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585б |
| 0 |
177.41Кб |
| 001 Course material.html |
58б |
| 001 First steps in Python_en.vtt |
6.43Кб |
| 001 First steps in Python.mp4 |
13.67Мб |
| 001 Floating point numbers_en.vtt |
9.14Кб |
| 001 Floating point numbers.mp4 |
24.48Мб |
| 001 Gaussian elimination implementation I_en.vtt |
11.52Кб |
| 001 Gaussian elimination implementation I.mp4 |
35.67Мб |
| 001 Graph representation of the WWW_en.vtt |
6.51Кб |
| 001 Graph representation of the WWW.mp4 |
25.06Мб |
| 001 How to deal with differential equations_en.vtt |
9.79Кб |
| 001 How to deal with differential equations.mp4 |
24.50Мб |
| 001 How to measure the running time of algorithms_en.vtt |
12.61Кб |
| 001 How to measure the running time of algorithms.mp4 |
37.29Мб |
| 001 Integration introduction_en.vtt |
3.81Кб |
| 001 Integration introduction.mp4 |
10.91Мб |
| 001 Introduction_en.vtt |
2.44Кб |
| 001 Introduction.mp4 |
13.60Мб |
| 001 Matrix multiplication introduction_en.vtt |
5.06Кб |
| 001 Matrix multiplication introduction.mp4 |
12.46Мб |
| 001 numerical-methods.zip |
3.32Мб |
| 001 Portfolio optimization introduction_en.vtt |
4.03Кб |
| 001 Portfolio optimization introduction.mp4 |
13.13Мб |
| 001 Python crash course introduction.html |
441б |
| 001 Root of functions introduction_en.vtt |
4.06Кб |
| 001 Root of functions introduction.mp4 |
12.28Мб |
| 001 What are eigenvalues and eigenvectors_en.vtt |
5.95Кб |
| 001 What are eigenvalues and eigenvectors.mp4 |
14.38Мб |
| 001 What are functions_en.vtt |
5.22Кб |
| 001 What are functions.mp4 |
17.29Мб |
| 001 What is Gaussian elimination_en.vtt |
6.82Кб |
| 001 What is Gaussian elimination.mp4 |
16.82Мб |
| 001 What is gradient descent_en.vtt |
7.90Кб |
| 001 What is gradient descent.mp4 |
27.80Мб |
| 001 What is interpolation_en.vtt |
10.79Кб |
| 001 What is interpolation.mp4 |
38.72Мб |
| 001 What is object oriented programming (OOP)_en.vtt |
2.86Кб |
| 001 What is object oriented programming (OOP).mp4 |
12.50Мб |
| 001 What is Pandas_en.vtt |
7.88Кб |
| 001 What is Pandas.mp4 |
25.43Мб |
| 001 What is the key advantage of NumPy_en.vtt |
5.01Кб |
| 001 What is the key advantage of NumPy.mp4 |
17.18Мб |
| 001 What is the Monte-Carlo method_en.vtt |
8.15Кб |
| 001 What is the Monte-Carlo method.mp4 |
30.11Мб |
| 002 Bisection method introduction_en.vtt |
4.78Кб |
| 002 Bisection method introduction.mp4 |
10.17Мб |
| 002 Class and objects basics_en.vtt |
3.25Кб |
| 002 Class and objects basics.mp4 |
10.48Мб |
| 002 Crawling the web with breadth-first search_en.vtt |
8.30Кб |
| 002 Crawling the web with breadth-first search.mp4 |
25.88Мб |
| 002 Creating and updating arrays_en.vtt |
8.47Кб |
| 002 Creating and updating arrays.mp4 |
33.76Мб |
| 002 Data structures introduction_en.vtt |
3.98Кб |
| 002 Data structures introduction.mp4 |
13.84Мб |
| 002 Defining functions_en.vtt |
6.14Кб |
| 002 Defining functions.mp4 |
18.85Мб |
| 002 Eigenvalues and eigenvectors implementation_en.vtt |
3.51Кб |
| 002 Eigenvalues and eigenvectors implementation.mp4 |
10.84Мб |
| 002 Euler's method introduction_en.vtt |
7.02Кб |
| 002 Euler's method introduction.mp4 |
12.94Мб |
| 002 First steps_en.vtt |
3.40Кб |
| 002 First steps.mp4 |
10.44Мб |
| 002 GaussElimination.py |
838б |
| 002 Gaussian elimination illustration_en.vtt |
7.85Кб |
| 002 Gaussian elimination illustration.mp4 |
13.62Мб |
| 002 Gaussian elimination implementation II_en.vtt |
8.01Кб |
| 002 Gaussian elimination implementation II.mp4 |
29.57Мб |
| 002 GradientDescent.py |
1.26Кб |
| 002 Gradient descent implementation_en.vtt |
11.30Кб |
| 002 Gradient descent implementation.mp4 |
47.54Мб |
| 002 Interpolation illustration_en.vtt |
6.65Кб |
| 002 Interpolation illustration.mp4 |
