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NEURAL NETWORK TRAINING

Neural Network Training, Learn ANN, RNN, CNN using Python and R. Convolutional Neural Network Course, at Mildaintrainings we provide neural network python course and neural network R course, artificial neural network course, deep neural network course. Learn how to make Neural Network models in Tensorflow, learn Deep Learning algorithms using ANN. The Main focus of this course is to building a Neural Network from scratch and understanding its basic concepts, understanding the working of a neural network like how does forward and backward propagation work. Optimization algorithms (Full Batch and Stochastic gradient descent), how to update weights and biases? visualization of each step in Excel and on top of that code in python and R. Enroll Now and know applications of ANN, RNN, CNN, Neural Network in Python and solving real-life challenges related to:

  • Computer Vision
  • Speech
  • Natural Language Processing

 

4 Days / 32 Hrs

For Classroom & Online Training

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neural network training artificial neural network course deep neural network course  Reviews 

Email Id : info@mildaintrainings.com

Contact no: +91 8447121833 

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MODE OF TRAININGS

ONLINE CLASSROOM

Instructor-Led Training

Live Online Classroom

ONE TO ONE

Instructor-Led Training

Live Online Classroom

TEAM / CORPORATE

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Neural Network Training | Neural Network Python Course

Price: INR 20000

Course Outline: Click here

Discounts: We offer multiple discount options for team and corporates call or whatsapp+91-8447121833 for more info.

Placement: 100% Placement Assistance

Delivery Options: Attend remote-live or on-demand online classes.

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DESCRIPTION

Artificial Neural Network Course | Convolutional Neural Network Course

DESCRIPTION

Artificial Neural Network Course | Convolutional Neural Network Course

Artificial Neural Network ANN
An ‘artificial neural network’ (ANN) is a computing system made up of a number of simple, highly interconnected processing elements, which process information by their dynamic state response to external inputs. -by Dr. Robert Hecht-Nielsen. ANNs are processing devices (algorithms or actual hardware) that are loosely modeled after the neuronal structure of the mammalian cerebral cortex but on much smaller scales. A large ANN might have hundreds or thousands of processor units, whereas a mammalian brain has billions of neurons with a corresponding increase in the magnitude of their overall interaction and emergent behavior. Although ANN researchers are generally not concerned with whether their networks accurately resemble biological systems, some have. For example, researchers have accurately simulated the function of the retina and modeled the eye rather well. Although the mathematics involved with neural networking is not a trivial matter, a user can rather easily gain at least an operational understanding of their structure and function. The Main focus of this course is to building a Neural Network from scratch and understanding its basic concepts. understanding the working of a neural network like how does forward and backward propagation work. optimization algorithms (Full Batch and Stochastic gradient descent), how to update weights and biases, visualization of each step in Excel and on top of that code in python and R.
Enroll Now and know applications of using Neural Network in Python and solving real-life challenges related to:

  • Computer Vision
  • Speech
  • Natural Language Processing

Course Objectives: Neural Network Training
  • Introduce ‘artificial neural network’ (ANN)
  • Tensorboard visualization
  • Hands-on coding of Neural Networks with Tensorflow
  • Applying ANN with Tensorflow on the case study
Course Prerequisites: Neural Network Training
  • Basic Python Programming is required.
  • Knowledge of Statistics and mathematics is recommended
  • Basic understanding of neural networks

CURRICULUM

Neural Network Python Course | Convolutional Neural Network Course

CURRICULUM

Neural Network Python Course | Convolutional Neural Network Course

Introduction to Neural Network
  • Intuition behind Neural networks
  • Learn about various test cases
  • Working with hidden layers
  • Forward Propagation
  • How do you reduce the error?
  • Backward Propagation
  • Gradient Descent
Multi-Layer Perceptron and its basics
  • Perceptron
  • Input output relationships
    • Combining the input and computing the output
    • Add weights to the inputs
    • Add bias
  • Artificial neuron
  • Non-linear transformations
  • Activation function
    • Sigmoid
    • Tanh
    • ReLu
    • Logit etc.
  • Forward Propagation
  • Back Propagation
  • Epochs
  • Multi-layer perceptron
  • Full Batch Gradient Descent
  • Stochastic Gradient Descent
Steps involved in Neural Network methodology

Forward Propagation

  • Initialize weights and biases with random values
  • Linear transformation
  • Non-linear transformation
  • Linear transformation on hidden layer activation

Backward Propagation

  • Compare prediction
  • Compute the slope/ gradient
  • Compute change factor
  • Finding errors in hidden layer
  • Compute change factor at hidden layer
  • Update weights at the output and hidden layer
  • Update biases at the output and hidden layer
Visualizing steps for Neural Network working methodology
  • Understand working methodology of Neural Network (MLP)
    • Input
    • Weights
    • Biases
    • Output
    • Error matrix
  • Read input and output
  • Initialize weights and biases with random values
  • Calculate hidden layer input
  • Perform non-linear transformation on hidden linear input
  • Perform linear and non-linear transformation of hidden layer activation at output layer
  • Calculate gradient of Error(E) at output layer
  • Compute slope at the output and hidden layer
  • Compute delta at output layer
  • Calculate Error at hidden layer
  • Compute delta at hidden layer
  • Update weight at both output and hidden layer
  • Update biases at both output and hidden layer
Implementing NN using Numpy (Python) (Hands-on Practical)

Practical Sessions on

  • NN
  • Numpy
Implementing NN using R (Hands-on Practical)

Practical Sessions on

  • NN
  • R

FAQ | Neural Network in Python and R

Why should I take Neural Network in Python and R Training from Mildaintrainings?

You should go for Neural Network in Python and R from Mildaintrainings as our trainers have 12 plus years of industry practical experience and we also provide Practical training with the live project so that you could understand each and everything better, it will help you in your job. At Mildaintraining we will provide you six months Technical support as well.

Do I get the Neural Network in Python and R training certificate?

Yes, at Mildaintrainings we will provide you participation certificate after the completion of Neural Network in Python and R course from Mildaintrainings.

When will the classes be held for Neural Network in Python and R?

Classes will be held on weekends as well as weekdays as per schedule or your convenience.

What if I miss the Neural Network in Python and R class?

If you miss the class in that case backup class can be adjusted in next live session.

What is Neural Network in Python and R course duration?

This course duration will be of 32 – 40 hours or 4 days and it will be Instructor lead training at Mildaintrainings with Practical training with live project. The timing will be according to your convenience it can be on weekend and weekdays.

Reviews

Rekha Amminbhavi

Testing Engineer at CSC

Guidewire:
The program was really knowledgeable and the modules were just perfectly made and managed and were taught with ease.
I am so happy I choose mildain

Jyotish Phukon

Senior EDI Analyst at SIQES

IBM Sterling Integrator:
Good training. Explaining the things with practical examples. Well experienced and confident enough to answer every query.
Trainers are working more as a friend rather than working like for money. Worth of paying for the course.

Machine Leaning With Python

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