Join With Our Courses To Develop Yourself.
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
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.
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.