The MOST in-depth look at neural network theory for machine learning, with both pure Python and Tensorflow code

RS 699

What Will I Learn?

- Learn how Deep Learning REALLY works (not just some diagrams and magical black box code)
- Code a neural network from scratch in Python and numpy
- Describe different types of neural networks and the different types of problems they are used for
- Create a neural network with an output that has K > 2 classes using softmax
- Install TensorFlow
- Learn how a neural network is built from basic building blocks (the neuron)
- Code a neural network using Google's TensorFlow
- Derive the backpropagation rule from first principles
- Describe the various terms related to neural networks, such as
- Understand important foundations for OpenAI ChatGPT, GPT-4, DALL-E, Midjourney, and Stable Diffusion

Curriculum For This Course

4 Sections
9 Lessons
02:35:32 Hours

Course Content

1 Lessons
00:05:52 Hours

- Introduction & Outline 00:05:52

Preliminaries : From Neurons to Neural Networks

0 Lessons
00:00:00 Hours

Training a Neural Network

5 Lessons
00:51:29 Hours

- Training: Section Introduction and Outline 00:12:29
- What do all these symbols and letters mean 00:18:54
- What does it mean to "train" a neural network? 00:03:47
- How to Brace Yourself to Learn Backpropagation 00:06:48
- Categorical Cross-Entropy Loss Function 00:09:31

Practical Machine Learning

3 Lessons
01:38:11 Hours

- Practical Issues: Section Introduction and Outline 00:07:52
- donut and xor review 01:17:10
- Donut and XOR Revisite 00:13:09

Requirements

- Basic math (calculus derivatives, matrix arithmetic, probability)
- Install Numpy and Python
- Don't worry about installing TensorFlow, we will do that in the lectures.
- Being familiar with the content of my logistic regression course (cross-entropy cost, gradient descent, neurons, XOR, donut) will give you the proper context for this course

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Description

Ever wondered how AI technologies like **OpenAI** **ChatGPT**,** GPT-4**, **DALL-E**, **Midjourney**, and **Stable Diffusion** really work? In this course, you will learn the foundations of these groundbreaking applications.

This course will get you started in building your FIRST **artificial neural network** using **deep learning** techniques. Following my previous course on logistic regression, we take this basic building block, and build full-on non-linear neural networks right out of the gate using Python and Numpy. All the materials for this course are FREE.

We extend the previous binary classification model to multiple classes using the softmax function, and we derive the very important training method called "**backpropagation**" using first principles. I show you how to code backpropagation in Numpy, first "the slow way", and then "the fast way" using Numpy features.

Next, we implement a neural network using Google's new TensorFlow library.

You should take this course if you are interested in starting your journey toward becoming a master at deep learning, or if you are interested in **machine learning** and **data science** in general. We go beyond basic models like logistic regression and linear regression and I show you something that **automatically learns features**.

This course provides you with many practical examples so that you can really see how deep learning can be used on anything. Throughout the course, we'll do a course project, which will show you how to predict user actions on a website given user data like whether or not that user is on a mobile device, the number of products they viewed, how long they stayed on your site, whether or not they are a returning visitor, and what time of day they visited.

Another project at the end of the course shows you how you can use deep learning for facial expression recognition. Imagine being able to predict someone's emotions just based on a picture!

After getting your feet wet with the fundamentals, I provide a brief overview of some of the newest developments in neural networks - slightly modified architectures and what they are used for.

NOTE:

If you *already* know about softmax and backpropagation, and you want to skip over the theory and speed things up using more advanced techniques along with GPU-optimization, check out my follow-up course on this topic, **Data Science: Practical Deep Learning Concepts in Theano and TensorFlow**.

I have other courses that cover more advanced topics, such as **Convolutional Neural Networks**, **Restricted Boltzmann Machines**, **Autoencoders**, and more! But you want to be very comfortable with the material in this course before moving on to more advanced subjects.

This course focuses on "**how to build and understand**", not just "how to use". Anyone can learn to use an API in 15 minutes after reading some documentation. It's not about "remembering facts", it's about **"seeing for yourself" via experimentation**. It will teach you how to visualize what's happening in the model internally. If you want **more** than just a superficial look at machine learning models, this course is for you.

"If you can't implement it, you don't understand it"

Or as the great physicist Richard Feynman said: "What I cannot create, I do not understand".

My courses are the ONLY courses where you will learn how to implement machine learning algorithms from scratch

Other courses will teach you how to plug in your data into a library, but do you really need help with 3 lines of code?

After doing the same thing with 10 datasets, you realize you didn't learn 10 things. You learned 1 thing, and just repeated the same 3 lines of code 10 times...

Suggested Prerequisites:

calculus (taking derivatives)

matrix arithmetic

probability

Python coding: if/else, loops, lists, dicts, sets

Numpy coding: matrix and vector operations, loading a CSV file

Be familiar with basic linear models such as linear regression and logistic regression

WHAT ORDER SHOULD I TAKE YOUR COURSES IN?:

Check out the lecture "Machine Learning and AI Prerequisite Roadmap" (available in the FAQ of any of my courses, including the free Numpy course)

**1**Reviews**51**Courses

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Hello, my name is Victor Kercado. I'm a successful podcaster and life coach in the area of personal growth. I've been fortunate to help many people improve their lives thru mindfulness, communication, and spirituality. I look forward to sharing my knowledge with you thru courses that will inspire and motivate you to improve in all areas of your life. Thank you for listening!

RS 699

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