Who is a neural network developer?
A neural network developer designs and programs hardware and software systems that operate on the principle of the human brain (neural networks).
Introduction to Neural Network Courses
A neural network developer is a programmer who creates software for mathematical models that work on the principle of the nervous system of a living organism.
A neural network is a computer programme built on the model of the structure and functioning of the human brain. Its constituent artificial neurons are tiny mathematical functions that perform computational actions - receive information, process and compare it, and pass it on. A neural network is not programmed in the usual sense of the word once and for all - it learns by loading and constantly processing huge data sets. For this purpose, special algorithms are used, which are created by the neural network developer. As a result, an artificial neural network can compare data, find patterns and on their basis make its own conclusions, classify information, predict events, recognise images, speech.
The task of a neural network developer is to create a programme capable of learning and teach it to learn. Examples of the results of neural network developers' work after neural network courseі include chatbots, voice assistants, text generators, mobile applications capable of recognising faces in photos or emotions in videos, navigation systems for unmanned cars, systems for detecting faults during maintenance, etc.
Career Paths and Learning Paths
Since, by and large, the creation of neural networks is one of the narrow specialisations of a Data Science specialist, the core knowledge of a neural network developer is Big Data science (data modelling, quality assessment of algorithms and prediction models). Also included in the knowledge pool are:
Neural Network Architecture.
Python programming (neural networks are also written in other languages - R, Java, C# (Sharp), C++, Go, Swift, but in Python most often).
PyTorch and TensorFlow machine learning frameworks.
Python libraries for Data Science - Numpy, Matplotlib, Scikit-learn.
Working with databases and SQL.
Working in Linux.
User interface technologies.
A lot of lectures on neural networks can be found on YouTube. Often after the video, machine learning enthusiasts do a detailed breakdown of the material. There are tutorial applications on the Internet (like artificial neural network course) with ready-made architectures that clearly demonstrate what is happening inside a neural network and give instructions on how to build it into a specific project.
Course Benefits and Features
Let's take a look at the main pluses of the neural networks and deep learning courseі:
Short and to the point: we give information in a concise manner, imagine, you can learn something new during a metro journey!
Detailed instructions: each lesson has step-by-step instructions with screenshots, lists and checklists to help you master the material.
Professional teachers: our teachers are practitioners who work every day with the constructors and tools they talk about in the lessons, so you will always have the opportunity to ask an expert a question in special chats or webinars if something is unclear.
These are just some of our advantages.
Enrollment Process
You can email us and sign up for our online lessons now. You can take a deep neural network course on one of the educational platforms. These courses are designed for people with no particular background, so they are suitable for most people. Online training is usually focused on practice - this allows you to quickly build up your portfolio and get a job immediately after training!
Udemy
Paid Course
1 day 17 hours 17 minutes
On-Demand
