Join With Our Courses To Develop Yourself.
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
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.
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.
TensorFlow to build Neural Networks
Choosing the right number of hidden layers
The importance of back propagation.