Udemy Artificial Neural NetWorks ANN with Keras in Python and R

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3.72 GB | 00:20:16 | mp4 | 1920X1080  | 16:9
Genre:eLearning |Language:English

Files Included :
1 - Introduction  (18.41 MB)
37 - Python Dataset for classification problem  (75.73 MB)
38 - Python Normalization and TestTrain split  (65.45 MB)
39 - R Dataset Normalization and TestTrain set  (103.5 MB)
41 - Different ways to create ANN using Keras  (9.31 MB)
42 - Building the Neural Network using Keras  (118.1 MB)
43 - Compiling and Training the Neural Network model  (116.66 MB)
44 - Evaluating performance and Predicting using Keras  (98.49 MB)
45 - BuildingCompiling and Training  (105.98 MB)
46 - Evaluating and Predicting  (73.78 MB)
47 - Building Neural Network for Regression Problem  (279.96 MB)
48 - Using Functional API for complex architectures  (130.58 MB)
49 - Building Regression Model with Functional AP  (97.06 MB)
50 - Complex Architectures using Functional API  (125.97 MB)
51 - Saving Restoring Models and Using Callbacks  (251.89 MB)
52 - Saving Restoring Models and Using Callbacks  (287.59 MB)
53 - Hyperparameter Tuning  (56.16 MB)
54 - Hyperparameter Tuning  (56.13 MB)
55 - Testtrain split  (29.85 MB)
56 - Bias Variance tradeoff  (18.35 MB)
57 - Test train split in Python  (55.84 MB)
58 - Test train split in R  (108.52 MB)
59 - The final milestone  (8.36 MB)
10 - Lists Part 1  (11.23 MB)
11 - Lists Part 2  (13.29 MB)
12 - Tuples and Directories  (12.88 MB)
13 - Working with Numpy Library of Python  (52.79 MB)
14 - Working with Pandas Library of Python  (55.76 MB)
15 - Working with Seaborn Library of Python  (45.77 MB)
3 - Installing Python and Anaconda  (15.35 MB)
4 - This is a milestone  (26.14 MB)
5 - Opening Jupyter Notebook  (54.65 MB)
6 - Introduction to Jupyter part 1  (21.66 MB)
7 - Introduction to Jupyter part 2  (8.42 MB)
8 - Arithmetic operators in Python Python Basics  (10.41 MB)
9 - Strings in Python Python Basics  (81.08 MB)
17 - Integrating ChatGPT with Jupyter notebook  (52.42 MB)
18 - Installing R and R studio  (62.41 MB)
19 - Basics of R and R studio  (33.12 MB)
20 - Packages in R  (120.74 MB)
21 - Inputting data part 1 Inbuilt datasets of R  (37.55 MB)
22 - Inputting data part 2 Manual data entry  (25.45 MB)
23 - Inputting data part 3 Importing from CSV or Text files  (108.48 MB)
24 - Creating Barplots in R  (141.02 MB)
25 - Creating Histograms in R  (37.07 MB)
26 - Perceptron  (39.4 MB)
27 - Activation Functions  (24.54 MB)
28 - Python Creating Perceptron model  (129.03 MB)
29 - Basic Terminologies  (28.61 MB)
30 - Gradient Descent  (44.32 MB)
31 - Back Propagation  (84.48 MB)
32 - Some Important Concepts  (52.78 MB)
33 - Hyperparameters  (33.06 MB)
34 - Keras and Tensorflow  (11.05 MB)
35 - Installing Tensorflow and Keras in Python  (29.51 MB)
36 - Installing TensorFlow and Keras in R  (14.93 MB)

Screenshot

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https://rapidgator.net/file/b8589f3fec4afea8f41d3b757d7b29b0/Udemy_Artificial_Neural_Networks_ANN_with_Keras_in_Python_and_R.part1.rar
https://rapidgator.net/file/c50303d848f0fcf824cb07549dbab9bd/Udemy_Artificial_Neural_Networks_ANN_with_Keras_in_Python_and_R.part2.rar

https://turbobit.net/rpgnpszmiqb2/Udemy_Artificial_Neural_Networks_ANN_with_Keras_in_Python_and_R.part1.rar.html
https://turbobit.net/d3dd2r76hdy9/Udemy_Artificial_Neural_Networks_ANN_with_Keras_in_Python_and_R.part2.rar.html