Udemy
  •  
  •  
  •  
  •  
  •  
  •  
  •  
  •  
  •  
  •  
  •  
  •  
  •  
Development
Web Development Data Science Mobile Development Programming Languages Game Development Database Design & Development Software Testing Software Engineering Development Tools No-Code Development
Business
Entrepreneurship Communications Management Sales Business Strategy Operations Project Management Business Law Business Analytics & Intelligence Human Resources Industry E-Commerce Media Real Estate Other Business
Finance & Accounting
Accounting & Bookkeeping Compliance Cryptocurrency & Blockchain Economics Finance Finance Cert & Exam Prep Financial Modeling & Analysis Investing & Trading Money Management Tools Taxes Other Finance & Accounting
IT & Software
IT Certification Network & Security Hardware Operating Systems Other IT & Software
Office Productivity
Microsoft Apple Google SAP Oracle Other Office Productivity
Personal Development
Personal Transformation Personal Productivity Leadership Career Development Parenting & Relationships Happiness Esoteric Practices Religion & Spirituality Personal Brand Building Creativity Influence Self Esteem & Confidence Stress Management Memory & Study Skills Motivation Other Personal Development
Design
Web Design Graphic Design & Illustration Design Tools User Experience Design Game Design Design Thinking 3D & Animation Fashion Design Architectural Design Interior Design Other Design
Marketing
Digital Marketing Search Engine Optimization Social Media Marketing Branding Marketing Fundamentals Marketing Analytics & Automation Public Relations Advertising Video & Mobile Marketing Content Marketing Growth Hacking Affiliate Marketing Product Marketing Other Marketing
Lifestyle
Arts & Crafts Beauty & Makeup Esoteric Practices Food & Beverage Gaming Home Improvement Pet Care & Training Travel Other Lifestyle
Photography & Video
Digital Photography Photography Portrait Photography Photography Tools Commercial Photography Video Design Other Photography & Video
Health & Fitness
Fitness General Health Sports Nutrition Yoga Mental Health Dieting Self Defense Safety & First Aid Dance Meditation Other Health & Fitness
Music
Instruments Music Production Music Fundamentals Vocal Music Techniques Music Software Other Music
Teaching & Academics
Engineering Humanities Math Science Online Education Social Science Language Teacher Training Test Prep Other Teaching & Academics
AWS Certification Microsoft Certification AWS Certified Solutions Architect - Associate AWS Certified Cloud Practitioner CompTIA A+ Cisco CCNA Amazon AWS CompTIA Security+ AWS Certified Developer - Associate
Photoshop Graphic Design Adobe Illustrator Drawing Digital Painting InDesign Character Design Canva Figure Drawing
Life Coach Training Neuro-Linguistic Programming Mindfulness Personal Development Meditation Personal Transformation Life Purpose Neuroscience Emotional Intelligence
Web Development JavaScript React CSS Angular PHP WordPress Node.Js Python
Google Flutter Android Development iOS Development Swift React Native Dart Programming Language Mobile Development Kotlin SwiftUI
Digital Marketing Google Ads (Adwords) Social Media Marketing Google Ads (AdWords) Certification Marketing Strategy Internet Marketing YouTube Marketing Email Marketing Retargeting
SQL Microsoft Power BI Tableau Business Analysis Business Intelligence MySQL Data Analysis Data Modeling Data Science
Business Fundamentals Entrepreneurship Fundamentals Business Strategy Online Business Business Plan Startup Freelancing Blogging Home Business
Unity Game Development Fundamentals Unreal Engine C# 3D Game Development C++ 2D Game Development Unreal Engine Blueprints Blender
30-Day Money-Back Guarantee
Development Data Science Python

Data Science: Deep Learning in Python

The MOST in-depth look at neural network theory, and how to code one with pure Python and Tensorflow
Rating: 4.6 out of 54.6 (7,202 ratings)
45,651 students
Created by Lazy Programmer Inc.
Last updated 1/2021
English
English [Auto], Portuguese [Auto], 
30-Day Money-Back Guarantee

What you'll 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

Course content

14 sections • 89 lectures • 11h 13m total length

  • Preview04:29
  • Preview08:48
  • Where to get the code
    05:01
  • Anyone Can Succeed in this Course
    12:42

  • Review Section Introduction
    01:58
  • What does machine learning do?
    05:28
  • Neuron Predictions
    05:00
  • Neuron Training
    08:47
  • Deep Learning Readiness Test
    05:33
  • Review Section Summary
    03:52

  • Neural Networks with No Math
    04:20
  • Introduction to the E-Commerce Course Project
    08:52

  • Prediction: Section Introduction and Outline
    05:39
  • From Logistic Regression to Neural Networks
    05:12
  • Interpreting the Weights of a Neural Network
    08:05
  • Softmax
    02:54
  • Sigmoid vs. Softmax
    01:30
  • Feedforward in Slow-Mo (part 1)
    19:42
  • Feedforward in Slow-Mo (part 2)
    10:55
  • Where to get the code for this course
    01:30
  • Softmax in Code
    03:39
  • Building an entire feedforward neural network in Python
    06:23
  • E-Commerce Course Project: Pre-Processing the Data
    05:24
  • E-Commerce Course Project: Making Predictions
    03:55
  • Prediction Quizzes
    03:25
  • Prediction: Section Summary
    01:45
  • Suggestion Box
    03:03

