TensorFlow Training in Brasilia

This instructor-led live training in Brasilia (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 Brasilia (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.

This instructor-led live training in Brasilia (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 Brasilia (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.

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