Deep Learning with Python

Artificial Intelligence for Developers

In a short period of time, you’ll learn the basics and practical applications of deep learning with Python. During this course, you’ll develop powerful neural networks that you can use for image recognition, predictions, or text processing. The course is designed for anyone who wants to get started with AI quickly and in a practical way—whether you’re just starting out or already have programming experience. You’ll learn to work with modern tools like PyTorch and apply your knowledge directly to realistic datasets.

Course duration: 2 days

Nederlands

Introduction to Deep Learning with Python

Are you curious about how deep learning works, but don’t know where to start? In this course, you’ll be introduced to the key concepts and techniques of deep learning. You’ll learn, among other things, how neural networks are structured, how they learn from data, and how they’re used in applications such as image recognition, speech recognition, and text analysis.

We’ll use PyTorch, among other tools, to apply these concepts in practice. This open-source framework is popular worldwide for its flexibility and speed, especially when training neural networks.

The training is intended for students with a basic knowledge of Python. You do not need to have prior experience with deep learning or PyTorch. However, it is important that you are familiar with programming concepts such as variables, loops, and functions.
Don’t have any experience with Python yet? Then take our basic Python course or Python for Beginner Programmers first.

Whether you work as a data analyst, programmer, or are transitioning into AI—this course is a logical next step. You’ll learn how to build, train, and apply models to real-world data. This way, you’ll translate theory into practical skills.

What will you learn in the course?

During the course, you’ll learn how to build, train, and optimize deep learning models yourself using Python and frameworks like PyTorch and TensorFlow. You’ll start with the theory: how does a neural network work, and what are layers, activation functions, and loss functions? Then you’ll get to work with real datasets and learn how to apply models to classification and regression problems, among others.

You’ll work with image, text, and structured data, and learn how to prepare input, train models, and evaluate their performance. We’ll also cover practical optimization techniques such as hyperparameter tuning and the use of GPUs to speed up your training.

By the end of the course, you’ll know how to independently set up a deep learning project—from data input to a fully functional model.

Why choose this Deep Learning with Python course?

This course gives you a solid foundation in the world of deep learning. You’ll learn not only how neural networks work in theory, but especially how to apply them in practice using Python. The training is goal-oriented, with plenty of room for practice and experimentation.

You’ll learn to work with leading frameworks such as PyTorch and TensorFlow. No dry lectures, but practical assignments that align with realistic use cases such as image classification or text analysis.

The course is ideal for anyone looking to expand their Python knowledge into AI, machine learning, and data science. Upon completion, you’ll be able to apply deep learning independently in your work or projects.

Topics Covered

During this intensive course, all essential components of deep learning with Python will be covered. You’ll start by installing and configuring your work environment, including GPU support for faster training.

Next, you’ll learn how to build neural networks yourself. You’ll work with layers, activation functions, and loss functions, and discover how to combine these elements into a high-performing model. You’ll also learn how to evaluate and fine-tune the performance of your models.

You will then delve into optimization techniques such as hyperparameter tuning. You will apply this knowledge to both classification and regression problems, using datasets from domains such as computer vision (e.g., image recognition) and natural language processing (e.g., text analysis).

Finally, you’ll gain insight into the role of deep learning within the broader AI landscape. You’ll compare the pros and cons of different frameworks (such as PyTorch and TensorFlow) so you’ll know which platform best suits your future projects.

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€1195,- (VAT included)

Course duration: 2 days

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Dagindeling

Day 1

We’ll start with an overview of the key concepts of deep learning. You’ll be introduced to neural networks, learn how they learn, and understand their structure. Next, we’ll set up the programming environment and practice building your first models. You’ll learn how to build, train, and evaluate a network using structured data or images.

Day 2

On the second day, we’ll take a closer look at model optimization. You’ll learn how to tune hyperparameters, how to use GPU training, and how to apply models to other types of data, such as text. By the end, you’ll work on a final project: your own deep learning model that you’ll set up, train, and test entirely on your own.

Leerdoelen

After completing this course, you will be able to:

Independently set up, train, and evaluate a deep learning model using Python.
Work effectively with activation functions, loss functions, and optimization techniques.
Apply models to image, text, and tabular data.
Use frameworks such as PyTorch and GPUs for faster training.
Dive deeper into AI projects with applications in NLP, computer vision, and more.

Want to know more?

Do you have questions about the course content? Or are you unsure whether the course aligns with your learning goals or preferences? Would you prefer an in-house or private course? We’d be happy to help.