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Building Intelligent Systems with AI and Deep Learning

Use deep learning for building intelligent applications and services

Pablo Maldonado

With the recent advancements in the field of AI and deep learning, there has been a massive movement among organizations to make their systems intelligent. Intelligent systems driven by AI are becoming increasingly relevant in the modern world where everything is driven by technology and data. Intelligent apps and systems are being developed extensively across many fields such as healthcare, robotics, finance, and much more.

This live training session will introduce you to the recent advancements in the field of AI and Deep Learning and how these advancements can be applied in organizations to build intelligent systems. It will explore various real-world scenarios to implement some of the latest algorithms for building intelligent applications and services. In addition to learning some of the cutting edge algorithms, the audience would gain an understanding of what algorithm to use in a given context. Overall the session would help organizations get more productive with its developers embracing AI to automate mundane tasks and build intelligent applications.

What you'll learn-and how you can apply it

By the end of this live, online course, you’ll understand:

  • Introduction to Convergence of AI and deep learning
  • Understand AI and deep learning concepts
  • Learn ANN and deep learning models
  • Explore set of advance algorithms to build smart applications
  • Apply learning and best practices in different set of domains

And you’ll be able to:

  • Make your applications work smart leveraging the power of advanced algorithms
  • Bring AI to your systems and automate complex tasks
  • Get well versed with recent advancements and concepts to build efficient intelligent systems
  • Apply variety of AI and deep learning concepts to solve problems
  • Explore relevant case studies to build applications in your interest of domain

This training course is for you because...

  • You're a manager, data scientist, analysts and software developer looking to understand at a high level the main concepts of applied deep learning and artificial intelligence.

Prerequisites

  • Working knowledge of R and/or Python and familiarity with calculus and probability.

Recommended preparation:

About your instructor

  • Pablo Maldonado is an applied mathematician and data scientist with a taste for software development since his days of programming BASIC on a Tandy 1000. As an academic and business consultant, he spends a great deal of his time building applied artificial intelligence solutions for text analytics, sensor and transactional data, and reinforcement learning. Pablo earned his PhD in applied mathematics (with focus on mathematical game theory) at the University Pierre et Marie Curie in Paris, France.

    Pablo is the founder of Maldonado Consulting which is a technology-agnostic data analytics consultancy based in Prague, Czech Republic, that leverage the latest tools and research to develop custom solutions around like Data Analytics, Mathematical Modeling, and Machine Learning and Artificial Intelligence.

    Pablo has been an adjunct professor, teaching AI (Reinforcement Learning) and Machine Learning at Czech Technical University in Prague, the oldest technical university in Central Europe. He has co-authored a book “R Deep Learning Projects” published by Packt.

Schedule

The timeframes are only estimates and may vary according to how the class is progressing

Part 1: Introduction to Convergence of AI and deep learning (15 mins)

  • Deep learning is a subfield of machine learning that is behind the most notable achievements in human translation, computer vision (including autonomous driving) and other tasks.

Part 2: Understand AI and deep learning concepts (30 mins)

  • We demystify the building blocks of deep learning: Neural network, multi-layer perceptron, computation graphs, Tensorflow, Keras, PyTorch.

Part 3: Learn ANN and deep learning models (30 mins)

  • In this section, we cover briefly other neural network architectures that go beyond the classical feed-forward architectures introduced in the previous section.

Part 4: Advanced algorithms for smart applications (10 mins)

  • We will describe how to deploy advanced algorithms into production, and how to quickly develop prototypes using pre-trained models or third-party APIs.

Part 5: Deep Learning case studies and best practices (10 mins)

  • We present a collection of use cases and success stories in applied deep learning and artificial intelligence. Long the exclusive domain of technology giants, deep learning is permeating to the enterprise and might take the market by storm in many sectors.