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Artificial Intelligence Summer Training in Delhi NCR | Yami Services


Join With Our Courses To Develop Yourself.


Courses Overview

YCSPL provides comprehensive deep learning training which will help you to work on the cutting edge of artificial intelligence. As part of the training, you will master various aspects of the artificial intelligence of various aspects of neural network, supervised and unsafe education, neural network mentality, binary classification, vectoration, Python for logistic applications for scripting machine learning applications.

Introduction to Neural Networks

Introduction to AI, Introduction to Neural Network, Supervised Education with Neural Network, Concept of Machine Learning, Data Basics, Probability Distribution, Hypothesis Test


Multi-layered Neural Networks

Introduction to Multi Layer Network, Concept of Deep neural networks, Regularization.


Regularisation techniques (L1, L2)

Regression techniques, Lasso – L1, Ridge – L2.


Deep Learning Libraries

How Deep Training Works, Activation Functions, Illustrate Pseptron, Training a Pseptron, Important Parametric Parameters, Tensiformo, Tansforlo Code-Basics, Graph Visualization, Constants, Placeholders, Variables, Phase-by-Step Implementation, What is the phase of Kairas .


CNN: Convolutional Neural Networks

Introduction to CNNs, CNNs Application, Architecture of a CNN, Convolution and Pooling layers in a CNN, Understanding and Visualizing a CNN, Transfer Learning and Fine-tuning Convolutional Neural Networks


RNN: Recurrent Neural Networks

Intro to RNN Model, Application use cases of RNN, Modelling sequences, Training RNNs with Backpropagation, Long Short-Term memory (LSTM), Recursive Neural Tensor Network Theory, Recurrent Neural Network Model


LSTM: Long Short Term Memory

LSTM: Long Short Term Memory


Hidden Markov

Hidden Markov





Project 1 : Image recognition with TensorFlow

Industry : Internet Search

Problem Statement : Creating a strong deep learning model to identify the right object on the Internet based on user search for the image.

Description : In this project you will learn how to build Convolutional Neural Network using Google TensorFlow. You will do visualization of images using training, providing input images, losses and distributions of activations and gradients. You will learn to break each image into manageable tiles and input it to the Convolutional Neural Network for the desired result.

Highlights :

  • Constructing Convolutional Neural Network using TensorFlow

  • Convolutional, Dense & Pooling layers of CNNs

  • Filtering the images based on user queries.



Project 2 :Handwriting recognition with neural network

Industry : General

Problem Statement :To identify handwriting on the basis of input training data, make an artificial intelligence network with tenerflow.

Topic : You will build an artificial intelligence model for training the neural network to recognize the handwriting. Various layers of neural network such as input, hiding, and output layers with their functions will be clear. Implementing back-propagation for calculating error of each neuron used with a gradient-based optimizer is explained.

Highlights :

  • TensorFlow to build Neural Networks

  • Choosing the right number of hidden layers

  • The importance of back propagation.

  • Duration: 30 Days
  • Services

    Technical Support Project, Consultancy Monitoring and Control Smart Metering Data Logging, Dedicated Graphical Interface

    Corporate Training, Industrial Training, Campus Training, Classroom Training, Bootcamp Training, Online Training

    Data Science, Machine Learning, Robotics, Business Intelligance, Finance Controlling, Water Treatment and Power Plants

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