TensorFlow Training in Groningen

This instructor-led, live training in Groningen (online or on-site) is aimed at experienced professionals who wish to deepen their knowledge of computer vision and explore TensorFlow's capabilities for developing sophisticated vision models using Google Colab.

By the end of this training, participants will be able to:

• Construct and train convolutional neural networks (CNNs) using TensorFlow.
• Leverage Google Colab for scalable and efficient cloud-based model development.
• Implement image preprocessing techniques for computer vision tasks.
• Deploy computer vision models for real-world applications.
• Use transfer learning to enhance the performance of CNN models.
• Visualize and interpret the results of image classification models.

This instructor-led live training in Groningen (online or onsite) is designed for intermediate-level data scientists and developers who wish to understand and apply deep learning techniques using the Google Colab environment.

By the end of this training, participants will be able to:

• Set up and navigate Google Colab for deep learning projects.
• Understand the fundamentals of neural networks.
• Implement deep learning models using TensorFlow.
• Train and evaluate deep learning models.
• Utilize advanced features of TensorFlow for deep learning.

This instructor-led live training in Groningen (online or onsite) is intended for data scientists aiming to utilize TensorFlow for the analysis of potential fraud data.

By the conclusion of this training, participants will be capable of:

• Creating a fraud detection model using Python and TensorFlow.
• Building linear regressions and linear regression models to predict fraud.
• Developing an end-to-end AI application for analyzing fraud data.

This instructor-led live training in Groningen (online or onsite) targets developers and data scientists wishing to utilize TensorFlow 2.x to build predictors, classifiers, generative models, neural networks, and similar applications.

By the end of this training, participants will be able to:

• Install and configure TensorFlow 2.x.
• Understand the benefits of TensorFlow 2.x over previous versions.
• Build deep learning models.
• Implement an advanced image classifier.
• Deploy a deep learning model to the cloud, mobile and IoT devices.

This course provides foundational knowledge of neural networks, machine learning algorithms, and deep learning (both algorithms and applications).

Part 1 (40%) of this training focuses on fundamentals but also assists you in selecting the appropriate technology: TensorFlow, Caffe, Theano, DeepDrive, Keras, etc.

Part 2 (20%) introduces Theano - a Python library that simplifies writing deep learning models.

Part 3 (40%) of the training will be extensively based on TensorFlow - Google's open-source software library API for Deep Learning. All examples and hands-on exercises will be conducted in TensorFlow.

Audience

This course is intended for engineers seeking to utilize TensorFlow for their Deep Learning projects.

Upon completion, delegates will be able to:

• Gain a solid understanding of deep neural networks (DNN), CNNs, and RNNs.
• Comprehend TensorFlow’s structure and deployment mechanisms.
• Perform installation, production environment setup, architecture tasks, and configuration.
• Assess code quality, perform debugging, and monitoring.
• Implement advanced production-like training models, building graphs, and logging.