TensorFlow Training in Patras

This instructor-led live training in Patras (online or onsite) is designed for advanced professionals who wish to expand their expertise in computer vision and investigate TensorFlow's capabilities for developing advanced 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 for scalable and efficient cloud-based model development.
• Apply image preprocessing techniques specifically for computer vision applications.
• Deploy computer vision models for practical, real-world solutions.
• Employ transfer learning to optimize the performance of CNN models.
• Visualize and interpret outcomes from image classification models.

This instructor-led, live training in Patras (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 is a four-day course introducing AI and its applications. There is an option to have an additional day to undertake an AI project upon completion of this course.

In this instructor-led live training, participants will learn to use Python libraries for NLP by building an application that processes a set of pictures and generates captions.

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

• Design and code DL for NLP using Python libraries.
• Create Python code that reads a substantially huge collection of pictures and generates keywords.
• Create Python Code that generates captions from the detected keywords.

Target Audience

This course is designed for Deep Learning researchers and engineers who wish to leverage existing tools (primarily open-source solutions) for analyzing visual data.

The course includes practical, working examples.

This instructor-led, live training in Patras (online or onsite) targets data scientists who wish to utilize TensorFlow for analyzing potential fraud data.

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

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

This instructor-led, live training in Patras (online or on-site) is designed for developers and data scientists who intend to use 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.

In this instructor-led live training held in Patras (online or onsite), participants will learn how to configure and utilize 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 represents Google's second-generation API within its open-source software library dedicated to Deep Learning. This system is engineered to support machine learning research, enabling a swift and seamless transition from research prototypes to production-grade systems.

Target Audience

This course is designed for engineers who wish to leverage TensorFlow for their Deep Learning initiatives.

Upon completing this course, delegates will:

• Grasp the structure and deployment mechanisms of TensorFlow.
• Perform installation, production environment setup, architecture planning, and configuration tasks.
• Assess code quality, execute debugging, and implement monitoring practices.
• Implement advanced production-level processes such as training models, constructing graphs, and handling logging.

This course examines, through specific examples, how to apply TensorFlow to image recognition tasks.

Audience

This course is designed for engineers who wish to leverage TensorFlow for image recognition projects.

Upon completing this course, delegates will be able to:

• gain an understanding of TensorFlow’s architecture and deployment mechanisms
• manage installation, production environment setup, and architectural configuration
• assess code quality, perform debugging, and implement monitoring
• execute advanced production tasks such as training models, constructing graphs, and logging

This instructor-led live training in Patras (online or on-site) is designed for data scientists seeking to advance from training single ML models to deploying multiple models into production.

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

• Install and configure TFX and supporting third-party tools.
• Use TFX to create and manage a complete ML production pipeline.
• Work with TFX components to carry out modeling, training, serving inference, and managing deployments.
• Deploy machine learning features to web applications, mobile applications, IoT devices and more.

TensorFlow™ is an open-source software library designed for numerical computation using 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". Word2vec is a highly computationally efficient predictive model for acquiring word embeddings from raw text. It is available in 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.).

When used together, SyntaxNet and Word2Vec enable users to generate Learned Embedding models from Natural Language input.

Audience

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

After completing this course, delegates will:

• understand TensorFlow’s structure and deployment mechanisms
• be able to perform installation, production environment setup, architecture tasks, and configuration
• be able to assess code quality, perform debugging, and monitoring
• be able to implement advanced production-level tasks such as training models, embedding terms, building graphs, and logging

This course provides a conceptual foundation in neural networks and broadly covers machine learning algorithms, deep learning (algorithms and applications).

Part-1 (40%) of this training focuses heavily on fundamentals, helping you select the appropriate technology such as TensorFlow, Caffe, Theano, DeepDrive, Keras, and others.

Part-2 (20%) introduces Theano, a Python library designed to simplify the creation of deep learning models.

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

Audience

This course is designed for engineers aiming to utilize TensorFlow for their Deep Learning projects.

Upon completing this course, delegates will:

• possess a solid understanding of deep neural networks (DNN), CNNs, and RNNs
• comprehend TensorFlow’s structure and deployment mechanisms
• be capable of performing installation, production environment setup, architecture tasks, and configuration
• be able to assess code quality, perform debugging, and monitoring
• be able to implement advanced production-level tasks such as training models, building graphs, and logging