15.11Мб |
| 002 MatrixMultiplication.py |
496б |
| 002 Matrix multiplication implementation_en.vtt |
6.41Кб |
| 002 Matrix multiplication implementation.mp4 |
20.57Мб |
| 002 MonteCarloIntegral.py |
1.15Кб |
| 002 Monte-Carlo integral implementation I_en.vtt |
9.51Кб |
| 002 Monte-Carlo integral implementation I.mp4 |
39.77Мб |
| 002 Portfolio optimization implementation_en.vtt |
3.01Кб |
| 002 Portfolio optimization implementation.mp4 |
13.84Мб |
| 002 Precision and accuracy_en.vtt |
3.46Кб |
| 002 Precision and accuracy.mp4 |
9.44Мб |
| 002 Rectangle method introduction_en.vtt |
5.92Кб |
| 002 Rectangle method introduction.mp4 |
18.05Мб |
| 002 What are the basic data types_en.vtt |
5.59Кб |
| 002 What are the basic data types.mp4 |
15.70Мб |
| 003 Applications of eigenvectors in machine learning_en.vtt |
2.22Кб |
| 003 Applications of eigenvectors in machine learning.mp4 |
10.65Мб |
| 003 BisectionMethod.py |
360б |
| 003 Bisection method implementation_en.vtt |
6.44Кб |
| 003 Bisection method implementation.mp4 |
24.12Мб |
| 003 Booleans_en.vtt |
2.21Кб |
| 003 Booleans.mp4 |
6.78Мб |
| 003 Dimension of arrays_en.vtt |
10.65Кб |
| 003 Dimension of arrays.mp4 |
36.26Мб |
| 003 Euler's method example_en.vtt |
6.06Кб |
| 003 Euler's method example.mp4 |
19.65Мб |
| 003 EulerMethodExample1.py |
449б |
| 003 Gradient descent with momentum_en.vtt |
4.71Кб |
| 003 Gradient descent with momentum.mp4 |
20.02Мб |
| 003 Interpolation implementation I_en.vtt |
7.22Кб |
| 003 Interpolation implementation I.mp4 |
26.13Мб |
| 003 MonteCarloIntegral2.py |
676б |
| 003 Monte-Carlo integral implementation II_en.vtt |
5.00Кб |
| 003 Monte-Carlo integral implementation II.mp4 |
24.52Мб |
| 003 Positional arguments and keyword arguments_en.vtt |
11.69Кб |
| 003 Positional arguments and keyword arguments.mp4 |
46.11Мб |
| 003 RectangleIntegral.py |
376б |
| 003 Rectangle method implementation_en.vtt |
6.82Кб |
| 003 Rectangle method implementation.mp4 |
22.38Мб |
| 003 Rounding errors_en.vtt |
4.65Кб |
| 003 Rounding errors.mp4 |
14.12Мб |
| 003 Running time analysis of matrix multiplication_en.vtt |
5.56Кб |
| 003 Running time analysis of matrix multiplication.mp4 |
27.04Мб |
| 003 Series_en.vtt |
8.16Кб |
| 003 Series.mp4 |
26.94Мб |
| 003 The original formula_en.vtt |
6.69Кб |
| 003 The original formula.mp4 |
17.78Мб |
| 003 Using the constructor_en.vtt |
6.79Кб |
| 003 Using the constructor.mp4 |
33.59Мб |
| 003 What are array data structures I_en.vtt |
8.05Кб |
| 003 What are array data structures I.mp4 |
24.99Мб |
| 003 What is pivoting_en.vtt |
7.25Кб |
| 003 What is pivoting.mp4 |
19.76Мб |
| 004 Applications of Monte-Carlo simulations in finance_en.vtt |
3.29Кб |
| 004 Applications of Monte-Carlo simulations in finance.mp4 |
14.29Мб |
| 004 Class variables and instance variables_en.vtt |
4.91Кб |
| 004 Class variables and instance variables.mp4 |
31.01Мб |
| 004 DataFrames_en.vtt |
6.08Кб |
| 004 DataFrames.mp4 |
18.45Мб |
| 004 Euler's method example - pendulum_en.vtt |
12.94Кб |
| 004 Euler's method example - pendulum.mp4 |
34.00Мб |
| 004 EulerMethodExample2.py |
421б |
| 004 Gaussian elimination and singular matrixes_en.vtt |
4.01Кб |
| 004 Gaussian elimination and singular matrixes.mp4 |
8.69Мб |
| 004 Indexes and slicing_en.vtt |
9.38Кб |
| 004 Indexes and slicing.mp4 |
31.50Мб |
| 004 Interpolation implementation II_en.vtt |
5.50Кб |
| 004 Interpolation implementation II.mp4 |
30.85Мб |
| 004 LagrangeInterpolation.py |
2.12Кб |
| 004 Mathematical formulation of eigenvectors.html |
261б |
| 004 Matrix vector multiplication_en.vtt |
4.65Кб |
| 004 Matrix vector multiplication.mp4 |
13.41Мб |
| 004 MatrixVectorMultiplication.py |
423б |
| 004 Newton method introduction_en.vtt |
5.28Кб |
| 004 Newton method introduction.mp4 |
