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Neural Network Courses

424 Courses

California Institute of Technology Treehouse

(UPI) Chapter 19: AI in Cybersecurity Course (How To)

Chapter 19 explores how AI, particularly deep learning models like RNNs and CNNs, enhances malware detection by analyzing static and dynamic features, addressing the growing complexity of cyber threats with automated precision.
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California Institute of Technology Coursera

AI ML with Deep Learning and Supervised Models

This comprehensive AI ML with Deep Learning and Supervised Models specialization equips you with the skills to excel in roles across AI, machine learning, and deep learning. Through in-depth modules, you'll master regression, classification, clustering, neural networks, and advanced AI frameworks to solve real-world challenges. By the end of this c.
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California Institute of Technology AWS Skill Builder

Fundamentals of Machine Learning and Artificial Intelligence (Português)

Depois de 28 de março, os títulos dos cursos estarão somente em inglês. No entanto, as descrições dos cursos permanecerão disponíveis no idioma de sua preferência para permitir que você pesquise nesse idioma.
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California Institute of Technology Udemy

AI & Cognitive Science: Bridging Minds and Machines

Explore the Intersection of Artificial Intelligence and Human Cognition to Unlock New Frontiers in Technology
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California Institute of Technology Udemy

Theoretical Foundations of AI in Cybersecurity

Unlock the Power of AI: Strengthen Cybersecurity with Theoretical Insights and Advanced Techniques
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California Institute of Technology Udemy

Master Generative AI for RPA from basics to Advanced

Learn Efficient Automation and Optimization with Generative AI for RPA and Learn organizational workflows and Automation
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California Institute of Technology Udemy

Custom ChatGPT Publishing & AI Bootcamp Masterclass

Learn Python, AI basics, ChatGPT customization, and build 150+ hands-on projects with zero prior experience.
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California Institute of Technology Northeastern University

Generative AI: Foundations and Concepts

This course provides an overview of some different concepts underpinning Generative AI, their mathematical principles, and their applications in engineering. The focus will be on the practical implementation of generative AI including, neural networks, attention mechanism, and advanced deep learning models.
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California Institute of Technology Coursera

Introduction to AI

Discover the fundamental concepts behind artificial intelligence (AI) and machine learning in this introductory course. Explore the various types of AI, examine ethical considerations, and delve into the key machine learning models that power modern AI systems. Whether your goal is to work directly with AI, strengthen your software development skil.
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California Institute of Technology LinkedIn Learning

Künstliche Intelligenz (KI) – Grundlagen

Vertiefen Sie Ihr Wissen über Künstliche Intelligenz (KI) und Machine Learning (ML) durch einen detaillierten Überblick über deren Geschichte, Techniken und Einsatzbereiche. Dieser Kurs von LinkedIn Learning bietet umfassende Einblicke und Anleitungen für alle Interessierten im Bereich der entwickelnden Technologie, einschließlich Computer Vis.
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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:

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:

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!