| 001 Creating data sets on our own!!.mp4 | 156.6 MB | ||
| 001 Data Types in Machine Learning.mp4 | 31.6 MB | ||
| 001 Derivative of sigmoid function [must watch].mp4 | 69.5 MB | ||
| 001 Download and setup Pycharm code editor on Windows.mp4 | 55.8 MB | ||
| 001 Introduction to Artificial Intelligence.mp4 | 68.3 MB | ||
| 001 Introduction to feed forward and backward propagation in computational graph.mp4 | 132.8 MB | ||
| 001 Introduction.mp4 | 24.3 MB | ||
| 001 Setting up environment and coding single neuron.mp4 | 68.8 MB | ||
| 002 Coding neuron layer.mp4 | 94.6 MB | ||
| 002 Data Preprocessing Part 1.mp4 | 229 MB | ||
| 002 Download Visual Studio code editor on Windows (Optional).mp4 | 34.9 MB | ||
| 002 Implementation of MLP classifier using scikit-learn.mp4 | 166.1 MB | ||
| 002 Install anaconda on your machine.mp4 | 69.1 MB | ||
| 002 Introduction to Neural Networks.mp4 | 116.4 MB | ||
| 002 Introduction to the problem.mp4 | 45.3 MB | ||
| 003 Data Preprocessing Part 2.mp4 | 155 MB | ||
| 003 Download and setup Pycharm code editon on Linux.mp4 | 57.2 MB | ||
| 003 Evaluation of the model (Neural Network).mp4 | 61.4 MB | ||
| 003 Forward Propagation of Artificial Neural Network.mp4 | 128 MB | ||
| 003 Inspiration and representation for Neural Network.mp4 | 77.1 MB | ||
| 003 Set up environment and Download Machine Learning Libraries.mp4 | 80.5 MB | ||
| 003 Using dot product to code neuron layer.mp4 | 49.1 MB | ||
| 004 Coding dense layer [must know Object Oriented Programming].mp4 | 121.3 MB | ||
| 004 Data Preprocessing Part 3.mp4 | 117.9 MB | ||
| 004 Error in the problem.mp4 | 75.7 MB | ||
| 004 Experimentation of hyper parameters.mp4 | 86 MB | ||
| 004 History and Application of Neural Network.mp4 | 69.5 MB | ||
| 004 How to read Python documentation.mp4 | 57.6 MB | ||
| 004 Introduction to Jupyter Notebook.mp4 | 111.4 MB | ||
| 005 Backpropagation in ANN.mp4 | 164.8 MB | ||
| 005 Example of neural network.mp4 | 49.4 MB | ||
| 005 Introduction to Activation Function.mp4 | 104.9 MB | ||
| 005 Introduction to Artificial Intelligence and Machine Learning [lecture].mp4 | 110.5 MB | ||
| 005 Introduction to numpy module.mp4 | 71.9 MB | ||
| 005 Variables on Python.mp4 | 57.8 MB | ||
| 006 Data Types_ String, Set and Numbers.mp4 | 86 MB | ||
| 006 Implementation of activation function [step and sigmoid].mp4 | 69.3 MB | ||
| 006 Introduction to pandas module.mp4 | 164.1 MB | ||
| 006 Updating the weights [partial differentiation].mp4 | 92.5 MB | ||
| 007 Data Types_ List, Dictionaty and Tuple.mp4 | 67.5 MB | ||
| 007 Implementation of activation function [tanh and ReLu].mp4 | 62 MB | ||
| 007 Introduction to partial differentiation.mp4 | 56.1 MB | ||
| 007 Train and Test Splitting of Data.mp4 | 89.5 MB | ||
| 008 Encoding Process in Machine Learning.mp4 | 68 MB | ||
| 008 Introduction to the Activation Function.mp4 | 105.6 MB | ||
| 008 Operators and Operands.mp4 | 104.4 MB | ||
| 009 Introduction to overfit and underfit of model.mp4 | 141 MB | ||
| 009 Logical Operators and Operations.mp4 | 53.1 MB | ||
| 009 Why do we need bias in the program.mp4 | 44.8 MB | ||
| 010 Comments and User Input.mp4 | 66 MB | ||
| 010 Cross entropy of Logistic Regression.mp4 | 157.9 MB | ||
| 010 Why we use regularization in the Neural Network.mp4 | 63.1 MB | ||
| 011 Built-in Modules and Creating your own Modules.mp4 | 117.2 MB | ||
| 011 Introduction to the gradient descent [review].mp4 | 60.5 MB | ||
| 012 Introduction to Stochastic Gradient Descent and Adam Optimizer.mp4 | 78 MB | ||
| 012 Python _List_ Data Structures.mp4 | 194.3 MB | ||
| 013 Introduction to mini-batch SGD.mp4 | 16.2 MB | ||
| 013 Python _Dictionary_ Data Structures.mp4 | 62.6 MB | ||
| 014 Python Indentation.mp4 | 41.2 MB | ||
| 015 Python Conditionals_ if...else statements.mp4 | 49.6 MB | ||
| 016 Looping in Python_ while Loops.mp4 | 31.4 MB | ||
| 017 Looping in Python_ for Loops.mp4 | 78.5 MB | ||
| 018 User Defined Functions in Python.mp4 | 130.3 MB | ||
| 019 Default Arguments in Python.mp4 | 33 MB | ||
| 020 Classes and Objects in Python.mp4 | 276.7 MB | ||
| 021 Basic Inheritance in Python.mp4 | 113.8 MB | ||
| 022 Multiple Inheritance in Python.mp4 | 47.5 MB | ||
| 023 __name__ == __main__.mp4 | 42.4 MB | ||
| 038 Confusion Matrix for your Multi-Class ML Model.pdf | 267.6 KB | ||
| 052 A Step by Step Backpropagation.pdf | 3.9 MB | ||
| 053 A Step by Step Backpropagation.pdf | 3.9 MB | ||
| 054 A Step by Step Backpropagation.pdf | 3.9 MB | ||
| 056 A Step by Step Backpropagation.pdf | 3.9 MB | ||
| Activation function.ipynb | 71.6 KB | ||
| Artificial NN from scratch.ipynb | 9.2 KB | ||
| Downloaded from 1337x.html | 512 B | ||
| MLP workshop.ipynb | 1.4 MB | ||
| ▲ 84 total files | |||
Knowledge should not be limited to those who can afford it or those willing to pay for it.
