TensorFlow Extended (TFX) Training Course

This is a four-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 Romania (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 Romania (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 Romania, 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 researchers and engineers specializing in Deep Learning who wish to leverage available tools (primarily open-source solutions) to analyze computer-generated images.

The course provides practical, working examples.

This instructor-led live training, offered Romania (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 Romania (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.

In this instructor-led, live training in Romania (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 a second-generation API within Google's open-source library for Deep Learning. This system is built to support machine learning research and to streamline the transition from research prototypes to production systems.

Audience

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

Upon completion of this course, delegates will:

• gain a thorough understanding of TensorFlow's architecture and deployment mechanisms
• possess the skills to perform installation, configure production environments and architectures
• be capable of assessing code quality, debugging, and monitoring systems
• know how to implement advanced production-level tasks such as training models, constructing graphs, and logging

This course examines the practical application of TensorFlow in image recognition, illustrated with concrete examples.

Target Audience

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

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

• grasp the structural framework and deployment mechanisms of TensorFlow
• execute installation, production environment setup, and architectural configuration
• evaluate code quality, conduct debugging, and implement monitoring
• deploy advanced production workflows, including model training, graph construction, and logging

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

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

Word2Vec facilitates the learning of vector representations for words, known as "word embeddings." It offers a computationally efficient predictive model for deriving word embeddings from raw text. The model 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.).

By utilizing SyntaxNet and Word2Vec together, users can generate Learned Embedding models from Natural Language inputs.

Audience

This course is designed for developers and engineers who plan to integrate SyntaxNet and Word2Vec models into their TensorFlow graphs.

Upon completion of this course, delegates will:

• understand TensorFlow’s architecture and deployment mechanisms
• be capable of executing installation, production environment setup, and configuration tasks
• be able to evaluate 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 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