TensorFlow Training in Moldova

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

By the conclusion 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 a solid conceptual foundation in neural networks, machine learning algorithms, and deep learning principles and applications.

The first part (40%) of this training emphasizes fundamentals, enabling you to select the most appropriate technology stack, such as TensorFlow, Caffe, Theano, DeepDrive, Keras, and others.

The second part (20%) introduces Theano, a Python library designed to simplify the creation of deep learning models.

The third part (40%) of the training focuses extensively on TensorFlow, Google's open-source library for deep learning. All examples and hands-on exercises will be conducted using TensorFlow.

Audience

This course is designed for engineers aiming to leverage TensorFlow for their deep learning projects.

Upon completion of this course, participants will:

• possess a strong understanding of deep neural networks (DNNs), Convolutional Neural Networks (CNNs), and Recurrent Neural Networks (RNNs)
• comprehend TensorFlow’s architecture and deployment mechanisms
• be capable of performing installation, production environment setup, and architectural configuration tasks
• be able to assess code quality, perform debugging, and monitor performance
• implement advanced production workflows, including model training, graph construction, and logging