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Pratheerth Padman (et al.) | Duration: 6h 37m | Video: H264 1280x720 | Audio: AAC 48 kHz 2ch | 805 MB | Language: English
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 popular algorithms. 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. This path will contain workable solutions.
Courses in this path
1. Literacy Essentials: Core Concepts Deep Learning (Pratheerth Padman, 2021)
2. Literacy Essentials: Core Concepts Neural Network (Abdul Rehman Yousaf, 2021)
3. Literacy Essentials: Core Concepts Convolutional Neural Network (Alex Schultz, 2021)
4. Literacy Essentials: Core Concepts Recurrent Neural Networks (Abdul Rehman Yousaf, 2022)
5. Literacy Essentials: Core Concepts Generative Adversarial Network (Jerry Kurata, 2022)
6. Literacy Essentials: Core Concepts Recommender Systems (Biswanath Halder, 2022)
7. Literacy Essentials: Core Concepts Data Normalization (Ifedayo Bamikole, 2022)
Homepage
https://www.pluralsight.com/paths/deep-learning-literacy
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