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Deep Learning - Linear Classifiers, Gradient Descent, Neural Networks, Data Wrangling, Convolutions, Visualisation, Training, Bias and Fairness, Computer Vision, Language Models, Embeddings, Machine Translation by Various is a nonfiction available to read on EtoBox.

What is Deep Learning - Linear Classifiers, Gradient Descent, Neural Networks, Data Wrangling, Convolutions, Visualisation, Training, Bias and Fairness, Computer Vision, Language Models, Embeddings, Machine Translation about?

1 Introduction 2 Review: Scalar derivative rules 3 Introduction to vector calculus and partial derivatives 4 Matrix calculus 4.1 Generalization of the Jacobian 4.2 Derivatives of vector element-wise binary operators 4.3 Derivatives involving scalar expansion 4.4 Vector sum reduction 4.5 The Chain Rules 4.5.1 Single-variable chain rule 4.5.2 Single-variable total-derivative chain rule 4.5.3 Vector chain rule 5 The gradient of neuron activation 6 The gradient of the neural network loss function 6.1 The gradient with respect to the weights 6.2 The derivative with respect to the bias 7 Summary 8 Matrix Calculus Reference 8.1 Gradients and Jacobians 8.2 Element-wise operations on vectors 8.3 Scalar expansion 8.4 Vector reductions 8.5 Chain rules 9 Notation 10 Resources 1 Introduction 2 What AD Is Not 2.1 AD Is Not Numerical Differentiation 2.2 AD Is Not Symbolic Differentiation 3 AD and Its Main Modes 3.1 Forward Mode 3.1.1 Dual Numbers 3.2 Reverse Mode 3.3 Origins of AD and Backpropagation 4 AD and Machine Learning 4.1 Gradient-Based Optimization 4.2 Neural Networks, Deep Learning, Differentiable Programming 4.3 Computer Vision 4.4 Natural Language Processing 4.5 Probabilistic Modeling

Who reads Deep Learning - Linear Classifiers, Gradient Descent, Neural Networks, Data Wrangling, Convolutions, Visualisation, Training, Bias and Fairness, Computer Vision, Language Models, Embeddings, Machine Translation?

Author

Various

Language

EN

Category

nonfiction

Subjects

Computer Science, Stem