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种子名称： Learn Artificial Neural Network From Scratch in Python
文件类型： 视频
文件数目： 68个文件
文件大小： 5.89 GB
收录时间： 2021-10-27 03:51
已经下载： 3次
资源热度： 222
最近下载： 2024-12-27 19:02

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Learn Artificial Neural Network From Scratch in Python.torrent

02 Optional but Recommended [Learn Python in Easy Way]/020 Classes and Objects in Python.mp4276.69MB

01 Introduction/001 Introduction.mp424.27MB

01 Introduction/002 Install anaconda on your machine.mp469.08MB

01 Introduction/003 Set up environment and Download Machine Learning Libraries.mp480.51MB

01 Introduction/004 Introduction to Jupyter Notebook.mp4111.41MB

01 Introduction/005 Introduction to Artificial Intelligence and Machine Learning [lecture].mp4110.5MB

02 Optional but Recommended [Learn Python in Easy Way]/001 Download and setup Pycharm code editor on Windows.mp455.76MB

02 Optional but Recommended [Learn Python in Easy Way]/002 Download Visual Studio code editor on Windows (Optional).mp434.88MB

02 Optional but Recommended [Learn Python in Easy Way]/003 Download and setup Pycharm code editon on Linux.mp457.24MB

02 Optional but Recommended [Learn Python in Easy Way]/004 How to read Python documentation.mp457.57MB

02 Optional but Recommended [Learn Python in Easy Way]/005 Variables on Python.mp457.83MB

02 Optional but Recommended [Learn Python in Easy Way]/006 Data Types_ String, Set and Numbers.mp486.04MB

02 Optional but Recommended [Learn Python in Easy Way]/007 Data Types_ List, Dictionaty and Tuple.mp467.53MB

02 Optional but Recommended [Learn Python in Easy Way]/008 Operators and Operands.mp4104.45MB

02 Optional but Recommended [Learn Python in Easy Way]/009 Logical Operators and Operations.mp453.06MB

02 Optional but Recommended [Learn Python in Easy Way]/010 Comments and User Input.mp466.04MB

02 Optional but Recommended [Learn Python in Easy Way]/011 Built-in Modules and Creating your own Modules.mp4117.16MB

02 Optional but Recommended [Learn Python in Easy Way]/012 Python _List_ Data Structures.mp4194.26MB

02 Optional but Recommended [Learn Python in Easy Way]/013 Python _Dictionary_ Data Structures.mp462.57MB

02 Optional but Recommended [Learn Python in Easy Way]/014 Python Indentation.mp441.15MB

02 Optional but Recommended [Learn Python in Easy Way]/015 Python Conditionals_ if...else statements.mp449.58MB

02 Optional but Recommended [Learn Python in Easy Way]/016 Looping in Python_ while Loops.mp431.4MB

02 Optional but Recommended [Learn Python in Easy Way]/017 Looping in Python_ for Loops.mp478.55MB

02 Optional but Recommended [Learn Python in Easy Way]/018 User Defined Functions in Python.mp4130.34MB

02 Optional but Recommended [Learn Python in Easy Way]/019 Default Arguments in Python.mp433.04MB

02 Optional but Recommended [Learn Python in Easy Way]/021 Basic Inheritance in Python.mp4113.84MB

02 Optional but Recommended [Learn Python in Easy Way]/022 Multiple Inheritance in Python.mp447.46MB

02 Optional but Recommended [Learn Python in Easy Way]/023 __name__ == __main__.mp442.43MB

03 Prerequisite_ ML libraries for data preprocessing/001 Data Types in Machine Learning.mp431.6MB

03 Prerequisite_ ML libraries for data preprocessing/002 Data Preprocessing Part 1.mp4229.04MB

03 Prerequisite_ ML libraries for data preprocessing/003 Data Preprocessing Part 2.mp4154.97MB

03 Prerequisite_ ML libraries for data preprocessing/004 Data Preprocessing Part 3.mp4117.85MB

03 Prerequisite_ ML libraries for data preprocessing/005 Introduction to numpy module.mp471.94MB

03 Prerequisite_ ML libraries for data preprocessing/006 Introduction to pandas module.mp4164.14MB

03 Prerequisite_ ML libraries for data preprocessing/007 Train and Test Splitting of Data.mp489.48MB

03 Prerequisite_ ML libraries for data preprocessing/008 Encoding Process in Machine Learning.mp468.02MB

03 Prerequisite_ ML libraries for data preprocessing/009 Introduction to overfit and underfit of model.mp4141.02MB

03 Prerequisite_ ML libraries for data preprocessing/010 Cross entropy of Logistic Regression.mp4157.88MB

04 Lecture_ Introduction to neural networks --Mandatory (Don't miss out)/001 Introduction to Artificial Intelligence.mp468.29MB

