TensorFlow Extended (TFX) Training Course

This four-day course provides an introduction to AI and its applications. Upon completion, there is an option to add an extra day to undertake a practical AI project.

This instructor-led live training in Brazil (online or onsite) targets advanced professionals seeking 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 Brazil (online or on-site) targets 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 Brazil, participants will learn to use Python libraries for NLP by creating 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 reads a substantial collection of images and generates keywords.
• Create Python code that generates captions from the detected keywords.

Audience

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

The course provides practical, working examples.

This instructor-led live training in Brazil (online or onsite) targets data scientists who wish 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 in Brazil (online or onsite) is tailored for developers and data scientists aiming to utilize TensorFlow 2.x to build predictors, classifiers, generative models, neural networks, and other 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 Brazil (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 from Google's open-source deep learning library. The system is designed to facilitate machine learning research and streamline the transition from research prototypes to production systems.

Audience

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

After completing this course, delegates will:

• 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

This course delves into the practical application of TensorFlow for image recognition, illustrated with specific examples.

Target Audience

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

Upon completion, participants will be able to:

• Comprehend the structure and deployment mechanisms of TensorFlow
• Execute installation procedures, configure production environments, and manage architecture
• Evaluate code quality, perform debugging, and set up monitoring
• Implement advanced production practices, such as 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-based Natural Language Processing framework for TensorFlow.

Word2Vec is utilized for learning vector representations of words, known as 'word embeddings'. It is a computationally efficient predictive model for acquiring word embeddings from raw text, available in two variants: 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 be able to:

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

This course provides foundational conceptual knowledge of neural networks, machine learning algorithms, and deep learning (including both algorithms and their practical applications).

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

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

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

Audience

This course is designed for engineers who intend to utilize TensorFlow for their deep learning projects.

Upon completion of this course, participants will:

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