Neural Network Training in Hyderabad | Neural Network in Python Training


Neural Network Training HYDERABAD, Learn ANN, RNN, CNN using Python and R. Convolutional Neural Network Course HYDERABAD, 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: Enroll & Get Certified now!

  • ✔ Course Duration : 32 hrs
  • ✔ Training Options : Live Online / Self-Paced / Classroom
  • ✔ Certification Pass : Guaranteed
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32 hrs

Course Duration


Countries And Counting


Corporates Served

20+ hrs



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.

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


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


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

  • NN
  • R


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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