  • Training: Section Introduction and Outline
    02:49
  • What do all these symbols and letters mean?
    09:45
  • What does it mean to "train" a neural network?
    06:45
  • How to Brace Yourself to Learn Backpropagation
    07:38
  • Categorical Cross-Entropy Loss Function
    11:01
  • Training Logistic Regression with Softmax (part 1)
    14:41
  • Training Logistic Regression with Softmax (part 2)
    05:41
  • Backpropagation (part 1)
    05:13
  • Backpropagation (part 2)
    10:50
  • Backpropagation in code
    17:07
  • Backpropagation (part 3)
    16:12
  • The WRONG Way to Learn Backpropagation
    03:52
  • E-Commerce Course Project: Training Logistic Regression with Softmax
    08:11
  • E-Commerce Course Project: Training a Neural Network
    06:19
  • Training Quiz
    05:30
  • Training: Section Summary
    02:41

  • Practical Issues: Section Introduction and Outline
    01:43
  • Donut and XOR Review
    01:06
  • Donut and XOR Revisited
    04:21
  • Neural Networks for Regression
    11:38
  • Common nonlinearities and their derivatives
    01:26
  • Practical Considerations for Choosing Activation Functions
    07:45
  • Hyperparameters and Cross-Validation
    04:10
  • Manually Choosing Learning Rate and Regularization Penalty
    04:08
  • Why Divide by Square Root of D?
    06:32
  • Practical Issues: Section Summary
    06:10

  • TensorFlow plug-and-play example
    19:18
  • Visualizing what a neural network has learned using TensorFlow Playground
    11:35
  • Where to go from here
    03:41
  • You know more than you think you know
    04:52
  • How to get good at deep learning + exercises
    05:07
  • Deep neural networks in just 3 lines of code with Sci-Kit Learn
    08:49

  • Facial Expression Recognition Project Introduction
    04:51
  • Facial Expression Recognition Problem Description
    12:21
  • The class imbalance problem
    06:01
  • Utilities walkthrough
    05:45
  • Facial Expression Recognition in Code (Binary / Sigmoid)
    12:13
  • Facial Expression Recognition in Code (Logistic Regression Softmax)
    08:57
  • Facial Expression Recognition in Code (ANN Softmax)
    10:44
  • Facial Expression Recognition Project Summary
    01:20

  • Backpropagation Supplementary Lectures Introduction
    01:03
  • Why Learn the Ins and Outs of Backpropagation?
    08:53
  • Gradient Descent Tutorial
    04:30
  • Help with Softmax Derivative
    04:09
  • Backpropagation with Softmax Troubleshooting
    11:55

  • What's the difference between "neural networks" and "deep learning"?
    07:58
  • Preview11:18
  • Where does this course fit into your deep learning studies?
    10:43

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

Description

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)

Who this course is for:

  • 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.

Featured review

Wkchiu
Wkchiu
84 courses
49 reviews
Rating: 5.0 out of 59 months ago
Very good course for deep learning. Not just teaching the intuition and teach you how to use the api. But spend most of the time teaching the concept and derivation of the algorithm. Now I can really understand how I construct a neural network and without the api. However, as the tensorflow used in this course is really old, it may be better to take the tensorflow 2.0 course first.

Instructor

Lazy Programmer Inc.
Artificial intelligence and machine learning engineer
Lazy Programmer Inc.
  • 4.6 Instructor Rating
  • 108,181 Reviews
  • 422,558 Students
  • 28 Courses

Today, I spend most of my time as an artificial intelligence and machine learning engineer with a focus on deep learning, although I have also been known as a data scientist, big data engineer, and full stack software engineer.

I received my masters degree in computer engineering with a specialization in machine learning and pattern recognition.

Experience includes online advertising and digital media as both a data scientist (optimizing click and conversion rates) and big data engineer (building data processing pipelines). Some big data technologies I frequently use are Hadoop, Pig, Hive, MapReduce, and Spark.

I've created deep learning models to predict click-through rate and user behavior, as well as for image and signal processing and modeling text.

My work in recommendation systems has applied Reinforcement Learning and Collaborative Filtering, and we validated the results using A/B testing.

I have taught undergraduate and graduate students in data science, statistics, machine learning, algorithms, calculus, computer graphics, and physics for students attending universities such as Columbia University, NYU, Hunter College, and The New School. 

Multiple businesses have benefitted from my web programming expertise. I do all the backend (server), frontend (HTML/JS/CSS), and operations/deployment work. Some of the technologies I've used are: Python, Ruby/Rails, PHP, Bootstrap, jQuery (Javascript), Backbone, and Angular. For storage/databases I've used MySQL, Postgres, Redis, MongoDB, and more.

  • Udemy for Business
  • Teach on Udemy
  • Get the app
  • About us
  • Contact us
  • Careers
  • Blog
  • Help and Support
  • Affiliate
  • Terms
  • Privacy policy
  • Cookie settings
  • Sitemap
  • Featured courses
Udemy
© 2021 Udemy, Inc.