Emby / illi_pbgilgwvkspiykgfrdpij
00000 Introduction
00001 Topics
00002 1.1_Defining_Linear_Algebra
00003 1.2_Solving_a_System_of_Equations_Algebraically
00005 1.4_Historical_and_Contemporary_Applications
00006 1.5_Exercise
00007 Topics
00008 2.1_Tensors
00009 2.2_Scalars
00010 2.3_Vectors_and_Vector_Transposition
00011 2.4_Norms_and_Unit_Vectors
00012 2.5_Basis_Orthogonal_and_Orthonormal_Vectors
00013 2.6_Matrices
00014 2.7_Generic_Tensor_Notation
00015 2.8_Exercises
00016 Topics
00017 3.1_Tensor_Transposition
00018 3.2_Basic_Tensor_Arithmetic
00019 3.3_Reduction
00020 3.4_The_Dot_Product
00021 3.5_Exercises
00022 Topics
00023 4.1_The_Substitution_Strategy
00024 4.2_Substitution_Exercises
00025 4.3_The_Elimination_Strategy
00026 4.4_Elimination_Exercises
00027 Topics
00028 5.1_Matrix-by-Vector_Multiplication
00029 5.2_Matrix-by-Matrix_Multiplication
00030 5.3_Symmetric_and_Identity_Matrices
00031 5.4_Exercises
00032 5.5_Machine_Learning_and_Deep_Learning_Applications
00033 Topics
00034 6.1_The_Frobenius_Norm
00035 6.2_Matrix_Inversion
00036 6.3_Diagonal_Matrices
00037 6.4_Orthogonal_Matrices
00038 6.5_The_Trace_Operator
00039 Topics
00040 7.1_The_Eigenconcept
00041 7.2_Exercises
00042 7.3_Eigenvectors_in_Python
00043 7.4_High-Dimensional_Eigenvectors
00044 Topics
00045 8.1_The_Determinant_of_a_2_x_2_Matrix
00046 8.2_The_Determinants_of_Larger_Matrices
00047 8.3_Exercises
00048 8.4_Determinants_and_Eigenvalues
00049 8.5_Eigendecomposition
00050 Topics
00051 9.1_Singular_Value_Decomposition
00052 9.2_Media_File_Compression
00053 9.3_The_Moore-Penrose_Pseudoinverse
00054 9.4_Regression_via_Pseudoinversion
00055 9.5_Principal_Component_Analysis
00056 9.6_Resources_for_Further_Study_of_Linear_Algebra
00058 Introduction
00059 Topics
00060 1.1_Differential_versus_Integral_Calculus
00061 1.2_A_Brief_History
00062 1.3_Calculus_of_the_Infinitesimals
00063 1.4_Modern_Applications
00064 Topics
00065 2.1_Continuous_versus_Discontinuous_Functions
00066 2.2_Solving_via_Factoring
00067 2.3_Solving_via_Approaching
00068 2.4_Approaching_Infinity
00069 2.5_Exercises
00070 Topics
00071 3.1_Delta_Method
00072 3.2_The_Most_Common_Representation
00073 3.3_Derivative_Notation
00074 3.4_Constants
00075 3.5_Power_Rule
00076 3.6_Constant_Product_Rule
00077 3.7_Sum_Rule
00078 3.8_Exercises
00079 Topics
00080 4.1_Product_Rule
00081 4.2_Quotient_Rule
00082 4.3_Chain_Rule
00083 4.4_Exercises
00084 4.5_Power_Rule_on_a_Function_Chain
00085 Topics
00086 5.1_Introduction
00087 5.2_Autodiff_with_PyTorch
00088 5.3_Autodiff_with_TensorFlow
00089 5.4_Directed_Acyclic_Graph_of_a_Line_Equation
00090 5.5_Fitting_a_Line_with_Machine_Learning
00091 Topics
00092 6.1_Derivatives_of_Multivariate_Functions
