Deep Learing

Deep Learing

Introduction

Deep Learning is at the cutting edge of what machines can do, and developers and business leaders absolutely need to understand what it is and how it works. This unique type of algorithm has far surpassed any previous benchmarks for classification of images, text, and voice.

It also powers some of the most interesting applications in the world, like autonomous vehicles and real-time translation. There was certainly a bunch of excitement around Google’s Deep Learning based AlphaGo beating the best Go player in the world, but the business applications for this technology are more immediate and potentially more impactful. This post will break down where Deep Learning fits into the ecosystem, how it works, and why it matters.

What is Deep Learning?

Deep learning is an artificial intelligence function that aims to imitate the human brain’s ability to process data and recognise patterns for making decisions. Deep learning is a subclass of machine learning in artificial intelligence that uses networks capable of learning from data that is unstructured or unlabelled in an unsupervised manner.

Deep learning utilises a hierarchical level of artificial neural networks to carry out of the process of machine learning. While in traditional programs, analyses are built with data in a linearly, deep learning enables machines to process data in a nonlinear way using a hierarchical function. Computer models learn to perform specific tasks such as identifying predetermined objects in images, text or sound. Deep learning can have extremely high accuracy, sometimes surpassing human-level. Deep learning algorithms are trained using a large set of labelled data and neural network architectures that contain several layers.

Other applications of deep learning include medical research, industrial automation, consumer electronics, aerospace, and space research.

Objective of this Training

To improve the performance of a Deep Learning model the goal is to the reduce the optimization function which could be divided based on the classification and the regression problems. Below are of some of objective functions used in Deep Learning. On completing this Training, there will be ample opportunities for you to work as a Deep learning engineer or AI specialist. You will be able to do some real world concepts like TensorFlow, Keras, neural network, ANN, CNN,OpenCV etc. After completion of this course will be able work on real life projects like image processing, text classification, recommendation engine, Object or Face recognition etc.

Learning Objectives

By the ending of this Training, you will be able to:

 Machine learning landscapes along with the historical development & progress of deep learning.

 Discuss deep machine intelligence & GPU computing w/ the latest TensorFlow 1.x.

 Access public datasets & utilize them using TensorFlow to load, process, and transform data.

 Use TensorFlow on real-world datasets, including images, text, & more.

 Use deep learning for scalable object detection & mobile computing.

 Train machines quickly to learn from data by exploring reinforcement learning techniques.

Eligibility Required

 BE/BTech. (All Streams)

 BCA, BSc (CS/IT) Degree

 PGDCA, MCA, ME /MTech

On completion of this training, you will be work as -

 Data Scientist

 Deep Learning Engineer

 Deep Learning Expert

 Machine Learning Engineer

 Product Data Engineer

 Principal Data Scientist

 Full Stack Data Developer