Udemy - Algorithms and Data Structures in Python

Torrent letöltése
Méret: 2.39 GB 
Seederek: 45
Leecherek: 9
Hozzáadva: 2018-11-18 09:41:08
Kategória: Other > Other
Udemy - Algorithms and Data Structures in Python.torrent:
01 Introduction/001 Introduction.mp44.07 MiB
01 Introduction/002 Complexity theory.mp456.85 MiB
02 Setup/001 Installing LiClipse.mp411.77 MiB
03 Data Structures/001 Linked List introduction.mp435.21 MiB
03 Data Structures/002 Linked list implementation.mp460.7 MiB
03 Data Structures/003 Binary search tree introduction.mp431.76 MiB
03 Data Structures/004 Binary search tree implementation - Node.mp450.26 MiB
03 Data Structures/005 Binary Search Tree implementation final.mp431.74 MiB
03 Data Structures/006 AVL trees introduction.mp461.02 MiB
03 Data Structures/007 AVL tree implementation I.mp440.81 MiB
03 Data Structures/008 AVL tree implementation II.mp476.34 MiB
03 Data Structures/009 Heaps introduction.mp445.62 MiB
03 Data Structures/010 Heap implementation I.mp431.97 MiB
03 Data Structures/011 Heap implementation II - heapsort.mp437.81 MiB
03 Data Structures/012 Hashtable introduction.mp469.79 MiB
03 Data Structures/013 Ternary search trees introduction.mp444.46 MiB
03 Data Structures/014 Ternary search trees implementation I.mp433.25 MiB
03 Data Structures/015 Ternary search trees implementation II.mp421.22 MiB
04 Graph Algorithms/001 Graph theory.mp429.01 MiB
04 Graph Algorithms/002 Breadth-first search introduction.mp411.34 MiB
04 Graph Algorithms/003 Breadth-first search implementation.mp423.3 MiB
04 Graph Algorithms/004 Depth-first search introduction.mp49.02 MiB
04 Graph Algorithms/005 Depth-first search implementation.mp417.94 MiB
04 Graph Algorithms/006 Shortest path introduction.mp437.99 MiB
04 Graph Algorithms/007 Dijkstra algorithm.mp464.97 MiB
04 Graph Algorithms/008 Bellman-Ford algorithm introduction.mp413.05 MiB
04 Graph Algorithms/009 Bellman-Ford implementation.mp474.66 MiB
04 Graph Algorithms/010 Spanning trees introduction.mp436.42 MiB
04 Graph Algorithms/011 Kruskal algorithm I - The union find data structure.mp467.7 MiB
04 Graph Algorithms/012 Kruskal algorithm II.mp427.9 MiB
04 Graph Algorithms/013 Prims-Jarnik algorithm.mp445.2 MiB
05 Other Problems/001 Red-black trees.mp460.22 MiB
05 Other Problems/002 Splay trees.mp464.68 MiB
05 Other Problems/003 Coloring problem.mp458.73 MiB
05 Other Problems/004 Backtracking - N queens problem.mp429.48 MiB
05 Other Problems/005 Euler cycles - chinese postman problem.mp441.65 MiB
06 Basic Sorting Algorithms/001 Bubble sort.mp419.28 MiB
06 Basic Sorting Algorithms/002 Insertion sort.mp418.81 MiB
06 Basic Sorting Algorithms/003 Merge sort.mp417.86 MiB
06 Basic Sorting Algorithms/004 Quicksort.mp418.96 MiB
06 Basic Sorting Algorithms/005 Counting sort.mp418.74 MiB
06 Basic Sorting Algorithms/006 Radix sort.mp426.66 MiB
07 Machine Learning - Introduction/001 Introduction.mp422.06 MiB
08 Machine Learning - Regression/001 Linear regression introduction.mp423.26 MiB
08 Machine Learning - Regression/002 Linear regression example.mp421.57 MiB
08 Machine Learning - Regression/003 Logistic regression introduction.mp443.02 MiB
08 Machine Learning - Regression/004 Cross validation.mp419.95 MiB
08 Machine Learning - Regression/005 Logistic regression example - sigmoid function.mp446.61 MiB
08 Machine Learning - Regression/006 Logistic regression example II.mp416.21 MiB
08 Machine Learning - Regression/007 Logistic regression example III - credit scoring.mp451.42 MiB
09 Machine Learning - Classification/001 K-nearest neighbour introduction.mp437.32 MiB
09 Machine Learning - Classification/002 K-nearest neighbour introduction - normalize data.mp410.27 MiB
09 Machine Learning - Classification/003 K-nearest neighbour example I.mp48.62 MiB
09 Machine Learning - Classification/004 K-nearest neighbour example II.mp47.56 MiB
09 Machine Learning - Classification/005 Naive bayesian classifier introduction.mp445.74 MiB
09 Machine Learning - Classification/006 Naive bayesian example.mp420.78 MiB
09 Machine Learning - Classification/007 Support vector machine SVM introduction.mp419.53 MiB
09 Machine Learning - Classification/008 Support vector machine example I.mp410.85 MiB
09 Machine Learning - Classification/009 Support vector machine example II - image recognition.mp422.78 MiB
10 Machine Learning - Tree Based Algorithms/001 Decision trees introduction.mp431.76 MiB
10 Machine Learning - Tree Based Algorithms/002 Decision tree example I.mp45.82 MiB
10 Machine Learning - Tree Based Algorithms/003 Decision tree example II - iris data.mp449.55 MiB
10 Machine Learning - Tree Based Algorithms/004 Pruning and bagging.mp414.46 MiB
10 Machine Learning - Tree Based Algorithms/005 Random forest introduction.mp410.28 MiB
10 Machine Learning - Tree Based Algorithms/006 Boosting.mp412.18 MiB
10 Machine Learning - Tree Based Algorithms/007 Random forest example I.mp48.92 MiB
10 Machine Learning - Tree Based Algorithms/008 Random forest example II - enhance credit scoring.mp48.89 MiB
11 Machine Learning - Clustering/001 K-means clustering introduction.mp434.02 MiB
11 Machine Learning - Clustering/002 K-means clustering example.mp417.11 MiB
11 Machine Learning - Clustering/003 DBSCAN clustering introduction.mp425.57 MiB
11 Machine Learning - Clustering/004 Hierarchical clustering introduction.mp422.85 MiB
11 Machine Learning - Clustering/005 Hierarchical clustering example.mp413.58 MiB
12 Machine Learning - Neural Networks/001 Introduction.mp416.57 MiB
12 Machine Learning - Neural Networks/002 Simple example - logical AND table.mp430.06 MiB
12 Machine Learning - Neural Networks/003 Training a neural network.mp436.1 MiB
12 Machine Learning - Neural Networks/004 Complex neural networks.mp434.83 MiB
12 Machine Learning - Neural Networks/005 Applications of neural networks.mp424.97 MiB
12 Machine Learning - Neural Networks/006 Deep learning.mp411.23 MiB
12 Machine Learning - Neural Networks/007 Neural network example - XOR problem.mp425.12 MiB
13 Outro/001 Final words.mp46.21 MiB