Deep Learning Literacy — Practical Application | Path
Год выпуска: 2022
Производитель: Pluralsight
Сайт производителя://app.pluralsight.com/paths/skill/deep-learning-literacy-practical-application
Автор: Коллектив авторов
Продолжительность: 9h 8m
Тип раздаваемого материала: Видеоурок
Язык: Английский
Описание:
Deep learning is part of a broader family of machine learning methods based on artificial neural networks with representation learning. Learning can be supervised, semi-supervised or unsupervised.
This path is focused on Deep Learning in action. We have pulled a series of examples to demonstrate how deep learning is embedded in our day to day lives. These are just in time sort of courses that reflect the journey from problem to solution.
The path is curated for Data enthusiasts that are eager to learn about Deep learning and foray into Data centered roles like Data scientist. Though this path will contain workable solutions, there is no requirement for the learner to have any background into Machine Learning or Deep learning. Intention is to have sandboxes for the path.
Prerequisites:
Understanding of algorithms used in the path. Though it is desired but not mandatory Understanding of Deep Learning Key concepts
Related Topics:
Deep Learning Literacy | Path
Machine Learning Literacy — Practical Application | Path
Machine Learning Literacy | Path
Feature Engineering | Path
Data Analytics Literacy ► Data Science Literacy | Path
Python for Data Analysts | Path
Содержание
Beginner
This path is designed to explore the application of Deep Learning in our day to day lives. This section focuses on a few industry examples of Deep Learning Practical applications.
Deep Learning Application for Healthcare (Colin Matthews, 2022)
Deep Learning Application for Marketing (Netta Tzin, 2022)
Deep Learning Application for Finance (Jaimin M, 2022)
Deep Learning Application for Retail (Trent McMillan, 2022)
Intermediate
This section of the path focuses on RNN, CNN and GAN’s implementation and exploring how deep learning solves problems like automating image captioning and sentiment classification.
Implement Image Recognition with a Convolutional Neural Network (Pratheerth Padman, 2022)
Implement Text Auto Completion with LSTM (Biswanath Halder, 2022)
Implement Image Captioning with Recurrent Neural Networks (Abdul Rehman Yousaf, 2022)
Advanced
This section explores practical examples of word embedding, Sentence classification, Named Entity Recognition and Recommendation Engines.
Implement Natural Language Processing for Word Embedding (Axel Sirota, 2022)
Build a Rating Recommendation Engine with Collaborative Filtering (Pratheerth Padman, 2022)
Build an Object Detection Model with Python (Gabrielle Davelaar, 2022)
Build a Model for Anomaly Detection in Time Series Data (Pratheerth Padman, 2022)
Файлы примеров: присутствуют
Субтитры: присутствуют
Формат видео: MP4
Видео: H.264/AVC, 1280x720, 16:9, 30fps, 80.7 kb/s
Аудио: AAC, 48000Hz, 96 kbit/s, 2 channels
