TensorFlow Training in Antalya

This instructor-led, live training in Antalya (available online or onsite) is designed for advanced professionals aiming to expand their expertise in computer vision and investigate TensorFlow's potential for creating sophisticated vision models via Google Colab.

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

• Construct 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 Antalya (online or onsite) targets 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:

• Configure and navigate Google Colab for deep learning projects.
• Grasp the core principles of neural networks.
• Develop deep learning models utilizing TensorFlow.
• Train and assess deep learning models.
• Leverage advanced TensorFlow features for deep learning applications.

This instructor-led, live training in Antalya (online or onsite) targets data scientists aiming to use TensorFlow for analyzing potential fraud data.

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

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

This instructor-led live training in Antalya (online or onsite) is designed for developers and data scientists who intend to use TensorFlow 2.x to build predictors, classifiers, generative models, neural networks, and related 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.

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

Part 1 (40%) of the training focuses on fundamentals, assisting you in selecting the appropriate technology, such as TensorFlow, Caffe, Theano, DeepDrive, and Keras.

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

Part 3 (40%) is extensively dedicated to TensorFlow, the open-source software library for Deep Learning developed by Google. All examples and hands-on exercises will utilize TensorFlow.

Audience

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

Upon completing 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 handling installation, production environment setup, architecture design, and configuration
• be able to assess code quality, perform debugging, and monitor performance
• be able to implement advanced production tasks, including model training, graph construction, and logging