Deep Learning: Convolutional Neural Networks in Python from Udemy

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.

Learn about one of the most powerful Deep Learning architectures yet.

The Convolutional Neural Network (CNN) has been used to obtain state-of-the-art results in computer vision tasks such as object detection, image segmentation, and generating photo-realistic images of people and things that don't exist in the real world.

This course will teach you the fundamentals of convolution and why it's useful for deep learning and even NLP (natural language processing).

You will learn about modern techniques such as data augmentation and batch normalization, and build modern architectures such as VGG yourself.

This course will teach you:

The basics of machine learning and neurons (just a review to get you warmed up. )
Neural networks for classification and regression (just a review to get you warmed up. )
How to model image data in code
How to model text data for NLP (including preprocessing steps for text)
How to build an CNN using Tensorflow 2
How to use batch normalization and dropout regularization in Tensorflow 2
How to do image classification in Tensorflow 2
How to do data preprocessing for your own custom image dataset
How to use Embeddings in Tensorflow 2 for NLP
How to build a Text Classification CNN for NLP (examples: spam detection, sentiment analysis, parts-of-speech tagging, named entity recognition)

All of the materials required for this course can be downloaded and installed for FREE. We will do most of our work in Numpy, Matplotlib, and Tensorflow. I am always available to answer your questions and help you along your data science journey.

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.

Suggested Prerequisites:

matrix addition and multiplication
basic probability (conditional and joint distributions)
Python coding: if/else, loops, lists, dicts, sets
Numpy coding: matrix and vector operations, loading a CSV file

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)

UNIQUE FEATURES

Every line of code explained in detail - email me any time if you disagree
No wasted time "typing" on the keyboard like other courses - let's be honest, nobody can really write code worth learning about in just 20 minutes from scratch
Not afraid of university-level math - get important details about algorithms that other courses leave out

What's inside

Learning objectives

Understand convolution and why it's useful for deep learning
Understand and explain the architecture of a convolutional neural network (cnn)
Implement a cnn in tensorflow 2

Apply cnns to challenging image recognition tasks
Apply cnns to natural language processing (nlp) for text classification (e.g. spam detection, sentiment analysis)
Understand important foundations for openai chatgpt, gpt-4, dall-e, midjourney, and stable diffusion

Understand convolution and why it's useful for deep learning
Understand and explain the architecture of a convolutional neural network (cnn)
Implement a cnn in tensorflow 2
Apply cnns to challenging image recognition tasks
Apply cnns to natural language processing (nlp) for text classification (e.g. spam detection, sentiment analysis)
Understand important foundations for openai chatgpt, gpt-4, dall-e, midjourney, and stable diffusion

Syllabus

Recap essential parts of deep learning covered previously, and learn about what will be covered in this course.

Introduction and Outline

Get Your Hands Dirty, Practical Coding Experience, Data Links

How to use Github & Extra Coding Tips (Optional)

Where to get the code, notebooks, and data

How to Succeed in this Course

Google Colab

Intro to Google Colab, how to use a GPU or TPU for free

Uploading your own data to Google Colab

Where can I learn about Numpy, Scipy, Matplotlib, Pandas, and Scikit-Learn?

Temporary 403 Errors

Machine Learning and Neurons

Review Section Introduction

What is Machine Learning?

Code Preparation (Classification Theory)

Classification Notebook

Code Preparation (Regression Theory)

Regression Notebook

The Neuron

How does a model "learn"?

Making Predictions

Saving and Loading a Model

Suggestion Box

Feedforward Artificial Neural Networks

Artificial Neural Networks Section Introduction

Forward Propagation

The Geometrical Picture

Activation Functions

Multiclass Classification

How to Represent Images

Color Mixing Clarification

Code Preparation (ANN)

ANN for Image Classification

ANN for Regression

Convolutional Neural Networks

What is Convolution? (part 1)

What is Convolution? (part 2)

What is Convolution? (part 3)

Why use 0-indexing?

Convolution on Color Images

CNN Architecture

CNN Code Preparation

CNN for Fashion MNIST

CNN for CIFAR-10

Data Augmentation

Batch Normalization

Improving CIFAR-10 Results

Natural Language Processing (NLP)

Embeddings

Code Preparation (NLP)

Text Preprocessing

CNNs for Text

Text Classification with CNNs

Learn about convolution and its application to audio, images, and physical systems.

