Neural Network Python | How to make a Neural Network in Python | Python Tutorial | Edureka

This Edureka video is a part of the Python Tutorial series which will give you a detailed explanation of how neural networks work in Python. Modeled in accordance with the human brain, a Neural Network was built to mimic the functionality of a human brain. The human brain is a neural network made up of multiple neurons, similarly, an Artificial Neural Network (ANN) is made up of multiple perceptions. The concept of Neural Networks in Python are further covered in this Python Tutorial video through the following topics:
00:00 Introduction to Neural Networks in Python Tutorial
01:26 What is a Neural Network?
06:25 What are Layers and Weights?
08:27 What is an Activation Function?
10:48 Feedforward and Backpropagation
12:18 Training a Neural Network Using Python

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About the Machine Learning Master's Program

Machine Learning Engineer Masters Program has been curated after thorough research and recommendations from industry experts. It will help you master concepts of Neural Networks in depth along with other concepts like Python Programming, Artificial Intelligence, Machine learning, Deep Learning, NLP, Graphical Modelling and Reinforcement Learning along with hands-on experience of tools and systems used by the Industry experts. Edureka will be by your side throughout the learning journey - We’re Ridiculously Committed.

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Why learn Machine Learning with Python?

Edureka's Machine Learning Masters Program imparts you the necessary skills like data pre-processing, dimensional reduction, model evaluation and also exposes you to different machine learning algorithms like regression, clustering, decision trees, random forest, Naive Bayes and Q-Learning.
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What will you learn in Python Machine Learning Training?

Topics covered but not limited to will be: Python Programming, PySpark, HDFS, Spark SQL, Machine Learning Techniques and Artificial Intelligence Types, Tokenisation, Named Entity Recognition, Lemmatisation, Supervised Algorithms, Unsupervised Algorithms, Tensor Flow, Deep learning, Keras, Neural Networks, Bayesian and Markov’s Models, Inference, Decision Making, Bandit Algorithms, Bellman Equation, Policy Gradient Methods.

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Edureka’s Python Machine Learning Certification Course is a good fit for the below professionals:
Developers aspiring to be a ‘Machine Learning Engineer'
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'Python' professionals who want to design automatic predictive models
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[commentatorJR]

numerical analysis class got me started, kept interested in pygame, i want to make a good AI
for my self made video games

[pulkitmathur]

Hey! Nice video
So had a query: can we predict for missing input values here? for example i want the output for [2, 4], so is it possible?

[victorthecat]

You really know how to explain your stuff, great video!

[mohammedkhaleel]

I love edurekha it is best website for code learning

[medashireesha]

Can you explain identifying fake profiles use of artificial neural networks with python?

[mahimagomladu]

I am doing my project in computational neuroscience and I have to make the cerebellar network for that. I made neuron cells code in python like Purkinje cell, Golgi cell. Can you please help me how should I start connecting the neuron cells or making the layers like granular layer and molecular layer in cerebellum?

[TheNazbul]

Suppose i want a classification model and my classes are very identical to each other so to get accurate result should I use more hidden layers?

[Data_In_real_world]

hello sir i want to calculate weights and bais for last hidden layer which can be used for calculation of output layer please help me

[indointanchannel]

Thanks very much. your information enrich my knowledge significantly.

[ZEA_TATA]

It was a nice content as usual. If possible, can you guys make videos on Julia language as well.

[vuyyurusaisrinivas]

can we build our own algorithm and can we stabilize algorithm or combine???

[ricky]

Neural Network model to predict sales amount or customer predictions please tell

[harrymaltby]

Great starter. It seem that a great video would be a one where the data is a row of numbers with one or two output values (from say a cvs file). The concept of a relative need of normalizing the numbers (or not) prior to being taken into the network and testing the performance of the network using a percentage of the data rows. Also, the concept of preforming modifications to the amount of hidden layers and finding/selecting those hidden layer compositions that provide better performance. Then given the network is at the performance level, how you create a brand new row and run/get the results of what the projected answer(s) are. Also, provide a discussion of whether the input of a lot of raw data can be enhanced by attempting to determine if specific math functions would be helpful in helping the network gleam the underlying physics of a set of data. Also, if data is artificially being somewhat manipulated by random increase/decrease over random time, is there a feature of the NNs that tell the amount of random inputs the process received if the data captures the data over time and provides a look back (a few values) in each row. It seems like a lot of discussions are getting away from the simple concept that armatures have of creating data and trying to predict and future event. Again thanks for providing this look at the subject from what python can now provide. I was raised on Basic, Fortran and Machine Language and so this is very interesting to see how language has captured features of a lot of programming in a single string/vocabulary by ganging extremely relevant words together.

[DanielMorales-rrid]

hi, im just begin using pythoon because id like to know how many trees are in a picture, but is hard to implement, thanks for your assistance