Learn Artificial Neural Network From Scratch in Python

1. 02 Optional but Recommended [Learn Python in Easy Way]/020 Classes and Objects in Python.mp4276.69MB
2. 01 Introduction/001 Introduction.mp424.27MB
3. 01 Introduction/002 Install anaconda on your machine.mp469.08MB
4. 01 Introduction/003 Set up environment and Download Machine Learning Libraries.mp480.51MB
5. 01 Introduction/004 Introduction to Jupyter Notebook.mp4111.41MB
6. 01 Introduction/005 Introduction to Artificial Intelligence and Machine Learning [lecture].mp4110.5MB
7. 02 Optional but Recommended [Learn Python in Easy Way]/001 Download and setup Pycharm code editor on Windows.mp455.76MB
8. 02 Optional but Recommended [Learn Python in Easy Way]/002 Download Visual Studio code editor on Windows (Optional).mp434.88MB
9. 02 Optional but Recommended [Learn Python in Easy Way]/003 Download and setup Pycharm code editon on Linux.mp457.24MB
10. 02 Optional but Recommended [Learn Python in Easy Way]/004 How to read Python documentation.mp457.57MB
11. 02 Optional but Recommended [Learn Python in Easy Way]/005 Variables on Python.mp457.83MB
12. 02 Optional but Recommended [Learn Python in Easy Way]/006 Data Types_ String, Set and Numbers.mp486.04MB
13. 02 Optional but Recommended [Learn Python in Easy Way]/007 Data Types_ List, Dictionaty and Tuple.mp467.53MB
14. 02 Optional but Recommended [Learn Python in Easy Way]/008 Operators and Operands.mp4104.45MB
15. 02 Optional but Recommended [Learn Python in Easy Way]/009 Logical Operators and Operations.mp453.06MB
16. 02 Optional but Recommended [Learn Python in Easy Way]/010 Comments and User Input.mp466.04MB
17. 02 Optional but Recommended [Learn Python in Easy Way]/011 Built-in Modules and Creating your own Modules.mp4117.16MB
18. 02 Optional but Recommended [Learn Python in Easy Way]/012 Python _List_ Data Structures.mp4194.26MB
19. 02 Optional but Recommended [Learn Python in Easy Way]/013 Python _Dictionary_ Data Structures.mp462.57MB
20. 02 Optional but Recommended [Learn Python in Easy Way]/014 Python Indentation.mp441.15MB
21. 02 Optional but Recommended [Learn Python in Easy Way]/015 Python Conditionals_ if...else statements.mp449.58MB
22. 02 Optional but Recommended [Learn Python in Easy Way]/016 Looping in Python_ while Loops.mp431.4MB
23. 02 Optional but Recommended [Learn Python in Easy Way]/017 Looping in Python_ for Loops.mp478.55MB
24. 02 Optional but Recommended [Learn Python in Easy Way]/018 User Defined Functions in Python.mp4130.34MB
25. 02 Optional but Recommended [Learn Python in Easy Way]/019 Default Arguments in Python.mp433.04MB
26. 02 Optional but Recommended [Learn Python in Easy Way]/021 Basic Inheritance in Python.mp4113.84MB
27. 02 Optional but Recommended [Learn Python in Easy Way]/022 Multiple Inheritance in Python.mp447.46MB
28. 02 Optional but Recommended [Learn Python in Easy Way]/023 __name__ == __main__.mp442.43MB
29. 03 Prerequisite_ ML libraries for data preprocessing/001 Data Types in Machine Learning.mp431.6MB
30. 03 Prerequisite_ ML libraries for data preprocessing/002 Data Preprocessing Part 1.mp4229.04MB
31. 03 Prerequisite_ ML libraries for data preprocessing/003 Data Preprocessing Part 2.mp4154.97MB
32. 03 Prerequisite_ ML libraries for data preprocessing/004 Data Preprocessing Part 3.mp4117.85MB
33. 03 Prerequisite_ ML libraries for data preprocessing/005 Introduction to numpy module.mp471.94MB
34. 03 Prerequisite_ ML libraries for data preprocessing/006 Introduction to pandas module.mp4164.14MB
35. 03 Prerequisite_ ML libraries for data preprocessing/007 Train and Test Splitting of Data.mp489.48MB
36. 03 Prerequisite_ ML libraries for data preprocessing/008 Encoding Process in Machine Learning.mp468.02MB
37. 03 Prerequisite_ ML libraries for data preprocessing/009 Introduction to overfit and underfit of model.mp4141.02MB
