Generative AI represents an exhilarating frontier in artificial intelligence, specializing in the creation of new data instances, imitation of real data, and content generation. Its remarkable capabilities facilitate automated content creation, enriched user experiences, and groundbreaking solutions across diverse industries, ultimately fueling efficiency and transcending technological limits. By harnessing the power of generative AI, developers can craft dynamic content, produce code and documentation, refine user interfaces, and devise customized recommendations, empowering them to construct highly efficient and custom solutions for a wide range of applications. Designed for experienced programmers, Turbocharge Your Code! Generative AI Boot Camp for Developers is a three-day workshop-style course that teaches you the latest skills and tools required to master generative AI models, transforming the way you approach software development.

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* Actual course outline may vary depending on offering center. Contact your sales representative for more information.

Learning Objectives

Working in an interactive learning environment, led by our engaging AI expert you’ll:
Develop a strong foundational understanding of generative AI techniques and their applications in software development.
Gain hands-on experience working with popular generative AI models, including Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and Transformer models.
Master the use of leading AI libraries and frameworks, such as TensorFlow, Keras, and Hugging Face Transformers, for implementing generative AI models.
Acquire the skills to design, train, optimize, and evaluate custom generative AI models tailored to specific software development tasks.
Learn to fine-tune pre-trained generative AI models for targeted applications and deploy them effectively in various environments, including cloud-based services and on-premises servers.
Understand and address the ethical, legal, and safety considerations of using generative AI, including mitigating biases and ensuring responsible AI-generated content.

1

  • INTRODUCTION TO GENERATIVE AI

  • Unveil the world of generative AI and its applications.

    Brief history of generative AI

    Overview of generative models

    Types of generative AI techniques

    Applications of generative AI

2

  • VARIATIONAL AUTOENCODERS (VAES)

  • Explore VAEs and learn their applications in generative AI.

    Introduction to VAEs

    VAE architecture and training process

    Applications of VAEs

    Comparing VAEs and GANs

3

  • DEEP LEARNING AND GANS

  • Dive into the fundamentals of GANs and their applications.

    Introduction to deep learning

    Basic components of GANs

    GAN architecture and training process

    Common GAN variants and applications

4

  • NATURAL LANGUAGE GENERATION (NLG)

  • Uncover the power of NLG and its applications in generative AI.

    Introduction to NLG

    Overview of language models

    Transformer architecture and variants

    Applications of NLG in generative AI

5

  • ETHICS AND RESPONSIBLE AI

  • Understand the ethical implications of generative AI applications.

    AI ethics and its importance

    Bias in generative models

    Responsible AI and best practices

    Future research and open problems

6

  • MULTIMODAL GENERATIVE AI

  • Discover the potential of combining different data modalities in generative AI.

    Introduction to multimodal AI

    Text-to-image synthesis

    Audio-to-video synthesis

    Applications of multimodal generative AI

7

  • STYLE TRANSFER AND NEURAL ART

  • Explore the creative side of generative AI with style transfer techniques.

    Introduction to style transfer

    Neural style transfer algorithms

    Applications of style transfer in generative AI

    Limitations and future directions

8

  • GENERATIVE AI IN THE REAL WORLD

  • Gain insights on practical applications of generative AI across various domains.

    Generative AI in marketing and advertising

    Generative AI in entertainment and gaming

    Generative AI in healthcare and life sciences

    Generative AI in finance and economics

9

  • PULLING IT ALL TOGETHER: BUILDING AND DEPLOYING GENERATIVE AI MODELS

  • Learn best practices for building, fine-tuning, and deploying generative AI models.

    Model selection and fine-tuning

    Deployment strategies

    Monitoring and maintenance

    Ensuring user privacy and security

Audience

Attendee roles might include: Software Developers/Programmers Data Scientists Machine Learning Engineers AI Researchers User Interface (UI) and User Experience (UX) Designers Technical Product Managers Technical Team Leads

Language

English

Prerequisites

This course is highly technical in nature. In order to gain the most from attending you should possess the following incoming skills: Python programming experience (Python syntax and constructs, experience with NumPy and Pandas) Basic understanding of artificial intelligence and machine learning concepts (supervised and unsupervised learning, neural networks, optimization techniques) Some experience with data manipulation and preprocessing , including working with various data formats, such as text, images, and structured data, preprocessing and cleaning data for use in machine learning models. Take Before: You should have incoming skills aligned with the topics in the course(s) below, or should attend as a prerequisite: TTML5503 AI & Machine Learning JumpStart | Introduction to AI, AI Programming & Machine Learning (3 days) TTPS4873 Fast Track to Python in Data Science (3 days)

$2,395

Length: 3.0 days (24 hours)

Level:

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