TensorFlow Extended (TFX) Training Course

This four-day course provides an introduction to Artificial Intelligence and its practical applications. Participants also have the option to extend the training by one additional day to work on a hands-on AI project upon completion of the course.

This instructor-led, live training in France (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 France (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 France, 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 ideal for deep learning researchers and engineers who wish to leverage accessible tools, primarily open-source solutions, to analyze computer images.

The course includes practical, hands-on examples.

This instructor-led live training in France (online or onsite) targets data scientists who want 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 France (online or onsite) is designed for developers and data scientists who aim to utilize TensorFlow 2.x to build predictors, classifiers, generative models, neural networks, and other applications.

By the end 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 held in France (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 the second-generation API of Google's open-source software library for Deep Learning. The system is designed to facilitate research in machine learning, and to make it quick and easy to transition from research prototype to production system.

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 provides a detailed examination of applying TensorFlow to image recognition tasks, supported by concrete examples.

Audience

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

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

• Grasp the structural framework and deployment mechanisms of TensorFlow
• Perform installation, set up production environments, and configure architecture
• Evaluate code quality, and conduct debugging and monitoring
• Implement advanced production processes, including training models, constructing graphs, and logging

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

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

Word2Vec facilitates the learning of vector representations for words, known as "word embeddings." It is a highly computationally efficient predictive model for deriving these embeddings from raw text. The library offers two main variants: the Continuous Bag-of-Words (CBOW) model and the Skip-Gram model (as detailed in Chapters 3.1 and 3.2 of 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.

After completing this course, participants will be able to:

• understand the structure and deployment mechanisms of TensorFlow
• perform installation, configure production environments, and manage architecture tasks
• assess code quality, conduct debugging, and implement monitoring
• implement advanced production-level tasks such as training models, embedding terms, building graphs, and setting up logging

This course provides foundational knowledge on neural networks, machine learning algorithms, and the principles and applications of deep learning.

Part 1 (40%) of the training emphasizes fundamentals, enabling you to select the appropriate technology stack, such as TensorFlow, Caffe, Theano, DeepDrive, or Keras.

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

Part 3 (40%) focuses extensively on TensorFlow, Google's open-source software library API 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 of this course, participants will:

• possess a solid understanding of deep neural networks (DNN), CNNs, and RNNs
• comprehend TensorFlow’s architecture and deployment mechanisms
• be capable of managing installation, production environments, and architectural configurations
• be able to evaluate code quality, perform debugging, and monitor performance
• be proficient in implementing advanced production tasks such as training models, constructing graphs, and logging