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Neural Network Courses

212 Courses

California Institute of Technology

PyTorch for Deep Learning

PyTorch for Deep Learning Learn PyTorch and become a proficient Deep Learning Engineer. This PyTorch course is a step-by-step guide designed to help you develop your own deep learning models. The curriculum includes essential topics such as Computer Vision, Neural Networks, and much more.
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California Institute of Technology AWS Skill Builder

AWS SimuLearn: TensorFlow and Computer Vision

AWS SimuLearn: TensorFlow and Computer Vision AWS SimuLearn is an online learning experience that pairs generative AI-powered simulations with hands-on practice to help individuals learn how to translate business problems into technical solutions through the simulation of dialog between a customer and a technology professional. AWS SimuLearn: Te.
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California Institute of Technology LinkedIn Learning

Generative AI: Introduction to Large Language Models

Generative AI: Introduction to Large Language Models | LinkedIn Learning Course Title: Generative AI: Introduction to Large Language Models Description: Gain a foundational knowledge of how large language models and other Generative AI models work. University: Provided by LinkedIn Learning Categories: Artificial Intelligence Cour.
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California Institute of Technology LinkedIn Learning

Introduction to Generative Adversarial Networks (GANs)

Introduction to Generative Adversarial Networks (GANs) Gain a better understanding of Generative Adversarial Networks (GANs). Learn how GANs are created, trained, and their capability to generate new media. This course is offered by LinkedIn Learning through the university platform. Categories: Arti.
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California Institute of Technology AWS Skill Builder

AWS ML Visão geral do curso de engenheiro associado (Português) | AWS ML Engineer Associate Curriculum Overview (Portuguese)

Neste curso introdutório à grade curricular de engenheiros de ML associados da AWS, você analisa os conceitos básicos de machine learning (ML) e examina a evolução do machine learning e da IA. Você explora as primeiras etapas do ciclo de vida do ML, identificando uma meta de negócios e formulando um problema de ML com base nessa meta de negócios. F.
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California Institute of Technology AWS Skill Builder

AWS ML Engineer Associate Curriculum Overview (Japanese)

AWS ML Engineer Associate Curriculum のこの入門コースでは、機械学習 (ML) の基礎を復習し、ML と AI の進化について確認します。ML ライフサイクルの最初のステップとして、ビジネス目標を特定し、そのビジネス目標に基づいて ML の問題を定式化します。最後に、ML モデルの構築、トレーニング、デプロイに使用できるフルマネージド型 AWS サービスである Amazon SageMak.
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California Institute of Technology edX

IA para todos: domina los conceptos básicos

En este MOOC, aprenderás qué es la IA y comprenderás sus aplicaciones y casos de uso y cómo está transformando nuestras vidas. Explorarás los conceptos básicos de la IA, como el aprendizaje automático, el aprendizaje profundo y las redes neuronales, así como los casos de uso y las aplicaciones de la IA. Estarás expuesto a las preocupaciones que r.
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California Institute of Technology Coursera

Introduction to On-Device AI

Introduction to On-Device AI As AI moves beyond the cloud, on-device inference is rapidly expanding to smartphones, IoT devices, robots, AR/VR headsets, and more. Billions of mobile and other edge devices are ready to run optimized AI models. This course equips you with key skills to deploy AI on device: Explore how deploying models on devic.
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California Institute of Technology Pluralsight

Object Detection Recognition and Tracking

Object Detection Recognition and Tracking Computer vision applications can automate and enhance the analysis and interpretation of visual data beyond human capabilities. This course will teach you how image classifiers can perform object detection, recognition, and tracking using Tensorflow. In this course, Object Detection Recognition and Tracki.
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California Institute of Technology Pluralsight

Troubleshooting and Improving Neural Network Performance

Troubleshooting and Improving Neural Network Performance This course will teach you neural network troubleshooting and performance tuning from a data scientist's perspective. Understand various troubleshooting techniques for neural networks and how to improve neural network performance effectively. In this course, Troubleshooting and Improving.
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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!