If you found this course useful and are financially stable please consider supporting the creators by buying the course :)
Learn Artificial Neural Network From Scratch in Python
The MOST in-depth look at neural network theory, and how to code one with pure Python and Numpy
This course includes:
* 18 hours on-demand video
What you'll learn
* Code a neural network from scratch in Python and numpy
* Learn the math behind the neural networks
* Get a proper understanding of Artificial Neural Networks (ANN) and Deep Learning
* Derive the backpropagation rule from first principles
* Describe the various terms related to neural networks, such as "activation", "backpropagation" and "feedforward"
* Learn to evaluate the neural network models
Welcome to the course where we will learn about Artificial Neural Network (ANN) From Scratch!
If you're looking for a complete Course on Deep Learning using ANN that teaches you everything you need to create a Neural Network model in Python?
You've found the right Neural Network course!
After completing this course you will be able to:
Identify the business problem which can be solved using Neural network Models.
Have a clear understanding of Advanced Neural network concepts such as Gradient Descent, forward and Backward Propagation etc.
Create Neural network models in Python and ability to optimize the model tuning hyper parameters
Confidently practice, discuss and understand Deep Learning concepts
This course will get you started in building your FIRST artificial neural network using deep learning techniques. Following my previous course on logistic regression, we take this basic building block, and build full-on non-linear neural networks right out of the gate using Python and Numpy. All the materials for this course are FREE.
You should take this course if you are interested in starting your journey toward becoming a master at deep learning, or if you are interested in machine learning and data science in general. We go beyond basic models like logistic regression and linear regression and I show you something that automatically learns features.
What is covered in this course?
This course teaches you all the steps of creating a Neural network based model i.e. a Deep Learning model, to solve business problems.
Below are the course contents of this course on ANN:
Part 1 - Python basics
This part gets you started with Python and learn the brush up the basics like data structures, comprehensions, Object Oriented Programming and so on.
This part will help you set up the python and Jupyter environment on your system and it'll teach you how to perform some basic operations in Python. We will understand the importance of different libraries such as Numpy, Pandas, Seaborn and matplotlib libraries.
Part 2 - Theoretical Concepts
This part will give you a solid understanding of concepts involved in Neural Networks.
In this section you will learn about the neurons and how neurons are stacked to create a network architecture. Once architecture is set, we understand the Gradient descent algorithm to find the minima of a function and learn how this is used to optimize our network model.
Part 3 - Creating Regression and Classification ANN model in Python and R
In this part you will learn how to create ANN models in Python.
We will learn how to model the neural network in two ways: first we model it from scratch and after that using scikit-learn library.
Part 4 - Tutorial numerical examples on Backpropagation
One of the most important concept of ANN is backpropagation, so in order to apply the theory we learnt in lecture session in the real world neural networks, we are going to execute backpropagation taking one numerical example. We are going to take the help of partial differentiation and update the weights in backpropagation using gradient descent algorithms.
By the end of this course, your confidence in creating a Neural Network model in Python will soar. You'll have a thorough understanding of how to use ANN to create predictive models and solve business problems.
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| 1.3 GB | freecoursewb | 2 weeks | 0 | 0 | |
| 722.6 MB | freecoursewb | 2 weeks | 80 | 5 | |
| 1.8 GB | freecoursewb | 1 month | 9 | 3 | |
| 803.9 MB | freecoursewb | 1 month | 15 | 15 | |
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Udemy - Power BI Full Course - Learn Power BI from Scratch to Advance Posted by
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