TensorFlow Training in Plovdiv

This live, instructor-led training in Plovdiv (online or onsite) is aimed at experienced professionals who wish to deepen their understanding 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 Plovdiv (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 4-day course introducing AI and its application. There is an option to have an additional day to undertake an AI project on completion of this course.

During this instructor-led live training in Plovdiv, participants will learn to utilize Python libraries for NLP by creating 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 aim to leverage available tools (primarily open-source solutions) for analyzing visual data.

The curriculum includes practical, hands-on examples.

This instructor-led live training in Plovdiv (online or on-site) targets data scientists aiming to utilize TensorFlow for analyzing potential fraud data.

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

• Build a fraud detection model using Python and TensorFlow.
• Implement linear regressions and models to predict fraud.
• Develop a complete AI application for fraud data analysis.

This instructor-led live training in Plovdiv (available online or onsite) is designed for developers and data scientists who wish to utilize TensorFlow 2.x to build predictors, classifiers, generative models, neural networks, and related 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.

In this instructor-led live training at Plovdiv (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 represents the second-generation API of Google's open-source library dedicated to Deep Learning. Designed to streamline machine learning research, it enables a seamless and efficient transition from research prototypes to fully deployed production systems.

Audience

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

Upon completing this course, participants will:

• Gain a comprehensive understanding of TensorFlow’s architecture and deployment mechanisms
• Be proficient in executing installation, production environment setup, architectural design, and configuration tasks
• Acquire skills to evaluate code quality, perform debugging, and implement monitoring strategies
• Master the implementation of advanced production-level tasks, including model training, graph construction, and logging

This course provides a detailed examination of how TensorFlow can be applied to image recognition tasks, supported by specific examples.

Target Audience

The program is designed for engineers who wish to leverage TensorFlow for image recognition solutions.

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

• Comprehend the architecture and deployment mechanisms of TensorFlow
• Execute installation, production environment setup, architectural planning, and configuration
• Evaluate code quality, conduct debugging, and implement monitoring
• Implement advanced production workflows, including model training, graph construction, and logging

This instructor-led, live training in Plovdiv (online or on-site) is tailored for data scientists looking to scale from training a single ML model to deploying multiple models into 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 comprehensive ML production pipeline.
• Work with TFX components to perform 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 framework for Natural Language Processing built upon TensorFlow.

Word2Vec is utilized for learning vector representations of words, known as "word embeddings." Word2vec is a computationally efficient predictive model that learns these embeddings from raw text. It offers two variations: the Continuous Bag-of-Words model (CBOW) and the Skip-Gram model (as detailed in Chapters 3.1 and 3.2 of 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:

• gain a thorough understanding of TensorFlow’s structure and deployment mechanisms
• possess the skills to perform installation, production environment setup, architecture tasks, and configuration
• be capable of assessing code quality, conducting debugging, and monitoring performance
• learn to implement advanced production practices, such as training models, embedding terms, constructing graphs, and logging

This course starts by providing conceptual knowledge about neural networks and machine learning algorithms, including deep learning (algorithms and applications).

Part-1 (40%) of this training focuses more on fundamentals, but will help you choose 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 will be extensively based on TensorFlow, Google's open-source software library API for Deep Learning. All examples and hands-on exercises will be done 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 of 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, and monitoring.
• be able to implement advanced production-like training models, building graphs, and logging.