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Zefs Guide to Deep Learning Flashcards

Zefs Guide to Deep Learning Flashcards is a set of digital flashcards that accompany the book Zefs Guide to Deep Learning. Anyone wanting to improve their knowledge of the key concepts in machine learning and deep learning will benefit from studying with these flashcards, whether to land that dream AI job or ace their machine learning exams.

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About

About

About the Book

Zefs Guide to Deep Learning Flashcards is a set of digital flashcards that accompany the book Zefs Guide to Deep Learning in Anki, PNG, and PDF formats.

The flashcards will help you review and study the core concepts of deep learning, the technique at the center of the current artificial intelligence revolution.

---->> Get the book + the flashcards together in the bundle and save! <<----

Who are these flashcards for?

This flashcard set, as well as the book, Zefs Guide to Deep Learning, are aimed at everyone wanting to get a better understanding of deep learning at the conceptual level. This includes:

  • Job seekers preparing for interviews to get that dream role in AI
  • Students preparing for exams
  • Technology workers wanting to expand their knowledge of the latest techniques
  • Executives and leaders needing to stay on top of the latest trends to more effectively make business decisions and have informed conversations with your technical team members.

Flashcard formats

The flashcards are in Anki , PNG, and PDF formats so that you can use them in whatever way best fits your study setup.

How to get the Anki and PNG flashcards

After you purchase this "book" the Anki and PNG formatted flashcards will be available to you via a download link in the email receipt for your purchase as well as a download link in your Leanpub Library in your Leanpub account. See the instructions here.

The Zefs Guides series

Zefs Guide to Deep Learning is the first book in the Zefs Guides series on deep learning and its applications. It forms the ground knowledge for the other books in the series:

  • Zefs Guide to Computer Vision
  • Zefs Guide to Natural Language Processing
  • Zefs Guide to Transformers

Zefs Guides are designed to help the beginner quickly get up to speed on topics in machine learning and data science and to help the experienced practitioner push their conceptual understanding even further. They are short and to the point, covering the most important topics and concepts. They're for you if you are a job seeker looking for a role in ML/AI/DS, a student studying for exams, an experienced data person dusting off your old knowledge, or an executive seeking a better understanding of some of today's most important technologies.

Learn more and get Zefs Guides merch at zefsguides.com

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Author

About the Author

Roy Keyes

Roy Keyes has worked in data science since 2012, building and leading teams at multiple tech startups as well as consulting for clients across a wide range of industries. Prior to data science, he received a PhD in computational physics, focusing on medical applications.

You can find his website and blog at roycoding.com.

Leanpub Podcast

Episode 207

An Interview with Roy Keyes

Contents

Table of Contents

1What this is

  1. Where are the cards?

2Card Index

3Applications of deep learning

4Hand-coded vs learned decision logic

5Classification vs Regression

6Supervised classification vs unsupervised classification

7Training supervised learning models

8Loss functions

9Training a regression model

10Learning curves and overfitting

11Bias and variance in estimates

12Bias and variance in ML models

13Model regularization

14Common machine learning models

15A simple neural network

16The binary perceptron: a binary neuron model

17The Sigmoid function

18Hyperbolic tangent

19Rectified linear unit (ReLU)

20Leaky ReLU

21Exponential linear unit (ELU)

22Gradient descent

23Backpropagation in a simple neural network

24Backpropagation to build the gradient

25Neural networks transform feature space

26Image classification

27Object detection and localization

28Image segmentation

29Image transformation

30Image generation

31Robustness to image transformations

32Hierarchical feature extraction in CNNs

331D convolution

342D convolution

35Convolution stride length

36Convolution padding

37Convolution pooling

38A basic convolutional neural network

39AlexNet

40ResNet and residual blocks

41The autoencoder

42U-Net

43Transposed convolutions

44YOLO

45Non-maximum supression

46Intersection over union

47Generative adversarial networks

48Dropout

49Data augmentation

50Variations of gradient descent

51Transfer learning

52Types of sequential data tasks

53Recurrent neural networks

54Backpropagation through time

55Feature embeddings

56Embeddings and tasks

57Cosine similarity

58Word embeddings and semantics

59Word2Vec training

60Gated recurrent units (GRU)

61The encoder-decoder RNN architecture

62The attention mechanism

63Attention weights

64The high-level Transformer architecture

65The self-attention mechanism

66Self-attention weights

67The Transformer (detailed architecture)

68Image captioning

69Joint embeddings of multi-modal data

70The diffusion process

71Stable diffusion

72Self-supervised learning

73Contrastive learning

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