TensorFlow Training in Turin

This instructor-led, live training in Turin (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 Turin (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.

This four-day course provides an introduction to AI and its applications. An optional fifth day is available to complete an AI project upon finishing the course.

In this instructor-led live training in Turin, participants will learn how to use Python libraries for NLP by creating 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

Designed for Deep Learning researchers and engineers who wish to leverage readily available tools, primarily open-source solutions, for computer image analysis.

The course includes practical, working examples to facilitate hands-on learning.

This instructor-led, live training in Turin (online or onsite) is tailored for 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 Turin (online or on-site) targets developers and data scientists who intend to use TensorFlow 2.x for building predictors, classifiers, generative models, neural networks, and related 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 held at Turin (online or onsite), participants will learn how to configure and utilize 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 accommodate other types of models beyond TensorFlow models.

TensorFlow represents a second-generation API within Google’s open-source software library for Deep Learning. It is engineered to streamline machine learning research, enabling teams to rapidly move from research prototypes to robust production systems.

Audience

This course is designed for engineers aiming to leverage TensorFlow for their Deep Learning initiatives.

Upon completion of this course, delegates will:

• gain a comprehensive understanding of TensorFlow’s structure and deployment mechanisms
• possess the skills to execute installation, production environment setup, architecture planning, and configuration tasks
• be capable of assessing code quality, as well as performing debugging and monitoring
• be able to implement advanced production-level practices, including model training, graph construction, and logging

This course investigates, through concrete examples, the application of TensorFlow to image recognition tasks.

Target Audience

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

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

• comprehend TensorFlow's structure and deployment mechanisms
• execute installation, production environment setup, and architectural configuration
• evaluate code quality, perform debugging, and implement monitoring
• implement advanced production practices such as model training, graph construction, and logging

This instructor-led, live training in Turin (online or onsite) is aimed at data scientists who wish to go from training a single ML model to deploying many ML models to 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 for TensorFlow.

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

By employing SyntaxNet and Word2Vec together, users can generate learned embedding models derived from Natural Language input.

Audience

This course is designed for developers and engineers who plan to integrate SyntaxNet and Word2Vec models into their TensorFlow workflows.

Upon completing this course, participants will be able to:

• understand the structure and deployment mechanisms of TensorFlow
• execute installation, production environment setup, architecture design, and configuration tasks
• evaluate code quality, perform debugging, and implement monitoring
• implement advanced production features such as model training, term embedding, graph construction, and logging

This course provides a conceptual foundation in neural networks, machine learning algorithms, and deep learning (algorithms and applications).

Part-1 (40%) of this training focuses heavily on fundamentals, aiding you in selecting the appropriate technology: TensorFlow, Caffe, Theano, DeepDrive, Keras, etc.

Part-2 (20%) of this training introduces Theano - a Python library that simplifies the creation of deep learning models.

Part-3 (40%) of the training is heavily centered on Tensorflow - the API for Google's open-source software library for Deep Learning. All examples and hands-on exercises will be conducted in TensorFlow.

Audience

This course is designed for engineers looking to leverage TensorFlow for their Deep Learning projects.

Upon completion of this course, delegates will:

• possess a solid understanding of deep neural networks (DNN), CNN, and RNN.
• comprehend the structure and deployment mechanisms of TensorFlow.
• be capable of performing installation, production environment setup, architecture tasks, and configuration.
• be able to evaluate code quality, perform debugging, and monitoring.
• be able to implement advanced production-grade tasks such as model training, graph construction, and logging.