Udemy - Fundamentals in Neural Networks

Fundamentals in Neural Networks



https://TutGee.com

MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English + srt | Duration: 37 lectures (6h 42m) | Size: 2.24 GB
Build up your intuition of the fundamental building blocks of Neural Networks
What you'll learn:
Understand the intuition behind Artificial Neural Networks
Understand the intuition behind Convolutional Neural Networks
Understand the intuition behind Recurrent Neural Networks
Apply Artificial Neural Networks in practice
Apply Convolutional Neural Networks in practice
Apply Recurrent Neural Networks in practice

Requirements
There is no prior coding or programming experience required. This course assumes you have your own laptop and the code will be done using Colab.

Description
Deep learning (also known as deep structured 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. Deep-learning architectures such as deep neural networks, deep belief networks, deep reinforcement learning, recurrent neural networks and convolutional neural networks have been applied to fields including computer vision, speech recognition, natural language processing, machine translation, bioinformatics, drug design, medical image analysis, material inspection and board game programs, where they have produced results comparable to and in some cases surpassing human expert performance.

This course covers the following three sections: (1) Neural Networks, (2) Convolutional Neural Networks, and (3) Recurrent Neural Networks. You will be receiving around 4 hours of materials on detailed discussion, mathematical description, and code walkthroughs of the three common families of neural networks. The descriptions of each section is summarized below.

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