Real-Life Examples of Convolution

Beginner's Guide to Convolution

Alternative Views on Convolution

Learn about the architecture of a convolutional neural network and how it is related to the animal visual cortex.

Convolution on 3-D Images

Tracking Shapes in a CNN

Practical tips for training convolutional neural networks.

Advanced CNNs and how to Design your Own

In-Depth: Loss Functions

Mean Squared Error

Binary Cross Entropy

Categorical Cross Entropy

In-Depth: Gradient Descent

Gradient Descent

Stochastic Gradient Descent

Momentum

Variable and Adaptive Learning Rates

Adam (pt 1)

Adam (pt 2)

Setting Up Your Environment (FAQ by Student Request)

Pre-Installation Check

Anaconda Environment Setup

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

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

Beginner's Coding Tips

How to Code by Yourself (part 1)

How to Code by Yourself (part 2)

How to Uncompress a .tar.gz file

Proof that using Jupyter Notebook is the same as not using it

Python 2 vs Python 3

Is Theano Dead?

Effective Learning Strategies for Machine Learning (FAQ by Student Request)

How to Succeed in this Course (Long Version)

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

Machine Learning and AI Prerequisite Roadmap (pt 1)

Machine Learning and AI Prerequisite Roadmap (pt 2)

Misc. topics tangentially related to the course that may help you with the course materials

What is the Appendix?

BONUS

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Develops understanding of how image data can be represented in code

Demystifies deep learning models by providing visualizations to understand internal workings

Focuses on teaching practical skills in building and understanding models

Provides practical tips for training convolutional neural networks

Covers advanced CNNs and how to design custom CNNs

Requires prerequisite knowledge in matrix addition and multiplication, basic probability, Python coding, and Numpy coding

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Deep Learning: Convolutional Neural Networks in Python with these activities:

Review Linear Algebra Concepts

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Strengthen foundational knowledge in linear algebra, which is essential for understanding deep learning concepts.

Browse courses on Linear Algebra

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Review basic matrix operations, such as addition, subtraction, and multiplication
Understand concepts of linear transformations and eigenvalues
Apply linear algebra to solve systems of equations and manipulate data

Revisit Probability and Statistics Concepts

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Refresh knowledge in probability and statistics, which are fundamental to understanding machine learning algorithms.

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Review basic concepts of probability, such as conditional probability and Bayes' theorem
Understand statistical distributions, including normal, binomial, and Poisson distributions
Practice applying statistical techniques to analyze data

Follow TensorFlow 2 Tutorials

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Enhance understanding of TensorFlow 2 by following guided tutorials and implementing sample code.

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Work through the TensorFlow 2 tutorials on the official website
Build and train simple neural network models using TensorFlow 2
Experiment with data preprocessing and model evaluation techniques

Five other activities

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Show all eight activities

Practice Python Coding Problems

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Reinforce Python coding skills by completing programming problems related to data manipulation and analysis.

Browse courses on scikit-learn

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Solve at least 10 Python coding problems each week
Focus on applying Numpy and pandas for data manipulation tasks
Utilize Scikit-learn for data analysis and modeling

Create a Visual Guide to Convolution

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Solidify understanding of convolution by creating a visual guide that explains the concept.

Browse courses on Convolution

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Research different visual representations of convolution
Design and create a visual guide that illustrates the process of convolution
Include examples and explanations to make the guide easy to understand
Share the guide with peers or post it online for feedback

Explore Convolutional Neural Network (CNN) Architectures

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Gain insights into the design and implementation of various Convolutional Neural Network (CNN) architectures.

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Study the architecture of popular CNN models, such as VGG, ResNet, and Inception
Understand the role of different layers and components in CNNs
Explore advanced CNN architectures for specific tasks, such as object detection or image segmentation (optional)

Practice Convolutional Neural Network exercises

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Enhance understanding of Convolutional Neural Networks (CNNs) through hands-on exercises.

Browse courses on Convolutional Neural Networks

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Complete coding exercises involving the implementation of CNNs for image classification
Experiment with different CNN architectures, such as VGG and ResNet
Apply data augmentation techniques to improve model performance
Implement CNNs for object detection or image segmentation tasks (optional)

Solve Text Classification exercises

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Gain proficiency in text classification tasks using Convolutional Neural Networks (CNNs).

