Master the essentials of deep learning using Python—fast. In this hands-on course, you’ll learn to build powerful neural networks that tackle real-world problems like image recognition, predictions, and text processing. Whether you’re new to AI or have some coding experience, this course provides a clear, practical path to get started. You'll use modern tools like PyTorch and apply what you learn to real datasets from day one.
Deep Learning with Python
PythonPython
Introduction to PyTorch and Deep Learning
Interested in deep learning but unsure where to start? This course introduces you to the core concepts and methods that power today’s AI systems. You’ll explore how neural networks work, how they learn from data, and how they’re used in applications like image classification, voice recognition, and text analysis.
You’ll use PyTorch, a leading open-source framework known for its flexibility and speed—ideal for building and training deep learning models.
This course is designed for learners who have a basic understanding of Python. You don’t need any background in deep learning or PyTorch yet, but it’s important that you’re comfortable with coding basics like variables, loops, and functions.
Just starting with Python? Begin with our Python Basics course.
Whether you’re a data analyst, software developer, or transitioning into an AI role, this course is a natural next step. You’ll build, train, and evaluate your own models—turning theory into hands-on experience.
What Will You Learn?
You’ll learn how to use Python and PyTorch to design, train, and optimize deep learning models. The course begins with the basics: what a neural network is, how it works, and how components like layers, activation functions, and loss functions fit together.
Then, you’ll jump into practice: using real datasets to solve classification and regression problems. You’ll work with image and text data, learn how to prepare inputs, train models, and measure performance. You’ll also explore techniques like hyperparameter tuning and training with GPUs to improve results.
By the end, you’ll know how to set up a complete deep learning pipeline—from raw data to high-performing models.
Why Choose This Course?
This course offers a solid foundation in deep learning with a strong focus on real-world application. You won’t just study concepts—you’ll put them into action through clear, hands-on exercises and realistic projects.
Using modern tools like PyTorch, you’ll follow a step-by-step approach designed to help you succeed. Expect real use cases like image classification or text analysis, not dry examples. The course is ideal for anyone looking to expand their Python skills into AI, machine learning, or data science.
After completing this course, you’ll be able to use deep learning confidently in your own work and projects.
What Topics Are Covered?
This intensive course covers all essential parts of deep learning with Python. You’ll start by setting up your development environment, installing PyTorch, and enabling GPU support for faster training.
Next, you’ll build neural networks from scratch. You’ll work with key elements like layers, activation functions, and loss functions—and learn how to combine them into models that actually perform. You’ll also evaluate your models using tools like TorchMetrics.
You’ll then dive into model tuning, optimizing performance with hyperparameters, and applying these skills to real datasets. Expect projects from domains like computer vision (e.g., image recognition) and natural language processing (e.g., text analysis).
Finally, you’ll explore where PyTorch fits in the broader AI landscape. You’ll compare it with other frameworks like TensorFlow, so you can choose the right tool for your future projects.
Course structure
Day 1
We start with the fundamentals of deep learning. You’ll learn how neural networks work, how they’re built, and how they learn from data. After setting up your programming environment, you’ll build and train your first models—working with structured data or images.
Day 2
On the second day, we go deeper. You’ll tune hyperparameters, use GPU acceleration, and explore how to apply your models to new types of data, such as text. The course wraps up with a final project: building, training, and testing your own deep learning model from start to finish.
Learning Outcomes
After completing this course, you will be able to:
- Build, train, and evaluate deep learning models independently using Python
- Apply activation functions, loss functions, and optimization techniques effectively
- Work with image, text, and tabular data
- Use tools like PyTorch and GPUs to speed up training
- Take on advanced AI projects involving natural language processing (NLP), computer vision, and beyond
More Information?
Do you have questions about the course content? Not sure if the course aligns with your learning objectives? Or would you prefer a private session or in-company training? We’re happy to assist—feel free to get in touch.
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Frequently Asked Questions
We aim to make our courses accessible to as many people as possible. If the course fee is a concern, instead of registering directly, you can indicate on the registration form that you’d like to receive a quote tailored to your needs. In many cases, we can offer flexible solutions. For instance, we can adjust the course content for large groups, shorten the course based on existing knowledge, or offer daily rates to suit your requirements.
Yes, you can reach out to the instructor with questions for up to 2 weeks after the course. Since the instructor is likely teaching other courses, we recommend emailing your questions to info@geo-ict.com. We’ll forward your inquiry to the instructor, and you’ll receive a response within 24 hours. After the 2-week period, we recommend using our Personalized Online Support for continued assistance.
Yes, we offer on-site training regularly throughout the Netherlands. Our instructor will bring laptops for the participants, and all you need to do is arrange a suitable room at your location.
Please send your requirements to info@geo-ict.com, and we will provide a customized quote, which will include travel and accommodation costs. Once the quote is confirmed, our course coordinator will reach out to schedule the training days.
After each course, participants receive a link to our evaluation portal where you can share your feedback on what you liked and didn’t like. We strive to provide a great experience for all our participants, but if you have a complaint, please click on ‘Complaints Procedure’ in the portal. This document will guide you through the steps to take. Geo-ICT Training Center, Netherlands, is a member of the Dutch Council for Training and Education (NRTO), ensuring a fair and transparent process.
Courses are typically scheduled according to the Dutch time zone, with sessions running from 9:00 AM to 12:00 PM and 1:00 PM to 4:00 PM. For participants in different time zones, we adjust the course times in consultation with you.