16.40Мб |
| 004 PageRank algorithm example_en.vtt |
11.20Кб |
| 004 PageRank algorithm example.mp4 |
24.94Мб |
| 004 Returning values_en.vtt |
2.71Кб |
| 004 Returning values.mp4 |
8.14Мб |
| 004 Speed consideration - C, Java and Python_en.vtt |
7.64Кб |
| 004 Speed consideration - C, Java and Python.mp4 |
27.51Мб |
| 004 Stochastic gradient descent introduction_en.vtt |
11.84Кб |
| 004 Stochastic gradient descent introduction.mp4 |
36.45Мб |
| 004 Strings_en.vtt |
8.77Кб |
| 004 Strings.mp4 |
28.16Мб |
| 004 Trapezoidal integral introduction_en.vtt |
7.74Кб |
| 004 Trapezoidal integral introduction.mp4 |
19.06Мб |
| 004 What are array data structures II_en.vtt |
8.60Кб |
| 004 What are array data structures II.mp4 |
24.95Мб |
| 005 Applications of interpolation_en.vtt |
2.90Кб |
| 005 Applications of interpolation.mp4 |
8.61Мб |
| 005 DataFrame operations_en.vtt |
10.85Кб |
| 005 DataFrame operations.mp4 |
41.30Мб |
| 005 Euler's method example - pendulum with drag_en.vtt |
4.67Кб |
| 005 Euler's method example - pendulum with drag.mp4 |
16.15Мб |
| 005 Inner product_en.vtt |
4.86Кб |
| 005 Inner product.mp4 |
16.56Мб |
| 005 InnerProduct.py |
386б |
| 005 Lists in Python_en.vtt |
6.42Кб |
| 005 Lists in Python.mp4 |
21.74Мб |
| 005 Mathematical formulation of Gaussian elimination.html |
345б |
| 005 Matrix representation of the problem_en.vtt |
9.77Кб |
| 005 Matrix representation of the problem.mp4 |
29.07Мб |
| 005 Newton method implementation_en.vtt |
4.70Кб |
| 005 Newton method implementation.mp4 |
15.79Мб |
| 005 NewtonRaphsonMethod.py |
294б |
| 005 Private variables and name mangling_en.vtt |
5.08Кб |
| 005 Private variables and name mangling.mp4 |
19.13Мб |
| 005 Returning multiple values_en.vtt |
3.37Кб |
| 005 Returning multiple values.mp4 |
12.17Мб |
| 005 StochasticGradientDescent.py |
1.93Кб |
| 005 Stochastic gradient descent implementation I_en.vtt |
24.44Кб |
| 005 Stochastic gradient descent implementation I.mp4 |
105.86Мб |
| 005 String slicing_en.vtt |
7.44Кб |
| 005 String slicing.mp4 |
25.08Мб |
| 005 TrapezoidalIntegral.py |
468б |
| 005 Trapezoidal integral implementation_en.vtt |
5.33Кб |
| 005 Trapezoidal integral implementation.mp4 |
17.61Мб |
| 005 Types_en.vtt |
4.95Кб |
| 005 Types.mp4 |
19.29Мб |
| 006 Lists and NumPy arrays_en.vtt |
5.14Кб |
| 006 Lists and NumPy arrays.mp4 |
19.40Мб |
| 006 Lists in Python - advanced operations_en.vtt |
8.71Кб |
| 006 Lists in Python - advanced operations.mp4 |
39.32Мб |
| 006 Mathematical formulation of interpolation.html |
265б |
| 006 Mathematical formulation of root finding.html |
271б |
| 006 Reshape_en.vtt |
8.85Кб |
| 006 Reshape.mp4 |
33.84Мб |
| 006 Runge-Kutta method introduction_en.vtt |
5.03Кб |
| 006 Runge-Kutta method introduction.mp4 |
14.24Мб |
| 006 Simpson's method introduction_en.vtt |
5.64Кб |
| 006 Simpson's method introduction.mp4 |
11.99Мб |
| 006 Speed comparison - DataFrame operations_en.vtt |
4.91Кб |
| 006 Speed comparison - DataFrame operations.mp4 |
28.65Мб |
| 006 Stochastic gradient descent implementation II_en.vtt |
6.37Кб |
| 006 Stochastic gradient descent implementation II.mp4 |
41.23Мб |
| 006 StochasticGradientDescentRegression.py |
2.17Кб |
| 006 The random surfer model_en.vtt |
5.66Кб |
| 006 The random surfer model.mp4 |
18.81Мб |
| 006 Type casting_en.vtt |
4.66Кб |
| 006 Type casting.mp4 |
16.74Мб |
| 006 What is inheritance in OOP_en.vtt |
4.14Кб |
| 006 What is inheritance in OOP.mp4 |
18.03Мб |
| 006 Yield operator_en.vtt |
5.77Кб |
| 006 Yield operator.mp4 |
18.28Мб |
| 007 Lists in Python - list comprehension_en.vtt |
6.12Кб |
| 007 Lists in Python - list comprehension.mp4 |
22.88Мб |
| 007 Matrix operations with NumPy_en.vtt |
4.54Кб |
| 007 Matrix operations with NumPy.mp4 |
