Math and Architectures of Deep Learning

table of contentsREAD IN LIVEBOOK1AN OVERVIEW OF MACHINE LEARNING AND DEEP LEARNINGREAD IN LIVEBOOK2INTRODUCTION TO VECTORS, MATRICES AND TENSORS FROM MACHINE LEARNING AND DATA SCIENCE POINT OF VIEWREAD IN LIVEBOOK3INTRODUCTION TO VECTOR CALCULUS FROM MACHINE LEARNING POINT OF VIEWREAD IN LIVEBOOK4LINEAR ALGEBRAIC TOOLS IN MACHINE LEARNING AND DATA SCIENCEREAD IN LIVEBOOK5PROBABILITY DISTRIBUTIONS FOR MACHINE LEARNING AND DATA SCIENCEREAD IN LIVEBOOK6BAYESIAN TOOLS FOR MACHINE LEARNING AND DATA SCIENCEREAD IN LIVEBOOK7FUNCTION APPROXIMATION: HOW NEURAL NETWORKS MODEL THE WORLDREAD IN LIVEBOOK8TRAINING NEURAL NETWORKS: FORWARD AND BACKPROPAGATIONREAD IN LIVEBOOK9LOSS, OPTIMIZATION AND REGULARIZATIONREAD IN LIVEBOOK10ONE, TWO AND THREE DIMENSIONAL CONVOLUTION AND TRANSPOSED CONVOLUTION IN NEURAL NETWORKS11 IMAGE ANALYSIS: 2D CONVOLUTION BASED NEURAL NETWORK ARCHITECTURES FOR OBJECT RECOGNITION AND DETECTION12 VIDEO ANALYSIS: 3D CONVOLUTION BASED SPATIO TEMPORAL NEURAL NETWORK ARCHITECTURESREAD IN LIVEBOOKAPPENDIX A: APPENDIXA.1Dot Product and cosine of the angle between two vectorsA.2Computing variance of Gaussian DistributionA.3Two Theorems in Statistic

Krishnendu Chaudhury is a deep learning and computer vision expert with decade-long stints at both Google and Adobe Systems. He is presently CTO and co-founder of Drishti Technologies. He has a PhD in computer science from the University of Kentucky at Lexington.