38. 03 Prerequisite_ ML libraries for data preprocessing/010 Cross entropy of Logistic Regression.mp4157.88MB
39. 04 Lecture_ Introduction to neural networks --Mandatory (Don't miss out)/001 Introduction to Artificial Intelligence.mp468.29MB
40. 04 Lecture_ Introduction to neural networks --Mandatory (Don't miss out)/002 Introduction to Neural Networks.mp4116.39MB
41. 04 Lecture_ Introduction to neural networks --Mandatory (Don't miss out)/003 Inspiration and representation for Neural Network.mp477.08MB
42. 04 Lecture_ Introduction to neural networks --Mandatory (Don't miss out)/004 History and Application of Neural Network.mp469.51MB
43. 04 Lecture_ Introduction to neural networks --Mandatory (Don't miss out)/005 Example of neural network.mp449.37MB
44. 04 Lecture_ Introduction to neural networks --Mandatory (Don't miss out)/006 Updating the weights [partial differentiation].mp492.48MB
45. 04 Lecture_ Introduction to neural networks --Mandatory (Don't miss out)/007 Introduction to partial differentiation.mp456.08MB
46. 04 Lecture_ Introduction to neural networks --Mandatory (Don't miss out)/008 Introduction to the Activation Function.mp4105.58MB
47. 04 Lecture_ Introduction to neural networks --Mandatory (Don't miss out)/009 Why do we need bias in the program.mp444.79MB
48. 04 Lecture_ Introduction to neural networks --Mandatory (Don't miss out)/010 Why we use regularization in the Neural Network.mp463.1MB
49. 04 Lecture_ Introduction to neural networks --Mandatory (Don't miss out)/011 Introduction to the gradient descent [review].mp460.52MB
50. 04 Lecture_ Introduction to neural networks --Mandatory (Don't miss out)/012 Introduction to Stochastic Gradient Descent and Adam Optimizer.mp477.98MB
51. 04 Lecture_ Introduction to neural networks --Mandatory (Don't miss out)/013 Introduction to mini-batch SGD.mp416.2MB
52. 05 Tutorial_ Numerical on Backpropagation/001 Derivative of sigmoid function [must watch].mp469.53MB
53. 05 Tutorial_ Numerical on Backpropagation/002 Introduction to the problem.mp445.3MB
54. 05 Tutorial_ Numerical on Backpropagation/003 Forward Propagation of Artificial Neural Network.mp4127.96MB
55. 05 Tutorial_ Numerical on Backpropagation/004 Error in the problem.mp475.73MB
56. 05 Tutorial_ Numerical on Backpropagation/005 Backpropagation in ANN.mp4164.81MB
57. 06 Workshop_ Coding Artificial Neural Network from Scratch/001 Setting up environment and coding single neuron.mp468.8MB
58. 06 Workshop_ Coding Artificial Neural Network from Scratch/002 Coding neuron layer.mp494.62MB
59. 06 Workshop_ Coding Artificial Neural Network from Scratch/003 Using dot product to code neuron layer.mp449.09MB
60. 06 Workshop_ Coding Artificial Neural Network from Scratch/004 Coding dense layer [must know Object Oriented Programming].mp4121.3MB
61. 06 Workshop_ Coding Artificial Neural Network from Scratch/005 Introduction to Activation Function.mp4104.95MB
62. 06 Workshop_ Coding Artificial Neural Network from Scratch/006 Implementation of activation function [step and sigmoid].mp469.3MB
63. 06 Workshop_ Coding Artificial Neural Network from Scratch/007 Implementation of activation function [tanh and ReLu].mp462.05MB
64. 07 Workshop_ Coding Multi Layer Perception (MLP) Classifier/001 Creating data sets on our own!!.mp4156.63MB
65. 07 Workshop_ Coding Multi Layer Perception (MLP) Classifier/002 Implementation of MLP classifier using scikit-learn.mp4166.09MB
66. 07 Workshop_ Coding Multi Layer Perception (MLP) Classifier/003 Evaluation of the model (Neural Network).mp461.42MB
67. 07 Workshop_ Coding Multi Layer Perception (MLP) Classifier/004 Experimentation of hyper parameters.mp485.95MB
68. 08 Explore more_ Computational Neural Network [advanced]/001 Introduction to feed forward and backward propagation in computational graph.mp4132.76MB