Удаление [Manning] Grokking Deep Learning in Motion [ENG-RUS]

Grokking Deep Learning in Motion
Manning на русском
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Автор: Beau Carnes

Студия: Manning

Год выпуска: 2019

Продолжительность: 5h 38m 39 exercises

Язык: Английский

Программное обеспечение:

Спойлер: Оригинальное описание

«Beau Carnes does it again; Breaking down one of the most complex fields of computer science and distilling it into repeatable, practical lessons to enhance a developer's skillset.»
Derek Hampton

Despite being one of the biggest technical leaps in AI in decades, building an understanding in deep learning doesn't mean you need a math degree. All it takes is the right intuitive approach, and you'll be writing your own neural networks in pure Python in no time!

table of contents

• Introducing deep learning
  • Introduction
  • What you need to get started
• Fundamental concepts
  • What are deep learning and machine learning?
  • Supervised vs. unsupervised learning
  • Parametric vs. non-parametric learning
• Introduction to neural prediction
  • Making a prediction
  • What does a neural network do?
  • Multiple inputs
  • Multiple outputs and stacking predictions
  • Primer on NumPy
• Introduction to neural learning
  • Compare and learn
  • Why measure error?
  • Hot and cold learning
  • Practice exercises
  • Gradient descent
  • Learning with gradient decent
  • Put it into practice
  • The secret to learning
  • How to use a derivative to learn
  • Put it into practice
  • Alpha
• Learning multiple weights at a time
  • Gradient descent learning with multiple inputs
  • Several steps of learning
  • Put it into practice
  • Gradient descent with multiple outputs
  • Visualizing weight values
• Building your first "deep" neural network
  • The streetlight problem
  • Building our neural network
  • Put it into practice
  • Up and down pressure
  • Correlation and backpropagation
  • Linear vs. non-linear
  • Our first "deep" neural network
  • Put it into practice
• How to picture neural networks
  • Simplifying
  • Simplified visualization
  • Seeing the network predict
• Learning signal and ignoring noise
  • 3-layer network on MNIST
  • Overfitting in neural networks
  • Regularization: Early stopping and dropout
  • Put it into practice
• Modeling probabilities and non-linearities
  • Activation function constraints
  • Standard activation functions
  • Softmax and implementation in code
  • Put it into practice
• Conclusion
  • Where to go from here
  • Credits

about the subject

Machine learning has made remarkable progress in recent years. Deep-learning systems now enable previously impossible smart applications, revolutionizing image recognition and natural-language processing, and identifying complex patterns in data. To really get the most out of deep learning, you need to understand it inside and out, but where do you start? This liveVideo course is the perfect jumping off point!

about the video

Grokking Deep Learning in Motion is a new liveVideo course that takes you on a journey into the world of deep learning. Rather than just learn how to use a single library or framework, you’ll actually discover how to build these algorithms completely from scratch!

Professional instructor Beau Carnes breaks deep learning wide open, drawing together his expertise in video instruction and Andrew Trask's unique, intuitive approach from Grokking Deep Learning! As you move through this course, you’ll learn the fundamentals of deep learning from a unique standing! Using Python, as well as Jupyter Notebooks, you’ll get stuck right in to the basics of neural prediction and learning, and teach your algorithms to visualize things like different weights. Throughout, you’ll train your neural network to be smarter, faster, and better at its job in a variety of ways, ready for the real world!

Packed with great animations and explanations that bring the world of deep learning to life in a way that just makes sense, Grokking Deep Learning in Motion is exactly what anyone needs to build an intuitive understanding of one of the hottest techniques in machine learning.

This liveVideo also works perfectly alongside the original Grokking Deep Learning by Andrew Trask, bringing his unique way to teaching to life.

prerequisites

This liveVideo course is perfect for anyone with high school-level math and basic programming skills with a language like Python. Experience with Calculus is helpful but NOT required.

what you will learn

• The differences between deep and machine learning
• An introduction to neural prediction
• Building your first deep neural network
• The importance of visualization tools
• Memorization vs Generalization
• Modeling probabilities and non-linearities

about the instructor

liveVideo instructor Beau Carnes is a software developer and a recognized authority in software instruction. Besides teaching in-person workshops and classes, Beau has recently joined the team at freeCodeCamp as their lead video instructor, helping to teach over 2 million people around the world to code. Beau also teaches Manning's best-selling video course, Algorithms in Motion.

Страница курса:

Курс зарезервирует переводчик, когда запишутся минимум 10-15 человек