00093 6.2_Partial_Derivative_Exercises
00094 6.3_Geometrical_Examples
00095 6.4_Geometrical_Exercises
00096 6.5_Notation
00097 6.6_Chain_Rule
00098 6.7_Chain_Rule_Exercises
00099 Topics
00100 7.1_Single-Point_Regression
00101 7.2_Partial_Derivatives_of_Quadratic_Cost
00102 7.3_Descending_the_Gradient_of_Cost
00103 7.4_Gradient_of_Mean_Squared_Error
00104 7.5_Backpropagation
00105 7.6_Higher-Order_Partial_Derivatives
00106 7.7_Exercise
00107 Topics
00108 8.1_Binary_Classification
00109 8.2_The_Confusion_Matrix_and_ROC_Curve
00110 8.3_Indefinite_Integrals
00111 8.4_Definite_Integrals
00112 8.5_Numeric_Integration_with_Python
00113 8.6_Exercises
00114 8.7_Finding_the_Area_Under_the_ROC_Curve
00115 8.8_Resources_for_Further_Study_of_Calculus
00116 Calculus_for_Machine_Learning_-_Summary
00117 Introduction
00118 Topics
00120 1.2_What_Probability_Theory_Is
00122 1.4_Multiple_Observations
00123 1.5_Factorials_and_Combinatorics
00124 1.6_Exercises
00125 1.7_The_Law_of_Large_Numbers_and_the_Gambler_s_Fallacy
00126 1.8_Probability_Distributions_in_Statistics
00127 1.9_Bayesian_versus_Frequentist_Statistics
00128 1.10_Applications_of_Probability_to_Machine_Learning
00129 Topics
00130 2.1_Discrete_and_Continuous_Variables
00131 2.2_Probability_Mass_Functions
00132 2.3_Probability_Density_Functions
00133 2.4_Exercises_on_Probability_Functions
00134 2.5_Expected_Value
00135 2.6_Exercises_on_Expected_Value
00136 Topics
00137 3.1_The_Mean_a_Measure_of_Central_Tendency
00138 3.2_Medians
00139 3.3_Modes
00140 3.4_Quantiles_-_Percentiles_Quartiles_and_Deciles
00141 3.5_Box-and-Whisker_Plots
00142 3.6_Variance_a_Measure_of_Dispersion
00143 3.7_Standard_Deviation
00144 3.8_Standard_Error
00145 3.9_Covariance_a_Measure_of_Relatedness
00146 3.10._Correlation
00147 Topics
00148 4.1_Joint_Probability_Distribution
00149 4.2_Marginal_Probability
00150 4.3_Conditional_Probability
00151 4.4_Exercises
00152 4.5_Chain_Rule_of_Probabilities
00153 4.6_Independent_Random_Variables
00154 4.7_Conditional_Independence
00155 Topics
00156 5.1_Uniform
00157 5.2_Gaussian_-_Normal_and_Standard_Normal
00158 5.3_The_Central_Limit_Theorem
00159 5.4_Log-Normal
00160 5.5_Exponential_and_Laplace
00161 5.6_Binomial_and_Multinomial
00162 5.7_Poisson
00163 5.8_Mixture_Distributions
00164 5.9_Preprocessing_Data_for_Model_Input
00165 5.10_Exercises
00166 Topics
00167 6.1_What_Information_Theory_Is
00168 6.2_Self-Information_Nats_and_Bits
00169 6.3_Shannon_and_Differential_Entropy
00170 6.4_Kullback-Leibler_Divergence_and_Cross-Entropy
00171 Topics
00172 7.1_Applications_of_Statistics_to_Machine_Learning
00173 7.2_Review_of_Essential_Probability_Theory
00174 7.3_z-scores_and_Outliers
00175 7.4_Exercises_on_z-scores
00176 7.5_p-values
