TensorFlow Training in Cancun

This instructor-led, live training in Cancun (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 Cancun (online or onsite) is designed for intermediate-level data scientists and developers aiming to understand and implement deep learning techniques using the Google Colab environment.

Upon completing this training, participants will be capable of:

• Setting up and navigating Google Colab for deep learning initiatives.
• Grasping the core concepts of neural networks.
• Developing deep learning models using TensorFlow.
• Training and assessing deep learning models.
• Leveraging advanced TensorFlow features for deep learning.

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) to analyze computer images.

The course includes practical working examples.

This instructor-led live training in Cancun (online or onsite) is designed for data scientists interested in using TensorFlow to analyze potential fraud data.

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

• Develop a fraud detection model using Python and TensorFlow.
• Build linear regression models to predict fraud.
• Create an end-to-end AI application for analyzing fraud data.

This instructor-led, live training in Cancun (online or onsite) is aimed at developers and data scientists who wish to use Tensorflow 2.x to build predictors, classifiers, generative models, neural networks and so on.

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.

This course starts by providing conceptual knowledge of neural networks, as well as a broader understanding of machine learning algorithms and deep learning (including both algorithms and their applications).

Part-1 (40%) of this training focuses primarily on fundamentals, yet it will assist 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 using TensorFlow.

Audience

This course is designed for engineers who intend to utilize TensorFlow for their Deep Learning projects.

Upon completing this course, delegates will:

• gain a solid understanding of deep neural networks (DNN), CNNs, and RNNs.
• comprehend TensorFlow’s structure and deployment mechanisms.
• be capable of performing installation, production environment setup, architecture tasks, and configuration.
• be able to evaluate code quality, perform debugging, and monitor performance.
• be able to implement advanced production-level tasks such as training models, building graphs, and logging.