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 & Get Certified now!

✔ Course Duration : 32 hrs

✔ Training Options : Live Online / Self-Paced / Classroom

✔ Certification Pass : Guaranteed

Neural Network Training

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 Python India and solving real-life challenges related to:

Computer Vision
Speech
Natural Language Processing

What you will Learn

Introduce 'artificial neural network' (ANN)
Tensorboard visualization
Hands-on coding of Neural Networks with Tensorflow
Applying ANN with Tensorflow on the case study

PREREQUISITES

Basic Python Programming is required.
Knowledge of Statistics and mathematics is recommended
Basic understanding of neural networks

CURRICULUM

Learning Objectives:

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

Learning Objectives:

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

Learning Objectives:

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

Learning Objectives:

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

Learning Objectives:

Practical Sessions on

NN
Numpy

Learning Objectives:

Practical Sessions on

FAQs

You can enroll for this classroom training online. Payments can be made using any of the following options and receipt of the same will be issued to the candidate automatically via email.
1. Online ,By deposit the mildain bank account
2. Pay by cash team training center location

Highly qualified and certified instructors with 20+ years of experience deliver more than 200+ classroom training.

Contact us using the form on the right of any page on the mildaintrainings website, or select the Live Chat link. Our customer service representatives will be able to give you more details.

You will never miss a lecture at Mildaintrainigs! You can choose either of the two options: View the recorded session of the class available in your LMS. You can attend the missed session, in any other live batch.

We have a limited number of participants in a live session to maintain the Quality Standards. So, unfortunately, participation in a live class without enrollment is not possible. However, you can go through the sample class recording and it would give you a clear insight about how are the classes conducted, quality of instructors and the level of interaction in a class.

Yes, you can cancel your enrollment if necessary prior to 3rd session i.e first two sessions will be for your evaluation. We will refund the full amount without deducting any fee for more details check our Refund Policy

Yes, the access to the course material will be available for lifetime once you have enrolled into the course.

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