Deep Learning with TensorFlow Training Course

This four-day course provides an introduction to Artificial Intelligence and its practical applications. An additional day is available to undertake an AI project upon completion of the course.

This instructor-led, live training in Portugal (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 Portugal (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 Portugal, participants will learn to utilize Python libraries for NLP by building an application that processes a set of images and generates captions.

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

• Design and implement DL for NLP using Python libraries.
• Write Python code that reads a large volume of images and generates keywords.
• Develop Python code that generates captions based on the detected keywords.

Target Audience

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

It provides practical, working examples.

This instructor-led, live training in Portugal (online or onsite) is designed for data scientists who wish to utilise TensorFlow to analyse 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 analysing fraud data.

This instructor-led live training in Portugal (online or onsite) is intended for developers and data scientists who wish to utilize TensorFlow 2.x to build 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 at Portugal (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.

This course examines, through concrete examples, how to apply TensorFlow for image recognition tasks.

Audience

This course is designed for engineers who wish to leverage TensorFlow for image recognition purposes.

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

• grasp the structure and deployment mechanisms of TensorFlow
• execute installation, production environment, and architecture tasks and configuration
• evaluate code quality, perform debugging, and monitoring
• implement advanced production practices such as training models, building graphs, and logging

This instructor-led live training in Portugal (online or on-site) is aimed at data scientists who wish to progress from training a single ML model to deploying numerous ML models into production.

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

• Install and configure TFX and supporting third-party tools.
• Use TFX to create and manage a complete ML production pipeline.
• Work with TFX components to carry out modeling, training, serving inference, and managing deployments.
• Deploy machine learning features to web applications, mobile applications, IoT devices and more.

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 built for TensorFlow.

Word2Vec facilitates the learning of vector representations for words, known as "word embeddings". It offers a highly computationally efficient predictive model for deriving word embeddings from raw text. This technology is available in two variations: the Continuous Bag-of-Words model (CBOW) and the Skip-Gram model (refer to Chapters 3.1 and 3.2 in Mikolov et al.).

When employed 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 integrate SyntaxNet and Word2Vec models into their TensorFlow graphs.

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

• Grasp the structure and deployment mechanisms of TensorFlow
• Execute installation, production environment setup, architecture tasks, and configuration
• Assess code quality, perform debugging, and implement monitoring
• Implement advanced production-level tasks such as training models, embedding terms, constructing graphs, and logging

This course provides a foundational understanding of neural networks, machine learning algorithms, and deep learning (both algorithms and applications).

Part 1 (40%) of this training focuses on fundamentals, helping you select the appropriate technology 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 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 looking to utilize TensorFlow for their deep learning projects.

Upon completion, participants will be able to:

• Gain a solid understanding of deep neural networks (DNN), CNNs, and RNNs
• Comprehend TensorFlow’s structure and deployment mechanisms
• Perform installation, production environment setup, architecture tasks, and configuration
• Assess code quality, debug, and monitor systems
• Implement advanced production-like tasks such as training models, building graphs, and logging