00177 7.6_Exercises_on_p-values
00178 Topics
00179 8.1_Single-Sample_t-tests_and_Degrees_of_Freedom
00180 8.2_Independent_t-tests
00181 8.3_Paired_t-tests
00182 8.4_Applications_to_Machine_Learning
00183 8.5_Exercises
00184 8.6_Confidence_Intervals
00185 8.7_ANOVA_-_Analysis_of_Variance
00186 Topics
00187 9.1_The_Pearson_Correlation_Coefficient
00188 9.2_R-squared_Coefficient_of_Determination
00189 9.4_Correcting_for_Multiple_Comparisons
00190 Topics
00191 10.1_Independent_versus_Dependent_Variables
00192 10.2_Linear_Regression_to_Predict_Continuous_Values
00193 10.3_Fitting_a_Line_to_Points_on_a_Cartesian_Plane
00194 10.4_Linear_Least_Squares_Exercise
00195 10.5_Ordinary_Least_Squares
00196 10.6_Categorical_Dummy_Features
00197 10.7_Logistic_Regression_to_Predict_Categories
00198 10.8_Open-Ended_Exercises
00199 Topics
00200 11.1_Machine_Learning_versus_Frequentist_Statistics
00201 11.2_When_to_Use_Bayesian_Statistics
00202 11.3_Prior_Probabilities
00203 11.4_Bayes_Theorem
00206 Introduction
00207 Topics
00209 1.2_A_Brief_History_of_Data
00210 1.3_A_Brief_History_of_Algorithms
00211 1.4_Applications_to_Machine_Learning
00212 Topics
00213 2.1_Introduction
00214 2.2_Constant_Time
00215 2.3_Linear_Time
00216 2.4_Polynomial_Time
00217 2.5_Common_Runtimes
00218 2.6_Best_versus_Worst_Case
00219 Topics
00220 3.1_Lists
00221 3.2_Arrays
00222 3.3_Linked_Lists
00223 3.4_Doubly-Linked_Lists
00224 3.5_Stacks
00225 3.6_Queues
00226 3.7_Deques
00227 Topics
00228 4.1_Binary_Search
00229 4.2_Bubble_Sort
00230 4.3_Merge_Sort
00231 4.4_Quick_Sort
00232 Topics
00233 5.1_Maps_and_Dictionaries
00234 5.2_Sets
00235 5.3_Hash_Functions
00236 5.4_Collisions
00237 5.5_Load_Factor
00238 5.6_Hash_Maps
00239 5.7_String_Keys
00240 5.8_Hashing_in_ML
00241 Topics
00242 6.1_Introduction
00243 6.2_Decision_Trees
00244 6.3_Random_Forests
00245 6.4_XGBoost_-_Gradient-Boosted_Trees
00246 6.5_Additional_Concepts
00247 Topics
00248 7.1_Introduction
00249 7.2_Directed_versus_Undirected_Graphs
00250 7.3_DAGs_-_Directed_Acyclic_Graphs
00251 7.4_Additional_Concepts
00252 7.5_Bonus_-_Pandas_DataFrames
00253 7.6_Resources_for_Further_Study_of_DSA
00254 Topics
00255 8.1_Statistics_versus_Machine_Learning
00256 8.2_Objective_Functions
00257 8.3_Mean_Absolute_Error
00258 8.4_Mean_Squared_Error
00259 8.5_Minimizing_Cost_with_Gradient_Descent
00260 8.6_Gradient_Descent_from_Scratch_with_PyTorch
00261 8.7_Critical_Points
00262 8.8_Stochastic_Gradient_Descent
00263 8.9_Learning_Rate_Scheduling
00264 8.10_Maximizing_Reward_with_Gradient_Ascent
00265 Topics
00266 9.1_Jacobian_Matrices
00267 9.2_Second-Order_Optimization_and_Hessians
00268 9.3_Momentum
00269 9.4_Adaptive_Optimizers
00270 9.5_Congratulations_and_Next_Steps