TensorFlow Training in Varna

This live, instructor-led training in Varna (online or onsite) is aimed at experienced 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 Varna (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 Varna (online or on-site) targets data scientists aiming to utilize TensorFlow for analyzing potential fraud data.

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

• Build a fraud detection model using Python and TensorFlow.
• Implement linear regressions and models to predict fraud.
• Develop a complete AI application for fraud data analysis.

This instructor-led live training in Varna (available online or onsite) is designed for developers and data scientists who wish to utilize TensorFlow 2.x to build 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 starts by providing conceptual knowledge about neural networks and machine learning algorithms, including deep learning (algorithms and applications).

Part-1 (40%) of this training focuses more on fundamentals, but will help you choose the right technology: TensorFlow, Caffe, Theano, DeepDrive, Keras, etc.

Part-2 (20%) of this training introduces Theano, a Python library that makes writing deep learning models easy.

Part-3 (40%) of the training will be extensively based on TensorFlow, Google's open-source software library API for Deep Learning. All examples and hands-on exercises will be done in TensorFlow.

Audience

This course is intended for engineers seeking to use TensorFlow for their Deep Learning projects.

After completing this course, delegates will:

• have a good understanding of deep neural networks (DNN), CNN, and RNN.
• 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, and monitoring.
• be able to implement advanced production-like training models, building graphs, and logging.