13.69Мб |
| 007 Operators_en.vtt |
5.88Кб |
| 007 Operators.mp4 |
20.63Мб |
| 007 Reading CSV and text files_en.vtt |
6.45Кб |
| 007 Reading CSV and text files.mp4 |
35.23Мб |
| 007 RungeKuttaExample1.py |
648б |
| 007 Runge-Kutta method example I_en.vtt |
6.92Кб |
| 007 Runge-Kutta method example I.mp4 |
24.49Мб |
| 007 Simpson's method implementation_en.vtt |
5.99Кб |
| 007 Simpson's method implementation.mp4 |
20.56Мб |
| 007 SimpsonMethod.py |
511б |
| 007 Stacking and merging arrays_en.vtt |
7.34Кб |
| 007 Stacking and merging arrays.mp4 |
27.69Мб |
| 007 The super keyword_en.vtt |
4.91Кб |
| 007 The super keyword.mp4 |
21.23Мб |
| 007 What are the most relevant built-in functions_en.vtt |
4.92Кб |
| 007 What are the most relevant built-in functions.mp4 |
15.38Мб |
| 007 What are the problems with the random surfer model_en.vtt |
4.08Кб |
| 007 What are the problems with the random surfer model.mp4 |
12.26Мб |
| 007 What is ADAGrad_en.vtt |
7.54Кб |
| 007 What is ADAGrad.mp4 |
22.13Мб |
| 008 (!!!) Python lists and arrays.html |
628б |
| 008 ADAGrad implementation_en.vtt |
13.63Кб |
| 008 ADAGrad implementation.mp4 |
63.99Мб |
| 008 Conditional statements_en.vtt |
4.63Кб |
| 008 Conditional statements.mp4 |
17.81Мб |
| 008 Filter_en.vtt |
4.22Кб |
| 008 Filter.mp4 |
15.24Мб |
| 008 Function (method) override_en.vtt |
2.71Кб |
| 008 Function (method) override.mp4 |
18.17Мб |
| 008 GradientDescentAdaGrad.py |
1.56Кб |
| 008 Mathematical formulation of numerical integration.html |
245б |
| 008 Operations_en.vtt |
6.61Кб |
| 008 Operations.mp4 |
32.93Мб |
| 008 PageRank algorithm - the final formula_en.vtt |
8.93Кб |
| 008 PageRank algorithm - the final formula.mp4 |
37.74Мб |
| 008 RungeKuttaExample2.py |
663б |
| 008 Runge-Kutta method example II_en.vtt |
4.88Кб |
| 008 Runge-Kutta method example II.mp4 |
22.20Мб |
| 008 What is recursion_en.vtt |
10.64Кб |
| 008 What is recursion.mp4 |
35.28Мб |
| 009 Data filtering_en.vtt |
8.67Кб |
| 009 Data filtering.mp4 |
27.06Мб |
| 009 How to use multiple conditions_en.vtt |
9.05Кб |
| 009 How to use multiple conditions.mp4 |
31.41Мб |
| 009 Local vs global variables_en.vtt |
4.77Кб |
| 009 Local vs global variables.mp4 |
15.01Мб |
| 009 Mathematical formulation of numerical differentiation.html |
251б |
| 009 Measuring running time of lists.html |
1.24Кб |
| 009 Power method_en.vtt |
6.30Кб |
| 009 Power method.mp4 |
21.32Мб |
| 009 Running time comparison arrays and lists.html |
1.34Кб |
| 009 What is polymorphism_en.vtt |
5.25Кб |
| 009 What is polymorphism.mp4 |
21.16Мб |
| 009 What is RMSProp_en.vtt |
4.31Кб |
| 009 What is RMSProp.mp4 |
19.12Мб |
| 010 ADAM optimizer introduction_en.vtt |
5.23Кб |
| 010 ADAM optimizer introduction.mp4 |
12.31Мб |
| 010 Logical operators_en.vtt |
3.97Кб |
| 010 Logical operators.mp4 |
17.60Мб |
| 010 Original scientific paper of PageRank algorithm.html |
254б |
| 010 Polymorphism and abstraction example_en.vtt |
6.03Кб |
| 010 Polymorphism and abstraction example.mp4 |
33.27Мб |
| 010 The __main__ function_en.vtt |
4.02Кб |
| 010 The __main__ function.mp4 |
14.81Мб |
| 010 Using the apply() function_en.vtt |
7.95Кб |
| 010 Using the apply() function.mp4 |
30.85Мб |
| 010 What are tuples_en.vtt |
4.32Кб |
| 010 What are tuples.mp4 |
14.73Мб |
| 011 ADAM.py |
1.07Кб |
| 011 ADAM optimizer implementation_en.vtt |
10.18Кб |
| 011 ADAM optimizer implementation.mp4 |
43.02Мб |
| 011 Loops - for loop_en.vtt |
6.92Кб |
| 011 Loops - for loop.mp4 |
18.95Мб |
| 011 Modules_en.vtt |
6.71Кб |
| 011 Modules.mp4 |
21.79Мб |
| 011 Mutability and immutability_en.vtt |
5.25Кб |
| 011 Mutability and immutability.mp4 |
18.49Мб |
| 011 Speed comparison - loops and apply()_en.vtt |
2.97Кб |
| 011 Speed comparison - loops and apply().mp4 |
