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Artificial intelligence training institute in delhi NCR | India

ARTIFICIAL INTELLIGENCE AI

Join With Our Courses To Develop Yourself.

$75
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Courses Overview

YCSPL provides 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, vectorization, Python for logistic applications for scripting machine learning applications.

 

What you will learn in this Deep Learning training?

  • Introduction to Deep Learning technique

  • Artificial Intelligence neural networks in Deep Learning

  • Training neural networks with training data

  • Convolutional neural networks and its applications

  • TensorFlow and Tensor Processing Unit

  • Supervised and unsupervised learning methodology

  • Machine learning with Python language

  • Application of DL in image recognition, NLP and more

  • Real world projects will be done in recommender systems and others

Who must take this Deep Learning Training Course?

  • Professionals who are in analytics, data science domains, ecommerce, search engine domains

  • Software professionals who are looking for a career switch and fresh graduates can also go for this training course.

Who can go for this Training Course?

Anybody can take this Training Course according to their background and related studies to this course.

How Deep Learning Training Course is effective?

Artificial Intelligence taking over each and every industry domain. Machine Learning and Deep Learning are the most common aspects of Artificial Intelligence that are being used everywhere from search engines. Taking the YCSPL Deep Learning training can help you to build a solid career in a rising technology domain and will help you to get the best jobs in top organizations

Introduction to Neural Networks

Introduction to AI, Introduction to Neural Networks, Supervised Learning with Neural Networks, Concept of Machine Learning, Basics of statistics, probability distributions, hypothesis testing.

 

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 Learning Works, Activation Functions, Illustrate Perceptron, Training a Perceptron, Important Parameters of Perceptron, What is Tensorflow, Tensorflow code-basics, Graph Visualization, Constants, Placeholders, Variables, Step by Step – Use-Case Implementation, Keras.

 

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

 

 

Projects

 

Project 1 : Image recognition with TensorFlow

Industry : Internet Search

Problem Statement : Building a robust deep learning model to recognize the right object on the internet depending on the 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 Networks

Industry : General

Problem Statement : Building an artificial Intelligence network with TensorFlow to identify the handwriting based on the input training data.

Topic : You will build an artificial intelligence model for training the neural network to recognize the handwriting. The various layers of neural network like input, hidden and output layers along 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
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