TensorFlow Training in Rome

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

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.