Deep Learning: Convolutional Neural Networks in Python
4.5 (2,966 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
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Deep Learning: Convolutional Neural Networks in Python

Use CNNs for Image Recognition, Natural Language Processing (NLP) +More! For Data Science, Machine Learning, and AI
Bestseller
4.5 (2,965 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
22,942 students enrolled
Last updated 8/2020
English
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Current price: $83.99 Original price: $119.99 Discount: 30% off
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This course includes
  • 10.5 hours on-demand video
  • 1 article
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
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What you'll 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)
Course content
Expand all 64 lectures 10:31:05
+ Google Colab
3 lectures 33:07
Intro to Google Colab, how to use a GPU or TPU for free
12:32
Uploading your own data to Google Colab
11:41
Where can I learn about Numpy, Scipy, Matplotlib, Pandas, and Scikit-Learn?
08:54
+ Machine Learning and Neurons
12 lectures 01:39:18
Review Section Introduction
02:37
What is Machine Learning?
14:26
Code Preparation (Classification Theory)
15:59
Beginner's Code Preamble
04:38
Classification Notebook
08:40
Code Preparation (Regression Theory)
07:18
Regression Notebook
10:34
How does a model "learn"?
10:53
Making Predictions
06:45
Saving and Loading a Model
04:27
Suggestion Box
03:03
+ Feedforward Artificial Neural Networks
9 lectures 01:36:21
Artificial Neural Networks Section Introduction
06:00
Forward Propagation
09:40
The Geometrical Picture
09:43
Activation Functions
17:18
Multiclass Classification
08:41
How to Represent Images
12:36
Code Preparation (ANN)
12:42
ANN for Image Classification
08:36
ANN for Regression
11:05
+ Convolutional Neural Networks
11 lectures 01:57:05
What is Convolution? (part 1)
16:38
What is Convolution? (part 2)
05:56
What is Convolution? (part 3)
06:41
Convolution on Color Images
15:58
CNN Architecture
20:58
CNN Code Preparation
15:13
CNN for Fashion MNIST
06:46
CNN for CIFAR-10
04:28
Data Augmentation
08:51
Batch Normalization
05:14
Improving CIFAR-10 Results
10:22
+ Natural Language Processing (NLP)
5 lectures 46:16
Embeddings
13:12
Code Preparation (NLP)
13:17
Text Preprocessing
05:30
CNNs for Text
08:07
Text Classification with CNNs
06:10
+ Convolution In-Depth
3 lectures 22:01
Beginner's Guide to Convolution
06:27
Alternative Views on Convolution
06:42
+ Convolutional Neural Network Description
2 lectures 27:26
Convolution on 3-D Images
10:49
Tracking Shapes in a CNN
16:37
+ Practical Tips
1 lecture 11:09
Advanced CNNs and how to Design your Own
11:09
+ Extras
1 lecture 00:06
Colab Notebooks
00:06
Requirements
  • Basic math (taking derivatives, matrix arithmetic, probability) is helpful
  • Python, Numpy, Matplotlib
Description

*** NOW IN TENSORFLOW 2 and PYTHON 3 ***

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 "What order should I take your courses in?" (available in the Appendix of any of my courses, including the free Numpy course)

Who this course is for:
  • 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