Deep Learning: Convolutional Neural Networks in Python

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11.5 часов курса
О курсе

 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)
Программа
Outline and Review
Recap essential parts of deep learning covered previously, and learn about what will be covered in this course.
Introduction and Outline
Review of Important Concepts
Where to get the code and data for this course
How to Succeed in this Course
Tensorflow or Theano - Your Choice!
How to load the SVHN data and benchmark a vanilla deep network
Convolution
Learn about convolution and its application to audio, images, and physical systems.
Real-Life Examples of Convolution
Beginner's Guide to Convolution
What is convolution?
Convolution example with audio: Echo
Требования
  • Basic math (taking derivatives, matrix arithmetic, probability) is helpful
  • Python, Numpy, Matplotlib
Что Вы изучите?
  • 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)
Лекторы
Lazy Programmer Inc.
Lazy Programmer Inc.
Artificial intelligence and machine learning engineer

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.

Платформа
Udemy
Курсы Udemy подойдут для профессионального развития. Платформа устроена таким образом, что эксперты сами запускают курсы. Все материалы передаются в пожизненный доступ. На этой платформе можно найти курс, без преувеличений, на любую тему – начиная от тьюториала по какой-то камере и заканчивая теоретическим курсом по управлению финансовыми рисками. Язык и формат обучения устанавливается преподавателем, поэтому стоит внимательно изучить информацию о курсе перед покупкой.
77.99 $ 119.99 $
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