TensorFlow Training in Banat

This instructor-led live training in Banat (online or onsite) is designed for advanced-level professionals who aim to deepen their knowledge of computer vision and investigate TensorFlow's capabilities for developing complex vision models using Google Colab.

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

• Construct and train convolutional neural networks (CNNs) using TensorFlow.
• Utilize Google Colab to facilitate scalable and efficient cloud-based model development.
• Apply image preprocessing techniques tailored for computer vision tasks.
• Deploy computer vision models for practical, real-world applications.
• Employ transfer learning to optimize the performance of CNN models.
• Visualize and analyze the outcomes of image classification models.

This instructor-led, live training in Banat (online or onsite) is aimed at 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, offered Banat (online or on-site), targets data scientists interested in using TensorFlow for fraud data analysis.

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

• Create a fraud detection model in Python and TensorFlow.
• Build linear regressions and linear regression models to predict fraud.
• Develop an end-to-end AI application for analyzing fraud data.

This live, instructor-led course in Banat (delivered online or onsite) is designed for developers and data scientists aiming to utilize TensorFlow 2.x to build predictors, classifiers, generative models, neural networks, and related systems.

Upon completion of this training, participants will be able to:

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