Heart Of Ai - A Theoretical Odyssey On Machine Learning

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[ FreeCourseWeb.com ] Heart Of Ai - A Theoretical Odyssey On Machine Learning
  • Get Bonus Downloads Here.url (0.2 KB)
    • ~Get Your Files Here ! 1 - Introduction Of The Course
    • 1 - Course Structure.mp4 (30.1 MB)
    2 - Linear Algebra For Machine Learning
    • 1 - L2 Norms exercise.html (0.1 KB)
    • 10 - QR Decomposition and GramSchmid Process.mp4 (47.7 MB)
    • 2 - Vectors and Matrices Scalar Vector Matrix Tensor.mp4 (23.7 MB)
    • 3 - Vector Operations.mp4 (30.5 MB)
    • 4 - Matrix Operations.mp4 (45.5 MB)
    • 5 - Norms in ML.mp4 (59.1 MB)
    • 6 - Linear Map And Linear Transformation.mp4 (57.5 MB)
    • 7 - Eigenvalues and Eigenvectors.mp4 (36.4 MB)
    • 8 - Principal Component Analysis.mp4 (32.0 MB)
    • 9 - LU Decomposition.mp4 (52.7 MB)
    3 - Calculus And Optimizations
    • 11 - Basics of Calculus Derivatives and Partial Derivatives.mp4 (40.5 MB)
    • 12 - Gradients and Directional Derivatives.mp4 (27.8 MB)
    • 13 - Integration Double Triple integrals.mp4 (40.5 MB)
    • 14 - Local And Global MinimaMaxima.mp4 (37.3 MB)
    • 15 - Gradient Descent And Stochastic Gradient Descent.mp4 (45.1 MB)
    • 16 - Newtons Method And Conjugate Gradient Descent.mp4 (26.1 MB)
    • 17 - Regularization Techniques L1 L2 Elastic Net.mp4 (29.0 MB)
    • 2 - Derivatives Quiz.html (0.0 KB)
    • 3 - Integration.html (0.0 KB)
    4 - Probability and Statistics for Machine Learning
    • 18 - Random Variables and Probability Distributions.mp4 (37.0 MB)
    • 19 - Joint Marginal and Conditional Distribution.mp4 (39.7 MB)
    • 20 - Hypothesis Testing.mp4 (32.5 MB)
    • 21 - Confidence Intervals.mp4 (27.2 MB)
    • 22 - Maximum Likelihood Estimation MLE and Bayesian Estimation.mp4 (36.5 MB)
    • 23 - Naive Bayes Classifier.mp4 (26.3 MB)
    • 24 - Gaussian Mixture Models GMMs.mp4 (23.3 MB)
    • 25 - Hidden Markov Models HMMs.mp4 (23.2 MB)
    • 4 - Bayes Theorem.html (0.0 KB)
    5 - Multivariable Calculus and Gradient Based Optimizations
    • 26 - Jacobian Matrices.mp4 (25.4 MB)
    • 27 - Chain Rule and HighOrder Derivatives.mp4 (24.3 MB)
    • 28 - Hessian Matrix and secondorder Conditions.mp4 (25.4 MB)
    • 29 - Backpropagation in Neural Network.mp4 (32.3 MB)
    • 30 - Vanishing And Exploding Gradients.mp4 (45.2 MB)
    • 31 - Optimizers Adam RMSProp SGD.mp4 (16.8 MB)
    6 - Linear Regression and Entropy
    • 32 - Least Square Estimation.mp4 (34.9 MB)
    • 33 - Normal Equations and Matrix Formulations.mp4 (22.7 MB)
    • 34 - Polynomial Regression.mp4 (23.8 MB)
    • 35 - Shannon Entropy.mp4 (19.1 MB)
    • 36 - CrossEntropy Loss.mp4 (25.0 MB)
    • 37 - Kullback Leibler Divergence.mp4 (22.7 MB)
    7 - Neural Networks
    • 38 - FeedForward Neural Networks FNNs.mp4 (27.3 MB)
    • 39 - Convolutional Neural Networks CNNs.mp4 (25.2 MB)
    • 40 - Recurrent Neural Network RNN.mp4 (27.7 MB)
    • 41 - Graph Theory And NN.mp4 (30.7 MB)
    • 42 - Autoencoders and Variational Autoencoders.mp4 (31.1 MB)
    • 43 - Generative Adversarial Networks GANs.mp4 (24.1 MB)
    8 - Advanced Topics in Machine Learning
    • 44 - Image Classification and Object Detection.mp4 (38.7 MB)
    • 45 - Natural Language Processing NLP.mp4 (37.3 MB)
    • 46 - Reinforcement Learning.mp4 (25.6 MB)
    • 47 - Quantum Machine Learning.mp4 (26.5 MB)
    • 48 - PresentationAI.pdf (7.1 MB)
    • 48 - Resources and Further Learning.mp4 (31.9 MB)
    • Bonus Resources.txt (0.4 KB)

Description

Heart Of Ai: A Theoretical Odyssey On Machine Learning



https://FreeCourseWeb.com

Published 9/2023
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz
Language: English | Size: 1.52 GB | Duration: 3h 58m

Unveiling the Elegance of Machine/Deep Learning

What you'll learn
Foundational Understanding of Mathematical Concepts in Machine Learning
Application of Mathematics to Neural Networks
Math-Driven Problem Solving in Deep Learning
Advanced Optimization and Regularization Techniques

Requirements
Basic understanding of algebra, including equations, functions, and basic operations.
Familiarity with basic concepts of calculus, including limits, derivatives, and integrals.
Basic knowledge of probability and statistics, including concepts of probability distributions, mean, and variance.
Basic programming skills in a language like Python, including variables, loops, and functions.
Basic knowledge of machine learning concepts, such as supervised and unsupervised learning.



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Heart Of Ai - A Theoretical Odyssey On Machine Learning

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1.5 GB
seeders:11
leechers:9
Heart Of Ai - A Theoretical Odyssey On Machine Learning

Torrent hash: CF6E003FCEEB789EFFAD885691E6324AC8DE2FC1