Deep Learning with TensorFlow 2 Training Course

This is a four-day course that introduces AI and its applications. There is an option to have an additional day to undertake an AI project on completion of this course.

This instructor-led, live training in Slovakia (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.

Upon completing this training, participants will be able to:

• Construct and train convolutional neural networks (CNNs) utilizing TensorFlow.
• Utilize Google Colab for scalable and efficient cloud-based model development.
• Apply image preprocessing techniques suitable for computer vision tasks.
• Deploy computer vision models for real-world applications.
• Employ transfer learning to boost the performance of CNN models.
• Visualize and interpret the outcomes of image classification models.

This instructor-led, live training in Slovakia (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.

In this instructor-led live training located in Slovakia, participants will learn to use Python libraries for NLP by creating an application that processes a set of pictures and generates captions.

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

• Design and code DL for NLP using Python libraries.
• Create Python code that reads a substantially huge collection of pictures and generates keywords.
• Create Python Code that generates captions from the detected keywords.

Audience

This course is ideal for deep learning researchers and engineers who wish to leverage available tools (primarily open-source) to analyze computer images.

The course offers practical, working examples.

This instructor-led, live training in Slovakia (online or onsite) is aimed at data scientists who wish 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.

In this instructor-led, live training held in Slovakia (online or on-site), 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.

TensorFlow serves as the second-generation API within Google’s open-source library for Deep Learning. It is engineered to accelerate machine learning research, enabling a swift and seamless transition from research prototypes to production-ready systems.

Target Audience

This course is designed for engineers who wish to leverage TensorFlow for their Deep Learning initiatives.

Upon completion of this course, participants will be able to:

• Grasp TensorFlow’s architecture and deployment strategies
• Execute installation, production environment setup, and architectural configuration tasks
• Evaluate code quality and perform debugging and monitoring
• Implement advanced, production-grade processes such as model training, graph construction, and logging

This course examines the practical application of TensorFlow in image recognition through concrete examples.

Audience

This course is designed for engineers who wish to leverage TensorFlow for image recognition tasks.

Upon completion of this course, participants will be able to:

• comprehend TensorFlow’s architecture and deployment mechanisms
• execute installation, production environment setup, and architectural configuration tasks
• evaluate code quality, perform debugging, and implement monitoring
• implement advanced production practices, such as model training, graph construction, and logging

This instructor-led live training in Slovakia (online or onsite) is aimed at data scientists who wish to transition from training a single ML model to deploying many ML models to production.

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

• Install and configure TFX and supporting third-party tools.
• Use TFX to create and manage a complete ML production pipeline.
• Work with TFX components to carry out modeling, training, serving inference, and managing deployments.
• Deploy machine learning features to web applications, mobile applications, IoT devices and more.

TensorFlow™ is an open-source software library designed for numerical computation using data flow graphs.

SyntaxNet serves as a neural-network framework for Natural Language Processing built on TensorFlow.

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

When used together, SyntaxNet and Word2Vec enable users to generate learned embedding models directly from natural language input.

Audience

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

Upon completion of this course, participants will be able to:

• grasp the structure and deployment mechanisms of TensorFlow
• perform installation, production environment setup, architecture design, and configuration
• assess code quality, conduct debugging, and monitor performance
• implement advanced production-grade tasks such as training models, embedding terms, building graphs, and logging

This course provides a solid conceptual foundation in neural networks, machine learning algorithms, and deep learning (both algorithms and applications).

Part 1 (40%) of this training focuses heavily on fundamentals, which will assist you in selecting the appropriate technology stack: TensorFlow, Caffe, Theano, DeepDrive, Keras, and others.

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, 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 aim to leverage TensorFlow for their Deep Learning projects.

Upon completing this course, participants will:

• possess a comprehensive understanding of deep neural networks (DNN), CNNs, and RNNs
• comprehend the structure and deployment mechanisms of TensorFlow
• be capable of performing installation, production environment setup, architecture tasks, and configuration
• be able to assess code quality, perform debugging, and monitor systems
• be able to implement advanced production-like tasks such as training models, building graphs, and logging