Browse courses on Text Classification

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Practice building CNN-based text classification models
Experiment with different text preprocessing techniques
Utilize word embeddings to capture semantic relationships in text
Apply text classification models to real-world datasets (optional)

Career center

Learners who complete Deep Learning: Convolutional Neural Networks in Python will develop knowledge and skills that may be useful to these careers:

Computer Vision Engineer

As a Computer Vision Engineer, you will develop algorithms and systems that enable computers to see and understand images and videos. CNNs are a key technology in computer vision, and this course will give you the skills you need to work with them effectively.

See salaries and explore the career path for Computer Vision Engineer

Machine Learning Engineer

As a Machine Learning Engineer, you will use machine learning algorithms to solve business problems. CNNs are a powerful tool for machine learning, and this course will give you the skills you need to use them effectively.

See salaries and explore the career path for Machine Learning Engineer

Data Scientist

As a Data Scientist, you will use computational tools to extract insights from large datasets. Understanding Convolutional Neural Networks (CNNs) is key to working with images and other complex data. This course will help you build a foundation in CNNs, a skill in high demand for Data Scientists.

See salaries and explore the career path for Data Scientist

Artificial Intelligence Engineer

As an Artificial Intelligence Engineer, you will be responsible for designing, developing and maintaining AI systems. CNNs are a crucial part of many AI applications, and this course will provide you with the knowledge and skills to work with them effectively.

See salaries and explore the career path for Artificial Intelligence Engineer

Deep Learning Engineer

As a Deep Learning Engineer, you will develop and implement deep learning models for a variety of applications. CNNs are a key type of deep learning model, and this course will give you the skills you need to work with them effectively.

See salaries and explore the career path for Deep Learning Engineer

Natural Language Processing Engineer

As a Natural Language Processing Engineer, you will develop algorithms and systems that enable computers to understand and generate human language. CNNs have recently been applied to NLP, and this course will give you the skills you need to use them effectively.

See salaries and explore the career path for Natural Language Processing Engineer

Research Scientist

As a Research Scientist, you will conduct research and development in a variety of fields. CNNs are used in many different areas of research, and this course will give you the skills you need to work with them effectively.

See salaries and explore the career path for Research Scientist

Software Engineer

As a Software Engineer, you will design, develop, and maintain software systems. CNNs are increasingly used in software applications, and this course will give you the skills you need to work with them effectively.

See salaries and explore the career path for Software Engineer

Actuary

As an Actuary, you will use mathematical and statistical models to assess risk. CNNs have recently been applied to actuarial science, and this course will give you the skills you need to use them effectively.

See salaries and explore the career path for Actuary

Business Intelligence Analyst

As a Business Intelligence Analyst, you will use data to help businesses make better decisions. CNNs can be used to extract insights from images and other complex data, and this course will give you the skills you need to use them effectively.

See salaries and explore the career path for Business Intelligence Analyst

Statistician

As a Statistician, you will collect, analyze, and interpret data. CNNs can be used to extract insights from images and other complex data, and this course will give you the skills you need to use them effectively.

See salaries and explore the career path for Statistician

Operations Research Analyst

As an Operations Research Analyst, you will use mathematical and analytical methods to improve the efficiency of systems. CNNs have recently been applied to operations research, and this course will give you the skills you need to use them effectively.

See salaries and explore the career path for Operations Research Analyst

Data Analyst

As a Data Analyst, you will collect, clean, and analyze data to identify trends and patterns. CNNs can be used to extract insights from images and other complex data, and this course will give you the skills you need to use them effectively.

See salaries and explore the career path for Data Analyst

Quantitative Analyst

As a Quantitative Analyst, you will use mathematical and statistical models to analyze financial data. CNNs have recently been applied to quantitative finance, and this course will give you the skills you need to use them effectively.

See salaries and explore the career path for Quantitative Analyst

Product Manager

As a Product Manager, you will be responsible for the development and launch of new products. Understanding Convolutional Neural Networks (CNNs) can be helpful for developing products that use images or other complex data.

See salaries and explore the career path for Product Manager