Data Science: Deep Learning and Neural Networks in Python

Data Science: Deep Learning and Neural Networks in Python, available at $79.99, has an average rating of 4.7, with 94 lectures, 4 quizzes, based on 9860 reviews, and has 57943 subscribers.

You will learn about Learn how Deep Learning REALLY works (not just some diagrams and magical black box code) Learn how a neural network is built from basic building blocks (the neuron) Code a neural network from scratch in Python and numpy Code a neural network using Google's TensorFlow Describe different types of neural networks and the different types of problems they are used for Derive the backpropagation rule from first principles Create a neural network with an output that has K > 2 classes using softmax Describe the various terms related to neural networks, such as "activation", "backpropagation" and "feedforward" Install TensorFlow Understand important foundations for OpenAI ChatGPT, GPT-4, DALL-E, Midjourney, and Stable Diffusion This course is ideal for individuals who are Students interested in machine learning – you'll get all the tidbits you need to do well in a neural networks course or Professionals who want to use neural networks in their machine learning and data science pipeline. Be able to apply more powerful models, and know its drawbacks. It is particularly useful for Students interested in machine learning – you'll get all the tidbits you need to do well in a neural networks course or Professionals who want to use neural networks in their machine learning and data science pipeline. Be able to apply more powerful models, and know its drawbacks.

Enroll now: Data Science: Deep Learning and Neural Networks in Python

Summary

Title: Data Science: Deep Learning and Neural Networks in Python

Price: $79.99

Average Rating: 4.7

Number of Lectures: 94

Number of Quizzes: 4

Number of Published Lectures: 90

Number of Curriculum Items: 98

Number of Published Curriculum Objects: 90

Original Price: $199.99

Quality Status: approved

Status: Live

What You Will Learn

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

Who Should Attend

Students interested in machine learning – you'll get all the tidbits you need to do well in a neural networks course
Professionals who want to use neural networks in their machine learning and data science pipeline. Be able to apply more powerful models, and know its drawbacks.

Target Audiences

Students interested in machine learning – you'll get all the tidbits you need to do well in a neural networks course
Professionals who want to use neural networks in their machine learning and data science pipeline. Be able to apply more powerful models, and know its drawbacks.

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)

Course Curriculum

Chapter 1: Welcome

Lecture 1: Introduction and Outline

Lecture 2: Where to get the code

Lecture 3: How to Succeed in this Course

Chapter 2: Review

Lecture 1: Review Section Introduction

Lecture 2: What does machine learning do?

Lecture 3: Neuron Predictions

Lecture 4: Neuron Training

Lecture 5: Deep Learning Readiness Test

Lecture 6: Review Section Summary

Chapter 3: Preliminaries: From Neurons to Neural Networks

Lecture 1: Neural Networks with No Math

Lecture 2: Introduction to the E-Commerce Course Project

Chapter 4: Classifying more than 2 things at a time

Lecture 1: Prediction: Section Introduction and Outline

Lecture 2: From Logistic Regression to Neural Networks

Lecture 3: Interpreting the Weights of a Neural Network

Lecture 4: Softmax

Lecture 5: Sigmoid vs. Softmax

Lecture 6: Feedforward in Slow-Mo (part 1)

Lecture 7: Feedforward in Slow-Mo (part 2)

Lecture 8: Where to get the code for this course

Lecture 9: Softmax in Code

Lecture 10: Building an entire feedforward neural network in Python

Lecture 11: E-Commerce Course Project: Pre-Processing the Data

Lecture 12: E-Commerce Course Project: Making Predictions

Lecture 13: Prediction Quizzes

Lecture 14: Prediction: Section Summary

Lecture 15: Suggestion Box

Chapter 5: Training a neural network

Lecture 1: Training: Section Introduction and Outline

Lecture 2: What do all these symbols and letters mean?

Lecture 3: What does it mean to "train" a neural network?

