Enrolled: 712 students
Duration: 3 days
Video: Remote / Physical Training
Level: All Level


Working hours

Monday 9:30 am - 6.00 pm
Tuesday 9:30 am - 6.00 pm
Wednesday 9:30 am - 6.00 pm
Thursday 9:30 am - 6.00 pm
Friday 9:30 am - 5.00 pm
Saturday Closed
Sunday Closed
HRD Corp Claimable Deep Learning Training

HRD Corp Claimable Deep Learning Training

Deep learning is a subfield of machine learning and artificial intelligence (AI) that attempts to model the method in which people learn specific facets of information. The field of data science, which also encompasses statistics and predictive modeling, contains an essential component known as deep learning. Deep learning makes the process of gathering, analyzing, and interpreting massive volumes of data both faster and easier, which is of tremendous use to data scientists who have been assigned with carrying out these activities. Deep learning can be viewed of, at its most fundamental level, as an approach to automating predictive analytics. Deep learning algorithms, on the other hand, are piled in a hierarchy of increasing complexity and abstraction, in contrast to the linear structure of typical machine learning algorithms.

  • Recognize the significance of deep learning and the process through which it operates.
  • Acquire a thorough understanding of the fundamental categories of neural networks.
  • Become familiar with deep learning libraries such as Keras and TensorFlow and learn how to use them.

It is strongly recommended that delegates have at least a fundamental understanding of the programming language Python, as well as linear algebra and probability.

Who should participate in this training for deep learning?
This course is designed for students who are interested in gaining an understanding of how deep learning functions and who may at some point in the future seek to develop their own applications utilizing deep learning techniques.

Deep Learning Course Overview
Building and training neural networks, which are composed of layers of decision-making nodes inspired by the human brain, is one use of Deep Learning.

It is a subset of the field of machine learning (ML) in which artificial neural networks learn from very massive volumes of data. It makes it possible for computers to tackle complex issues, even when using a wide variety of data sets that are interrelated and unstructured.

You will leave this Deep Learning Training course with a fundamental comprehension of the linear algebra, probabilities, and algorithms that are utilized in deep neural networks. Participants will obtain an in-depth understanding of the various types of neural networks, such as feedforward, convolutional, and recursive networks, after first becoming familiar with the distinction between deep learning and machine learning. At the conclusion of this course, delegates will have the ability to construct intricate models that assist machines in finding solutions to real-world issues.

Outline of the Deep Learning Course How Machine Learning Works

A Brief Look at Deep Learning

  • How important deep learning is
  • How Learning Deeply Works
  • Machine learning and deep learning are two different ways of learning.

Deep Neural Networks

  • Feedforward Networks
  • Convolved Networks
  • Networks That Loop Back on Themselves
  • Linear Algebra


  • Random Variables
  • Distributions of probabilities
  • Marginal Probability
  • Probability based on conditions
  • Rule for Conditional Probabilities in a Chain
  • Autoencoders for Bayes’ Rule

Graphs of computation

  • Methods of Monte Carlo
  • Deep Generative Model
  • Applications of Boltzmann Machines
  • Libraries and Building Blocks


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