17.77Мб |
| 012 Loops - while loop_en.vtt |
4.88Кб |
| 012 Loops - while loop.mp4 |
14.41Мб |
| 012 Mathematical formulation of optimization algorithms in machine learning.html |
275б |
| 012 The __str__ function_en.vtt |
3.51Кб |
| 012 The __str__ function.mp4 |
15.67Мб |
| 012 What are linked list data structures_en.vtt |
10.30Кб |
| 012 What are linked list data structures.mp4 |
34.49Мб |
| 012 What is vectorization_en.vtt |
7.52Кб |
| 012 What is vectorization.mp4 |
26.44Мб |
| 013 Comparing objects - overriding functions_en.vtt |
9.05Кб |
| 013 Comparing objects - overriding functions.mp4 |
40.18Мб |
| 013 Doubly linked list implementation in Python_en.vtt |
6.19Кб |
| 013 Doubly linked list implementation in Python.mp4 |
24.46Мб |
| 013 Vectorization example I_en.vtt |
5.80Кб |
| 013 Vectorization example I.mp4 |
22.18Мб |
| 013 What are nested loops_en.vtt |
3.03Кб |
| 013 What are nested loops.mp4 |
13.16Мб |
| 014 Enumerate_en.vtt |
4.34Кб |
| 014 Enumerate.mp4 |
15.13Мб |
| 014 Hashing and O(1) running time complexity_en.vtt |
9.73Кб |
| 014 Hashing and O(1) running time complexity.mp4 |
30.98Мб |
| 014 Vectorization example II_en.vtt |
4.01Кб |
| 014 Vectorization example II.mp4 |
20.81Мб |
| 015 Break and continue_en.vtt |
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| 015 Break and continue.mp4 |
20.42Мб |
| 015 Dictionaries in Python_en.vtt |
10.69Кб |
| 015 Dictionaries in Python.mp4 |
38.50Мб |
| 016 Calculating Fibonacci-numbers_en.vtt |
2.89Кб |
| 016 Calculating Fibonacci-numbers.mp4 |
8.39Мб |
| 016 Sets in Python_en.vtt |
9.56Кб |
| 016 Sets in Python.mp4 |
47.14Мб |
| 017 Sorting_en.vtt |
11.36Кб |
| 017 Sorting.mp4 |
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758.07Кб |
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333.49Кб |
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736.68Кб |
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439.31Кб |
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791.46Кб |
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519.93Кб |
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1.37Кб |
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165.90Кб |
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249.93Кб |
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415.81Кб |
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745.27Кб |
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66.72Кб |
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507.74Кб |
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606.87Кб |
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467.51Кб |
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1015.52Кб |
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19.76Кб |
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150.11Кб |
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157.08Кб |
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908.71Кб |
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436.03Кб |
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957.38Кб |
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357.73Кб |
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863.20Кб |
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203.33Кб |
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876.14Кб |
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322.18Кб |
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503.59Кб |
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965.72Кб |
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983.40Кб |
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60.21Кб |
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572.82Кб |
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895.28Кб |