Lecture 4: How to Brace Yourself to Learn Backpropagation

Lecture 5: Categorical Cross-Entropy Loss Function

Lecture 6: Training Logistic Regression with Softmax (part 1)

Lecture 7: Training Logistic Regression with Softmax (part 2)

Lecture 8: Backpropagation (part 1)

Lecture 9: Backpropagation (part 2)

Lecture 10: Backpropagation in code

Lecture 11: Backpropagation (part 3)

Lecture 12: The WRONG Way to Learn Backpropagation

Lecture 13: E-Commerce Course Project: Training Logistic Regression with Softmax

Lecture 14: E-Commerce Course Project: Training a Neural Network

Lecture 15: Training Quiz

Lecture 16: Training: Section Summary

Chapter 6: Practical Machine Learning

Lecture 1: Practical Issues: Section Introduction and Outline

Lecture 2: Donut and XOR Review

Lecture 3: Donut and XOR Revisited

Lecture 4: Neural Networks for Regression

Lecture 5: Common nonlinearities and their derivatives

Lecture 6: Practical Considerations for Choosing Activation Functions

Lecture 7: Hyperparameters and Cross-Validation

Lecture 8: Manually Choosing Learning Rate and Regularization Penalty

Lecture 9: Why Divide by Square Root of D?

Lecture 10: Practical Issues: Section Summary

Chapter 7: TensorFlow, exercises, practice, and what to learn next

Lecture 1: TensorFlow plug-and-play example

Lecture 2: Visualizing what a neural network has learned using TensorFlow Playground

Lecture 3: Where to go from here

Lecture 4: You know more than you think you know

Lecture 5: How to get good at deep learning + exercises

Lecture 6: Deep neural networks in just 3 lines of code with Sci-Kit Learn

Chapter 8: Project: Facial Expression Recognition

Lecture 1: Facial Expression Recognition Project Introduction

Lecture 2: Facial Expression Recognition Problem Description

Lecture 3: The class imbalance problem

Lecture 4: Utilities walkthrough

Lecture 5: Facial Expression Recognition in Code (Binary / Sigmoid)

Lecture 6: Facial Expression Recognition in Code (Logistic Regression Softmax)

Lecture 7: Facial Expression Recognition in Code (ANN Softmax)

Lecture 8: Facial Expression Recognition Project Summary

Chapter 9: Backpropagation Supplementary Lectures

Lecture 1: Backpropagation Supplementary Lectures Introduction

Lecture 2: Why Learn the Ins and Outs of Backpropagation?

Lecture 3: Gradient Descent Tutorial

Lecture 4: Help with Softmax Derivative

Lecture 5: Backpropagation with Softmax Troubleshooting

Chapter 10: Higher-Level Discussion

Lecture 1: What's the difference between "neural networks" and "deep learning"?

Lecture 2: Who should take this course in 2020 and beyond?

Lecture 3: Who should learn backpropagation in 2020 and beyond?

Lecture 4: Where does this course fit into your deep learning studies?

Chapter 11: Setting Up Your Environment (FAQ by Student Request)

Lecture 1: Pre-Installation Check

Lecture 2: Anaconda Environment Setup

Lecture 3: How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow

Chapter 12: Extra Help With Python Coding for Beginners (FAQ by Student Request)

Lecture 1: How to Uncompress a .tar.gz file

Lecture 2: How to Code by Yourself (part 1)

Lecture 3: How to Code by Yourself (part 2)

Lecture 4: Proof that using Jupyter Notebook is the same as not using it

Lecture 5: Python 2 vs Python 3

Chapter 13: Effective Learning Strategies for Machine Learning (FAQ by Student Request)

Lecture 1: How to Succeed in this Course (Long Version)

Lecture 2: Is this for Beginners or Experts? Academic or Practical? Fast or slow-paced?

Lecture 3: Where does this course fit into your deep learning studies? (Old Version)

Lecture 4: Machine Learning and AI Prerequisite Roadmap (pt 1)

Instructors

Lazy Programmer Inc.
Artificial intelligence and machine learning engineer

Rating Distribution

1 stars: 101 votes
2 stars: 108 votes
3 stars: 479 votes
4 stars: 3174 votes
5 stars: 5999 votes

Frequently Asked Questions

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You can view and review the lecture materials indefinitely, like an on-demand channel.

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