
Frequently Asked Questions
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