Deep Learning with TensorFlow Training Course

This four-day course provides an introduction to AI and its practical applications. Upon completion, participants have the option to extend their learning with an additional day dedicated to working on an AI project.

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

In this instructor-led, live training located in Turkey, participants will learn to utilize Python libraries for NLP while developing an application that processes images and generates captions.

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

• Design and code Deep Learning solutions for NLP using Python libraries.
• Create Python code to read large collections of images and generate keywords.
• Create Python code that produces captions from the detected keywords.

Audience

This course is tailored for Deep Learning researchers and engineers who wish to leverage readily available tools (primarily open-source solutions) for the analysis of computer-generated images.

The curriculum includes practical, working examples.

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

In this instructor-led live training in Turkey (online or onsite), participants will learn how to configure and use 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 serve other types of models beyond TensorFlow models.

This course delves into the practical application of TensorFlow in the context of image recognition, supported by concrete examples.

Audience

Designed for engineers aiming to leverage TensorFlow for image recognition tasks.

Upon completion of this course, participants will be capable of:

• Gaining a comprehensive understanding of TensorFlow’s architecture and deployment processes
• Executing installation, setting up production environments, and configuring system architecture
• Evaluating code quality while performing debugging and monitoring
• Implementing advanced production-level tasks such as model training, graph construction, and logging

This instructor-led live training in Turkey (available online or onsite) is intended for data scientists looking to advance from training single ML models to deploying multiple ML models into production.

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

• Install and configure TFX and its supporting third-party tools.
• Use TFX to create and manage a full machine learning production pipeline.
• Work with TFX components to execute modeling, training, inference serving, and deployment management.
• 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 built for TensorFlow.

Word2Vec is utilized to learn vector representations of words, known as "word embeddings." As a computationally efficient predictive model for acquiring word embeddings from raw text, Word2vec offers two primary variants: the Continuous Bag-of-Words model (CBOW) and the Skip-Gram model (refer to Chapters 3.1 and 3.2 in Mikolov et al.).

By combining SyntaxNet and Word2Vec, users can generate learned embedding models directly from natural language inputs.

Audience

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

After completing this course, participants will:

• gain a clear understanding of TensorFlow’s structure and deployment mechanisms
• be capable of handling installation, production environment setup, architectural tasks, and configuration
• develop skills in assessing code quality, debugging, and monitoring
• learn to implement advanced production-like procedures, including training models, embedding terms, constructing graphs, and logging

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