Zefs Guide to Deep Learning Flashcards (Flashcards (PDF, Anki, PNG formats))
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Zefs Guide to Deep Learning Flashcards

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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    • Machine Learning
    • Digital Transformation
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About the Author

Roy Keyes
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.

Roy Keyes

Episode 207

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Reader Testimonials

Emil Wallner
Emil Wallner

Resident at Google. Author of "No Degree ML"

The visualizations are fantastic. This book is super helpful for those who are looking to improve their understanding of deep learning.

Nick Singh
Nick Singh

Author of "Ace the Data Science Interview"

Zefs Guide provides a clear, succinct, and direct explanation of the most important topics in ML and Deep Learning. But words only go so far – it’s the visuals which illustrate the key concepts of the field where the book and flashcards truly shine!

Santi Tellez
Santi Tellez

ML Engineer NVIDIA

The flash cards are a real game changer. I've never been good reading a paragraph and remembering what I just read, but with pictures it makes it much easier.

Table of Contents

  • 1 What this is
  • 2 Card Index
  • 3 Applications of deep learning
  • 4 Hand-coded vs learned decision logic
  • 5 Classification vs Regression
  • 6 Supervised classification vs unsupervised classification
  • 7 Training supervised learning models
  • 8 Loss functions
  • 9 Training a regression model
  • 10 Learning curves and overfitting
  • 11 Bias and variance in estimates
  • 12 Bias and variance in ML models
  • 13 Model regularization
  • 14 Common machine learning models
  • 15 A simple neural network
  • 16 The binary perceptron: a binary neuron model
  • 17 The Sigmoid function
  • 18 Hyperbolic tangent
  • 19 Rectified linear unit (ReLU)
  • 20 Leaky ReLU
  • 21 Exponential linear unit (ELU)
  • 22 Gradient descent
  • 23 Backpropagation in a simple neural network
  • 24 Backpropagation to build the gradient
  • 25 Neural networks transform feature space
  • 26 Image classification
  • 27 Object detection and localization
  • 28 Image segmentation
  • 29 Image transformation
  • 30 Image generation
  • 31 Robustness to image transformations
  • 32 Hierarchical feature extraction in CNNs
  • 33 1D convolution
  • 34 2D convolution
  • 35 Convolution stride length
  • 36 Convolution padding
  • 37 Convolution pooling
  • 38 A basic convolutional neural network
  • 39 AlexNet
  • 40 ResNet and residual blocks
  • 41 The autoencoder
  • 42 U-Net
  • 43 Transposed convolutions
  • 44 YOLO
  • 45 Non-maximum supression
  • 46 Intersection over union
  • 47 Generative adversarial networks
  • 48 Dropout
  • 49 Data augmentation
  • 50 Variations of gradient descent
  • 51 Transfer learning
  • 52 Types of sequential data tasks
  • 53 Recurrent neural networks
  • 54 Backpropagation through time
  • 55 Feature embeddings
  • 56 Embeddings and tasks
  • 57 Cosine similarity
  • 58 Word embeddings and semantics
  • 59 Word2Vec training
  • 60 Gated recurrent units (GRU)
  • 61 The encoder-decoder RNN architecture
  • 62 The attention mechanism
  • 63 Attention weights
  • 64 The high-level Transformer architecture
  • 65 The self-attention mechanism
  • 66 Self-attention weights
  • 67 The Transformer (detailed architecture)
  • 68 Image captioning
  • 69 Joint embeddings of multi-modal data
  • 70 The diffusion process
  • 71 Stable diffusion
  • 72 Self-supervised learning
  • 73 Contrastive learning

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