04 Lecture_ Introduction to neural networks --Mandatory (Don't miss out)/002 Introduction to Neural Networks.mp4116.39MB

04 Lecture_ Introduction to neural networks --Mandatory (Don't miss out)/003 Inspiration and representation for Neural Network.mp477.08MB

04 Lecture_ Introduction to neural networks --Mandatory (Don't miss out)/004 History and Application of Neural Network.mp469.51MB

04 Lecture_ Introduction to neural networks --Mandatory (Don't miss out)/005 Example of neural network.mp449.37MB

04 Lecture_ Introduction to neural networks --Mandatory (Don't miss out)/006 Updating the weights [partial differentiation].mp492.48MB

04 Lecture_ Introduction to neural networks --Mandatory (Don't miss out)/007 Introduction to partial differentiation.mp456.08MB

04 Lecture_ Introduction to neural networks --Mandatory (Don't miss out)/008 Introduction to the Activation Function.mp4105.58MB

04 Lecture_ Introduction to neural networks --Mandatory (Don't miss out)/009 Why do we need bias in the program.mp444.79MB

04 Lecture_ Introduction to neural networks --Mandatory (Don't miss out)/010 Why we use regularization in the Neural Network.mp463.1MB

04 Lecture_ Introduction to neural networks --Mandatory (Don't miss out)/011 Introduction to the gradient descent [review].mp460.52MB

04 Lecture_ Introduction to neural networks --Mandatory (Don't miss out)/012 Introduction to Stochastic Gradient Descent and Adam Optimizer.mp477.98MB

04 Lecture_ Introduction to neural networks --Mandatory (Don't miss out)/013 Introduction to mini-batch SGD.mp416.2MB

05 Tutorial_ Numerical on Backpropagation/001 Derivative of sigmoid function [must watch].mp469.53MB

05 Tutorial_ Numerical on Backpropagation/002 Introduction to the problem.mp445.3MB

05 Tutorial_ Numerical on Backpropagation/003 Forward Propagation of Artificial Neural Network.mp4127.96MB

05 Tutorial_ Numerical on Backpropagation/004 Error in the problem.mp475.73MB

05 Tutorial_ Numerical on Backpropagation/005 Backpropagation in ANN.mp4164.81MB

06 Workshop_ Coding Artificial Neural Network from Scratch/001 Setting up environment and coding single neuron.mp468.8MB

06 Workshop_ Coding Artificial Neural Network from Scratch/002 Coding neuron layer.mp494.62MB

06 Workshop_ Coding Artificial Neural Network from Scratch/003 Using dot product to code neuron layer.mp449.09MB

06 Workshop_ Coding Artificial Neural Network from Scratch/004 Coding dense layer [must know Object Oriented Programming].mp4121.3MB

06 Workshop_ Coding Artificial Neural Network from Scratch/005 Introduction to Activation Function.mp4104.95MB

06 Workshop_ Coding Artificial Neural Network from Scratch/006 Implementation of activation function [step and sigmoid].mp469.3MB

06 Workshop_ Coding Artificial Neural Network from Scratch/007 Implementation of activation function [tanh and ReLu].mp462.05MB

07 Workshop_ Coding Multi Layer Perception (MLP) Classifier/001 Creating data sets on our own!!.mp4156.63MB

07 Workshop_ Coding Multi Layer Perception (MLP) Classifier/002 Implementation of MLP classifier using scikit-learn.mp4166.09MB

07 Workshop_ Coding Multi Layer Perception (MLP) Classifier/003 Evaluation of the model (Neural Network).mp461.42MB

07 Workshop_ Coding Multi Layer Perception (MLP) Classifier/004 Experimentation of hyper parameters.mp485.95MB

08 Explore more_ Computational Neural Network [advanced]/001 Introduction to feed forward and backward propagation in computational graph.mp4132.76MB