TensorFlow Training in Belgrade

This instructor-led live training Belgrade (available online or on-site) targets advanced professionals who aim to deepen their grasp 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:

• Build 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 Belgrade (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 four-day course provides an introduction to Artificial Intelligence and its practical applications. Upon completing the course, participants have the option to dedicate an additional day to working on a dedicated AI project.

In this instructor-led live training in Belgrade, participants will learn to utilize Python libraries for NLP by building an application that processes images and generates captions.

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

• Design and code Deep Learning for NLP using Python libraries.
• Create Python code that processes a large volume of images and generates keywords.
• Produce Python code that generates captions from the identified keywords.

Audience

This course is designed for researchers and engineers in the field of Deep Learning who wish to leverage available tools, primarily open-source solutions, for the analysis of computer images.

Participants will engage with practical working examples.

This instructor-led live training in Belgrade (online or on-site) is designed for data scientists who want to use TensorFlow to analyze potential fraud data.

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 Belgrade (online or onsite) is designed for developers and data scientists interested in utilizing TensorFlow 2.x to build predictors, classifiers, generative models, neural networks, and other solutions.

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.

In this instructor-led live training in Belgrade (online or onsite), participants will learn how to configure and use TensorFlow Serving to deploy and manage ML models in a production environment.

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

• Train, export and serve various TensorFlow models.
• Test and deploy algorithms using a single architecture and set of APIs.
• Extend TensorFlow Serving to serve other types of models beyond TensorFlow models.

TensorFlow is the second-generation API of Google’s open-source library for Deep Learning. The system is designed to facilitate machine learning research and enable a rapid transition from research prototypes to production systems.

Audience

This course is intended for engineers who wish to apply TensorFlow to their Deep Learning projects.

After completing this course, delegates will be able to:

• Understand the architecture and deployment mechanisms of TensorFlow
• Perform installation tasks, configure the production environment, and set up the architecture
• Assess code quality, debug, and monitor performance
• Implement advanced production-level tasks, such as training models, building graphs, and logging

This course provides a detailed examination of how TensorFlow is applied in the field of image recognition, illustrated through concrete examples.

Target Audience

The course is designed for engineers who wish to leverage TensorFlow for image recognition tasks.

Learning Outcomes:

• Gain a clear understanding of TensorFlow’s structure and deployment mechanisms
• Perform installation, set up production environments, and configure architecture
• Assess code quality, conduct debugging, and implement monitoring practices
• Execute advanced production tasks such as model training, graph construction, and logging

This instructor-led live training in Belgrade (online or onsite) is designed for data scientists seeking to expand their skills from training single ML models to deploying multiple models in production.

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

• Install and configure TFX and its supporting third-party tools.
• Use TFX to create and manage a complete ML production pipeline.
• Leverage TFX components to perform modeling, training, inference serving, and deployment management.
• Deploy machine learning features across web applications, mobile applications, IoT devices, and more.

TensorFlow™ is an open-source software library designed for numerical computation via data flow graphs.

SyntaxNet serves as a neural-network-based Natural Language Processing framework for TensorFlow.

Word2Vec is utilized for learning vector representations of words, known as 'word embeddings'. It is a highly computationally efficient predictive model for deriving word embeddings from raw text. It offers two variants: the Continuous Bag-of-Words model (CBOW) and the Skip-Gram model (refer to Chapters 3.1 and 3.2 in Mikolov et al.).

By combining SyntaxNet and Word2Vec, users can generate learned embedding models from Natural Language inputs.

Audience

This course is designed for developers and engineers who plan to work with SyntaxNet and Word2Vec models within their TensorFlow graphs.

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

• understand the structure and deployment mechanisms of TensorFlow
• execute installation, production environment setup, architecture tasks, and configuration
• assess code quality, perform debugging, and monitor performance
• implement advanced production-like activities such as training models, embedding terms, building graphs, and logging

This program initiates with conceptual knowledge in neural networks and generally in machine learning algorithms, deep learning (algorithms and applications).

Part-1(40%) of this training is more focus on fundamentals, but will help you choosing the right technology : TensorFlow, Caffe, Theano, DeepDrive, Keras, etc.

Part-2(20%) of this training introduces Theano - a python library that makes writing deep learning models easy.

Part-3(40%) of the training would be extensively based on Tensorflow - API of Google's open source software library for Deep Learning. The examples and handson would all be made in TensorFlow.

Audience

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

After completing this course, delegates will:

• have a good understanding on deep neural networks(DNN), CNN and RNN
• understand TensorFlow’s structure and deployment mechanisms
• be able to carry out installation / production environment / architecture tasks and configuration
• be able to assess code quality, perform debugging, monitoring
• be able to implement advanced production like training models, building graphs and logging