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Deep Learning: Convolutional Neural Networks in Python (Udemy.com)

Computer Vision and Data Science and Machine Learning combined! In Theano and TensorFlow

4.3
( 311 Reviews )

Created by: Lazy Programmer Inc.

Produced in 2018

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What you will learn

  • Understand convolution
  • Understand how convolution can be applied to audio effects
  • Understand how convolution can be applied to image effects
  • Implement Gaussian blur and edge detection in code
  • Implement a simple echo effect in code
  • Understand how convolution helps image classification
  • Understand and explain the architecture of a convolutional neural network (CNN)
  • Implement a convolutional neural network in Theano
  • Implement a convolutional neural network in TensorFlow

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Overall Score : 86 / 100

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Course Description

This is the 3rd part in my Data Science and Machine Learning series on Deep Learning in Python. At this point, you already know a lot about neural networks and deep learning, including not just the basics like backpropagation, but how to improve it using modern techniques like momentum and adaptive learning rates. You've already written deep neural networks in Theano and TensorFlow, and you know how to run code using the GPU.
This course is all about how to use deep learning for computer vision using convolutional neural networks. These are the state of the art when it comes to image classification and they beat vanilla deep networks at tasks like MNIST.
In this course we are going to up the ante and look at the StreetView House Number (SVHN) dataset - which uses larger color images at various angles - so things are going to get tougher both computationally and in terms of the difficulty of the classification task. But we will show that convolutional neural networks, or CNNs, are capable of handling the challenge!
Because convolution is such a central part of this type of neural network, we are going to go in-depth on this topic. It has more applications than you might imagine, such as modeling artificial organs like the pancreas and the heart. I'm going to show you how to build convolutional filters that can be applied to audio, like the echo effect, and I'm going to show you how to build filters for image effects, like the Gaussian blur and edge detection.
We will also do some biology and talk about how convolutional neural networks have been inspired by the animal visual cortex.
After describing the architecture of a convolutional neural network, we will jump straight into code, and I will show you how to extend the deep neural networks we built last time (in part 2) with just a few new functions to turn them into CNNs. We will then test their performance and show how convolutional neural networks written in both Theano and TensorFlow can outperform the accuracy of a plain neural network on the StreetView House Number dataset.
All the materials for this course are FREE. You can download and install Python, Numpy, Scipy, Theano, and TensorFlow with simple commands shown in previous courses.
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:
  • calculus (taking derivatives)
  • matrix addition, multiplication
  • probability
  • Python coding: if/else, loops, lists, dicts, sets
  • Numpy coding: matrix and vector operations, loading a CSV file
  • Can write a feedforward neural network in Theano or TensorFlow


TIPS (for getting through the course):
  • Watch it at 2x.
  • Take handwritten notes. This will drastically increase your ability to retain the information.
  • Write down the equations. If you don't, I guarantee it will just look like gibberish.
  • Ask lots of questions on the discussion board. The more the better!
  • Realize that most exercises will take you days or weeks to complete.
  • Write code yourself, don't just sit there and look at my code.


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 and professional computer scientists
  • Software engineers
  • Data scientists who work on computer vision tasks
  • Those who want to apply deep learning to images
  • Those who want to expand their knowledge of deep learning past vanilla deep networks
  • People who don't know what backpropagation is or how it works should not take this course, but instead, take parts 1 and 2.
  • People who are not comfortable with Theano and TensorFlow basics should take part 2 before taking this course.
Read More product length iconTime: 7.5h

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Instructor Details

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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

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Reviews

4.3

311 total reviews

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By Ranjith C P on 11/14/2020

yes. i am interested to learn machine learning

By Odai Aldawoud on 11/7/2020

The instructor has a fantastic way of teaching, which is theory (which he repeats multiple times to drill the concept into the student's head) followed by code and practice. As a self-learner, I cannot ask for more. You need to follow up on the material yourself with no instructor behind your back, but also there is no stress. The price is the cheapest one can find, and the material is just as much as covered in the curriculum of a top-level university (just with no homework and exams to prove you learned it).

By Marco Schwarzmueller on 10/14/2020

It was quite an intense course and I needed to interruped a lot to learn more in depth about some of the mentioned topics. Nevertheless, I like it very much to be forced to step deeper into these topics to get a better unterstanding. Good would have been to know more about the learning structure mentioned on deeplearningcourses.com before. I liked very much the differeent perspectives on explaining the problems. I think I learned a lot.

By Radosaw Szostak on 10/11/2020

A lot of practical information for beginners.

By Bacem Bcem on 10/9/2020

Not clear enough about the maximum likelihood, need more explanation about the phenomena

By Matan Anavi on 10/5/2020

looks like this course lecturere knows what he is talking about

By Dawa Chyophel Lepcha on 9/22/2020

Satisfied

By Rajendra Maharana on 9/16/2020

Insructor has a really great understnading & depth about the DEEP learning and it was good to have this course.

By Zerrin Sesim Glge on 9/4/2020

Evet kesinlikle

By Aatish on 9/2/2020

good and clear statements

By Rabih on 8/11/2020

Very nice explanation

By Edgar Martin Alvares Ayala on 8/7/2020

Going straight line, I like it and how transparent it is to tell us what we need.
Update: its freaking awesome, Im learnig way more than in other classes where classes werent going this deep into teaching things, theres a lot of valuable information and a lot to learn, though the time of the classes are "small"(10 hours in total) theres so much information on this that you just have to know.

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Deep Learning: Convolutional Neural Networks in Python
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  • Video Duration: icon 7.5h
  • Price: 11.99 Track Price 11.99 Track Price
  • Provider: icon Udemy
  • Year: icon 2018
  • Language: icon English, English [Auto-generated], Indonesian [Auto-generated], 6 more
  • Certificate: icon Yes
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