TensorFlow Extended (TFX) Training Course

This is a 4 day course introducing AI and it's application. There is an option to have an additional day to undertake an AI project on completion of this course.

This instructor-led, live training in Uzbekistan (online or onsite) is aimed at advanced-level 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 Uzbekistan (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.

In this instructor-led, live training in Uzbekistan, participants will learn to use Python libraries for NLP as they create 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.

Audience

This course is designed for Deep Learning researchers and engineers who wish to leverage available tools (primarily open source) to analyse computer images.

The course includes practical, working examples.

This instructor-led live training, delivered Uzbekistan (online or onsite), targets data scientists aiming to utilize TensorFlow for the analysis of 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 in Uzbekistan (online or on-site) is designed for developers and data scientists who aim to use TensorFlow 2.x to build predictors, classifiers, generative models, neural networks, and more.

Upon completing this training, participants will be able to:

• Install and configure TensorFlow 2.x.
• Understand the advantages of TensorFlow 2.x compared to earlier versions.
• Develop deep learning models.
• Implement advanced image classifiers.
• Deploy deep learning models to cloud platforms, mobile devices, and IoT systems.

In this instructor-led, live training in Uzbekistan (available online or on-site), participants will learn how to configure and utilize TensorFlow Serving to effectively deploy and manage ML models within a production setting.

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

• Train, export, and serve a variety of TensorFlow models.
• Test and deploy algorithms using a unified architecture and a consistent set of APIs.
• Extend TensorFlow Serving to support other types of models beyond those built with TensorFlow.

TensorFlow is a second-generation API from Google's open-source deep learning library. Designed to streamline machine learning research, it enables a rapid and seamless transition from prototype development to production deployment.

Audience

This course is tailored for engineers looking to leverage TensorFlow for their deep learning initiatives.

Upon completing this course, participants will be able to:

• Grasp the architecture and deployment mechanisms of TensorFlow.
• Execute installation, configuration, and architectural setup for production environments.
• Evaluate code quality, perform debugging, and monitor system performance.
• Implement advanced production-level tasks, such as training models, constructing graphs, and setting up logging.

This course explores, with specific examples, the application of TensorFlow for image recognition purposes

Audience

This course is intended for engineers seeking to utilise TensorFlow for image recognition

After completing this course, delegates will be able to:

• understand TensorFlow's structure and deployment mechanisms
• carry out installation, production environment, and architecture tasks and configuration
• assess code quality, perform debugging, and monitoring
• implement advanced production tasks such as training models, building graphs, and logging

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

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

Word2Vec is used to learn vector representations of words, known as "word embeddings". Word2Vec is a highly computationally efficient predictive model for learning word embeddings from raw text. It is available in two variants: the Continuous Bag-of-Words (CBOW) model and the Skip-Gram model (see 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.

Upon completing this course, participants will be able to:

• Understand TensorFlow's architecture and deployment mechanisms
• Perform installation, configure production environments, and manage architectural tasks
• Evaluate code quality, conduct debugging, and implement monitoring
• Implement advanced production practices such as model training, embedding term integration, graph construction, and logging

This course provides conceptual knowledge of neural networks, machine learning algorithms, and deep learning (algorithms and applications).

Part-1 (40%) of this training focuses on fundamentals but helps 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 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