Deep Learning: Convolutional Neural Networks in Python

Deep Learning: Convolutional Neural Networks in Python, available at $124.99, has an average rating of 4.62, with 118 lectures, based on 5938 reviews, and has 41652 subscribers.

You will learn about 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 This course is ideal for individuals who are Students, professionals, and anyone else interested in Deep Learning, Computer Vision, or NLP or Software Engineers and Data Scientists who want to level up their career It is particularly useful for Students, professionals, and anyone else interested in Deep Learning, Computer Vision, or NLP or Software Engineers and Data Scientists who want to level up their career.

Enroll now: Deep Learning: Convolutional Neural Networks in Python

Summary

Title: Deep Learning: Convolutional Neural Networks in Python

Price: $124.99

Average Rating: 4.62

Number of Lectures: 118

Number of Published Lectures: 79

Number of Curriculum Items: 118

Number of Published Curriculum Objects: 79

Original Price: $199.99

Quality Status: approved

Status: Live

What You Will Learn

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

Who Should Attend

Students, professionals, and anyone else interested in Deep Learning, Computer Vision, or NLP
Software Engineers and Data Scientists who want to level up their career

Target Audiences

Students, professionals, and anyone else interested in Deep Learning, Computer Vision, or NLP
Software Engineers and Data Scientists who want to level up their career

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

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: Google Colab

Lecture 1: Intro to Google Colab, how to use a GPU or TPU for free

Lecture 2: Uploading your own data to Google Colab

Lecture 3: Where can I learn about Numpy, Scipy, Matplotlib, Pandas, and Scikit-Learn?

Lecture 4: Temporary 403 Errors

Chapter 3: Machine Learning and Neurons

Lecture 1: Review Section Introduction

Lecture 2: What is Machine Learning?

Lecture 3: Code Preparation (Classification Theory)

Lecture 4: Classification Notebook

Lecture 5: Code Preparation (Regression Theory)

Lecture 6: Regression Notebook

Lecture 7: The Neuron

Lecture 8: How does a model "learn"?

Lecture 9: Making Predictions

Lecture 10: Saving and Loading a Model

Lecture 11: Suggestion Box

Chapter 4: Feedforward Artificial Neural Networks

Lecture 1: Artificial Neural Networks Section Introduction

Lecture 2: Forward Propagation

Lecture 3: The Geometrical Picture

Lecture 4: Activation Functions

Lecture 5: Multiclass Classification

Lecture 6: How to Represent Images

Lecture 7: Color Mixing Clarification

Lecture 8: Code Preparation (ANN)

Lecture 9: ANN for Image Classification

Lecture 10: ANN for Regression

Chapter 5: Convolutional Neural Networks

Lecture 1: What is Convolution? (part 1)

Lecture 2: What is Convolution? (part 2)

Lecture 3: What is Convolution? (part 3)

Lecture 4: Why use 0-indexing?

Lecture 5: Convolution on Color Images

Lecture 6: CNN Architecture

Lecture 7: CNN Code Preparation

Lecture 8: CNN for Fashion MNIST

Lecture 9: CNN for CIFAR-10

Lecture 10: Data Augmentation

Lecture 11: Batch Normalization

Lecture 12: Improving CIFAR-10 Results

Chapter 6: Natural Language Processing (NLP)

Lecture 1: Embeddings

Lecture 2: Code Preparation (NLP)

Lecture 3: Text Preprocessing

Lecture 4: CNNs for Text

Lecture 5: Text Classification with CNNs

Chapter 7: Convolution In-Depth

Lecture 1: Real-Life Examples of Convolution

Lecture 2: Beginner's Guide to Convolution

Lecture 3: Alternative Views on Convolution

Chapter 8: Convolutional Neural Network Description

Lecture 1: Convolution on 3-D Images

Lecture 2: Tracking Shapes in a CNN

Chapter 9: Practical Tips

Lecture 1: Advanced CNNs and how to Design your Own

Chapter 10: In-Depth: Loss Functions

Lecture 1: Mean Squared Error

Lecture 2: Binary Cross Entropy

Lecture 3: Categorical Cross Entropy

Chapter 11: In-Depth: Gradient Descent

Lecture 1: Gradient Descent

Lecture 2: Stochastic Gradient Descent

Lecture 3: Momentum

Lecture 4: Variable and Adaptive Learning Rates

Lecture 5: Adam (pt 1)

Lecture 6: Adam (pt 2)

Chapter 12: 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 13: Extra Help With Python Coding for Beginners (FAQ by Student Request)

Lecture 1: Beginner's Coding Tips

Lecture 2: Get Your Hands Dirty, Practical Coding Experience, Data Links

Lecture 3: Where To Get the Code Troubleshooting

Lecture 4: How to use Github & Extra Coding Tips (Optional)

Lecture 5: How to Code by Yourself (part 1)

Lecture 6: How to Code by Yourself (part 2)

Lecture 7: How to Uncompress a .tar.gz file

Lecture 8: Proof that using Jupyter Notebook is the same as not using it

Lecture 9: Python 2 vs Python 3

Lecture 10: Is Theano Dead?

Chapter 14: 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: Machine Learning and AI Prerequisite Roadmap (pt 1)

Lecture 4: Machine Learning and AI Prerequisite Roadmap (pt 2)

Chapter 15: Appendix / FAQ Finale

Lecture 1: What is the Appendix?

Lecture 2: BONUS

Instructors

Lazy Programmer Inc.
Artificial intelligence and machine learning engineer

Rating Distribution

1 stars: 63 votes
2 stars: 76 votes
3 stars: 271 votes
4 stars: 2001 votes
5 stars: 3527 votes

Frequently Asked Questions

How long do I have access to the course materials?

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

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!

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