Basics and Foundation of Artificial Neural Networks
- Free tutorial
- Rating: 4.0 out of 54.0 (7 ratings)
- 924 students
- 1hr 32min of on-demand video
- Created by Yiqiao Yin
- English
What you’ll learn
- Learn about the basics of neural network models without any prior knowledge
- Learn to use python to design a neural network model without any prior knowledge
- Learn from top tier Data Scientists to build neural network models for production
- Learn to develop your own customized neural network models
Requirements
- No prior programming experience needed. You will learn directly in this class.
Description
This course is created to follow up with the AI4ALL initiatives. The course presents coding materials at a pre-college level and introduces a fundamental pipeline for a neural network model. The course is designed for the first-time learners and the audience who only want to get a taste of a machine learning project but still uncertain whether this is the career path. We will not bored you with the unnecessary component and we will directly take you through a list of topics that are fundamental for industry practitioners and researchers to design their customized neural network model. The course focuses on the Artificial Neural Network models and introduce the important building block using Tensorflow.
This instructor team is lead by Ivy League graduate students and we have had 3+ years coaching high school students. We have seen all the ups and downs. Moreover, we want to share these roadblocks with you. This course is designed for beginner students at pre-college level who just want to have a quick taste of what AI is about and efficiently build a quick Github package to showcase some technical skills. We have other longer courses for more advanced students. However, we welcome anybody to take this course!
Who this course is for:
- Pre-college level students interested in neural network models
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Course content
1 section • 13 lectures • 1h 32m total length
Artificial Neural Networks13 lectures • 1hr 32min
- Load and Save Data06:58
- One Hot Encode05:13
- Q11 question
- Rescale Data02:42
- Activation and Softmax Function08:08
- Q21 question
- Regularization04:13
- Build a Neural Network Model04:41
- Loss Function06:36
- Optimization05:09
- Compiler05:24
- Fit and Train06:48
- Q32 questions
- Evaluation and Accuracy05:00
- Q41 question
- Save and Load Model02:55
- Create Installable Git Package28:32
