CTHR - Neural Networks With Deep Learning Training

Neural Networks With Deep Learning Training - Hong Kong

DATE & TIME (Expired)

29 Oct 2021 (Fri)
9:00 AM - 5:00 PM

19 Nov 2021 (Fri)
9:00 AM - 5:00 PM

31 Dec 2021 (Fri)
9:00 AM - 5:00 PM

PRICE

HKD 12,095

ORGANISER

The Knowledge Academy

LOCATION

DESCRIPTION

Prerequisites

There are no prerequisites for this course. However, some experience with the Python programming language would be beneficial.

Audience

Anyone who wants to learn about neural networks and deep learning can attend this course.

Neural Networks with Deep Learning Training Course Overview

Neural Networks are a set of algorithms designed to identify patterns. These are developed to imitate the human brain. Neural networks translate sensory data through labelling or clustering raw input and machine perception. These networks identify numerical patterns that are stored in vectors. All the real-world data, including text, images, or sound, must be translated into these numerical patterns. Neural networks can be thought of as a clustering and classification layer on top of the data stored and managed.

The Knowledge Academy’s Neural Networks with Deep Learning Training course will provide delegates with an understanding of deep learning and neural networks. Delegates will be familiarised with basic concepts of neural networks such as binary classification, logistic regression, derivatives, and vectorisation.

During this 1-day training course, delegates will be introduced to Python and Jupyter/IPython notebooks. Delegates will learn about shallow neural networks, including vectorised implementation, activation functions, and backpropagation intuition. In addition, delegates will also gain knowledge on the concepts of deep neural networks involving deep L-layer neural network, deep representations, and forward and backward propagation.

Delegate pack consisting of course notes and exercises
Manual
Experienced Instructor
Refreshments

Neural Networks with Deep Learning Training Course Outline

Introduction to Deep Learning

Introduction to Neural Networks
Supervised Learning with Neural Networks

Neural Networks Fundamentals

Binary Classification
Logistic Regression
Gradient Descent and Derivatives
Computational Graph
Vectorisation
Introduction to Python
Jupyter/IPython Notebooks

Shallow Neural Networks

Representation of a Neural Network
Computing the Output of a Neural Network
Vectorised Implementation
Activation Functions
Derivatives of Activation Functions
Gradient Descent for Neural Networks
Backpropagation Intuition

Deep Neural Networks

Deep L-layer Neural Network
Forward Propagation
Deep Representations
Building Blocks
Backward Propagation
Difference Between Parameters and Hyperparameters

For more details and registration, please click here.