Deep learning and neural networks with Python, Pandas, Keras and TensorFlow
.svg)
.svg)
.svg)
Machine Learning Engineer
1. introduction to deep learning
- What are neural networks and how do they learn?
- Mathematical basics explained in compact form
- Neural networks with Keras and TensorFlow
- Models: evaluation and adaptation
- Models: use and storage
2. data preparation and feature extraction
- Data preparation with Pandas
- Exploratory data analysis
- Standardization of numerical data and text data
- Feature extraction: Extracting features from data
- Train networks with small amounts of data
3. specialized neural networks
- Convolutional neural networks (CNN)
- Updating weights for CNNs
- Max pooling and dropout
- Monitor teach-in processes with TensorBoard
- Recurrent neural networks (RNN)
- Time series analysis and text processing with RNN
4. deploy models and transfer learning
- Use of cloud GPUs for machine learning projects
- Introduction to transfer learning and the Zoo model
- Presentation of ImageNet, ResNet, VGG16
- Use pre-trained layers in your own projects
This training will provide you with in-depth knowledge of the concepts and methods of deep learning. You will learn about the possibilities and limitations of the technology and create, train and optimize your own data models and neural networks.
You will get to know the practical work of the most important Python frameworks and know how to use them in your own projects.
The technical hurdles for getting started are minimal - thanks to the use of Jupyter notebooks and free cloud GPUs.
After completing this training, you will not only have a sound theoretical knowledge of deep learning, but also gain practical experience in the application of modern AI technologies. You will be able to evaluate, adapt and productively use neural networks. You will also learn how to use the technologies in your own projects. This will qualify you for advanced tasks in AI development.
The content of this training supports the obligation to provide evidence of the promotion of AI competence within the meaning of Art. 4 EU AI Regulation.
This training training is conducted in a group of a maximum of 12 participants using the Zoom video conferencing software. Individual support from the trainers is guaranteed.
The exercises are provided in the form of Jupyter notebooks, which you can install locally on your own computer. The computationally intensive training of the data models is carried out on freely available cloud GPUs.
The trainers are on hand to help you carry out the practical exercises - in the virtual classroom or individually in break-out sessions.
Once you have registered, you will find all the information, downloads and extra services for this training course in your online learning environment.
This training is aimed at anyone who wants to understand machine learning in detail and use it in their own projects.
Basic knowledge of programming with Python is assumed. Advanced technical, mathematical and statistical knowledge is helpful, but not required.
This course is a valuable building block in the qualification as a data scientist, data engineer and machine learning engineer.
.svg)
