Deep Learning with TensorFlow Training Course

This four-day course provides an introduction to artificial intelligence (AI) and its applications for government. Participants have the option to extend their learning by one additional day to work on an AI project upon completing the initial course.

This instructor-led, live training (online or onsite) is designed for advanced-level professionals who aim 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 training is tailored to ensure that professionals can apply these skills effectively in their roles, particularly in projects for government.

This instructor-led, live training in [location] (online or onsite) is designed for intermediate-level data scientists and developers who aim 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 government 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 session, participants will learn to utilize Python libraries for natural language processing (NLP) as they develop an application that processes a set of images and generates descriptive captions. By the end of this training, participants will be able to: - Design and code deep learning models for NLP using Python libraries. - Create Python scripts that analyze a large collection of images and extract relevant keywords. - Develop Python code that generates coherent captions based on the identified keywords. This training is designed to enhance technical skills in data processing and machine learning, specifically tailored for government applications.

Audience

This course is designed for deep learning researchers and engineers who are interested in leveraging available tools, primarily open source, to analyze computer images. The course provides practical, working examples to enhance skills and knowledge for government and other public sector applications.

This instructor-led, live training (available online or onsite) is designed for government data scientists who wish to utilize TensorFlow to analyze potential fraud data. By the end of this training, participants will be able to: - Develop a fraud detection model using Python and TensorFlow. - Construct linear regressions and linear regression models to predict fraudulent activities. - Create an end-to-end AI application for analyzing fraud data for government use.

This instructor-led, live training (online or onsite) is aimed at developers and data scientists who wish to utilize TensorFlow 2.x for government to build predictors, classifiers, generative models, neural networks, and more. By the end of this training, participants will be able to: - Install and configure TensorFlow 2.x. - Understand the advantages of TensorFlow 2.x over previous versions. - Construct deep learning models. - Implement an advanced image classifier. - Deploy a deep learning model to the cloud, mobile devices, and IoT systems.

In this instructor-led, live training in US (online or onsite), participants will learn how to configure and use TensorFlow Serving to deploy and manage machine learning models in a production environment for government.

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

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

This course delves into the application of TensorFlow for image recognition, providing specific examples to enhance understanding. **Audience** This course is designed for engineers who aim to leverage TensorFlow for government purposes in the field of Image Recognition. **Learning Objectives** Upon completing this course, participants will be able to: - Understand TensorFlow’s architecture and deployment processes - Perform installation, production environment setup, and configuration tasks - Evaluate code quality, conduct debugging, and implement monitoring - Execute advanced production tasks such as training models, constructing graphs, and logging for government applications

This instructor-led, live training in [location] (online or onsite) is aimed at data scientists who wish to transition from training a single machine learning (ML) model to deploying multiple ML models into production environments. By the end of this training, participants will be able to: - Install and configure TensorFlow Extended (TFX) and supporting third-party tools for government use. - Use TFX to create and manage a comprehensive ML production pipeline. - Work with TFX components to conduct modeling, training, serving inference, and managing deployments. - Deploy machine learning features to web applications, mobile applications, IoT devices, and other platforms.

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

SyntaxNet is a neural-network framework for natural language processing that operates within TensorFlow.

Word2Vec is a method used to learn vector representations of words, known as "word embeddings." Word2Vec is particularly efficient in learning these embeddings from raw text and offers two models: the Continuous Bag-of-Words (CBOW) model and the Skip-Gram model (as detailed in Chapter 3.1 and 3.2 of Mikolov et al.).

When used together, SyntaxNet and Word2Vec enable the generation of learned embedding models from natural language input.

Audience

This course is designed for developers and engineers who intend to work with SyntaxNet and Word2Vec models within their TensorFlow graphs for government applications.

After completing this course, participants will:

• Understand TensorFlow’s structure and deployment mechanisms
• Be capable of performing installation, production environment setup, architectural tasks, and configuration
• Be able to evaluate code quality, perform debugging, and monitoring
• Be proficient in implementing advanced production tasks such as training models, embedding terms, building graphs, and logging

This course begins with an introduction to the conceptual knowledge of neural networks and machine learning algorithms, including deep learning (algorithms and applications). Part-1 (40%) of this training focuses on fundamental concepts, which will assist in selecting the appropriate technology: TensorFlow, Caffe, Theano, DeepDrive, Keras, etc. Part-2 (20%) introduces Theano, a Python library that simplifies the process of writing deep learning models. Part-3 (40%) is extensively based on TensorFlow, Google's open-source software library for deep learning. All examples and hands-on exercises will be conducted using TensorFlow. **Audience** This course is designed for engineers who intend to use TensorFlow for their deep learning projects for government. Upon completing this course, participants will: - Have a comprehensive understanding of deep neural networks (DNN), convolutional neural networks (CNN), and recurrent neural networks (RNN). - Understand the structure and deployment mechanisms of TensorFlow. - Be capable of performing installation, production environment setup, architecture tasks, and configuration. - Be able to assess code quality, perform debugging, and monitoring. - Be proficient in implementing advanced production tasks such as training models, building graphs, and logging.