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121.95Кб |
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585.57Кб |
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946.66Кб |
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909.18Кб |
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966.75Кб |
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10.02Кб |
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48.42Кб |
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57.50Кб |
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491.74Кб |
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509.96Кб |
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523.84Кб |
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530.85Кб |
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555.95Кб |
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897.20Кб |
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1002.90Кб |
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119.26Кб |
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636.26Кб |
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815.62Кб |
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839.65Кб |
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892.19Кб |
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216.54Кб |
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270.71Кб |
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698.37Кб |
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786.84Кб |
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856.26Кб |
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714.47Кб |
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190.86Кб |
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381.88Кб |
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439.97Кб |
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450.23Кб |
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588.91Кб |
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1004.94Кб |
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247.27Кб |
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360.47Кб |
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609.89Кб |
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731.14Кб |
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792.56Кб |
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888.75Кб |
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902.11Кб |
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966.77Кб |
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53.55Кб |
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153.19Кб |
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193.41Кб |
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517.95Кб |
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564.75Кб |
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738.38Кб |
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851.35Кб |
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842.42Кб |
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971.07Кб |
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992.17Кб |
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195.45Кб |
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227.69Кб |
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237.17Кб |
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395.27Кб |
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411.36Кб |
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725.74Кб |
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844.08Кб |
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187.01Кб |
| TutsNode.com.txt |
63б |