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Genre: eLearning
Artificial Neural Networks, CNNs, RNNs, Self-Organizing Maps, Boltzmann Machines, Autoencoders, and GANs
What you’ll learn:
Learn both the theory and hands-on techniques for building artificial neural networks to solve real-world problems
Master key concepts in Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Self-Organizing Maps (SOMs), Boltzmann Machines, Autoencoders, an
Evaluate, tune, and optimize neural network hyperparameters
Build neural networks step by step for both classification and regression tasks
Develop neural networks from scratch to predict used vehicle prices and forecast video game sales
Implement Convolutional Neural Networks to classify handwritten digits and identify cats and dogs in images
Build a Recurrent Neural Network to predict Petrobras stock prices
Apply Self-Organizing Maps to data clustering and fraud detection in financial datasets
Perform dimensionality reduction using Boltzmann Machines and Autoencoders
Create a recommendation system using Boltzmann Machines
Generate new images using Generative Adversarial Networks (GANs)
Deep Learning is one of the most important fields in modern Artificial Intelligence. It uses artificial neural networks to solve complex problems involving computer vision, natural language processing, time series analysis, recommendation systems, fraud detection, content generation, and many other applications that are part of the daily operations of companies and organizations around the world.
Deep Learning techniques power a wide range of modern solutions, including intelligent assistants, generative AI models for text and images, AI-assisted medical diagnosis, autonomous vehicles, advanced recommendation systems, image and speech recognition, demand forecasting, financial analysis, and drug discovery. Although Artificial Intelligence has evolved rapidly in recent years with the emergence of foundation models, neural networks remain the core technology behind these breakthroughs.
The demand for professionals who can develop, train, evaluate, and deploy Deep Learning models continues to grow across technology companies, fintechs, industries, startups, research centers, and organizations in virtually every sector. Today, knowledge of Artificial Intelligence and Machine Learning is considered a valuable skill for software developers, data analysts, data scientists, and technology professionals.
To help you enter this exciting field, this course provides a comprehensive learning experience that combines theoretical foundations with practical applications using Python and the leading tools in the Machine Learning ecosystem. The content is carefully structured to guide you from the fundamentals to more advanced Deep Learning techniques, giving you the knowledge needed to understand, build, and adapt neural network models for real-world problems.
The course is organized into seven major modules:
- Artificial Neural Networks (ANNs)
- Convolutional Neural Networks (CNNs)
- Recurrent Neural Networks (RNNs)
- Self-Organizing Maps (SOMs)
- Boltzmann Machines
- Autoencoders
- Generative Adversarial Networks (GANs)
In each module, you will learn the underlying concepts, understand how the algorithms work, and implement practical projects step by step using Python.
Some of the projects developed throughout the course include:
- Classifying tumors as benign or malignant
- Plant species classification
- Predicting used vehicle prices
- Forecasting video game sales
- Handwritten digit recognition
- Cat and dog image classification
- Homer and Bart image classification from _The Simpsons_
- Object recognition in images (airplanes, automobiles, birds, cats, horses, trucks, and more)
- Building time series models to predict stock prices
- Predicting air pollution levels
- Clustering wines based on their characteristics
- Grouping medical records for exploratory analysis
- Detecting potential fraud in financial datasets
- Dimensionality reduction for images and complex datasets
- Building movie recommendation systems
- Comparing neural-network-based recommendation systems with traditional collaborative filtering approaches
- Generating new images using generative neural networks
At the end of each theoretical module, you will find quizzes to reinforce the concepts covered, along with additional resources for further study. The practical sections include programming exercises and complete projects with fully worked solutions, allowing you to compare your implementation and strengthen your understanding.
This course is designed for students, professionals, and technology enthusiasts at different experience levels. If this is your first exposure to Deep Learning, Machine Learning, or Neural Networks, you will have access to an introductory appendix covering the essential concepts needed to get started.
The only mandatory prerequisite is a basic understanding of programming logic. Advanced Python knowledge is not required, as all examples are explained in detail throughout the course.
If you want to build a strong foundation in Deep Learning and learn how to develop practical neural network applications, this course is for you.
See you in class!
Who this course is for:
Anyone interested in getting started with Deep Learning and building a solid foundation in the field
Individuals who want to learn about Artificial Neural Networks, Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Self-Organizing Maps (SOMs), Boltzmann Machines, Autoencoders, and Generative Adversarial Networks (GANs)
Aspiring Data Scientists looking to launch a career using modern Machine Learning and Deep Learning techniques
Entrepreneurs who want to apply Machine Learning and Deep Learning to commercial projects and innovative products
Data Analysts seeking to expand their expertise and advance their skills in Deep Learning
Business owners and decision-makers who want to develop effective AI-driven solutions for real-world business challenges
Undergraduate students taking courses related to Artificial Intelligence, Data Science, or Machine Learning who want practical, hands-on experience with modern neural network techniques
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