(All course fees are in USD)

Course Description

Deep Learning also known as Deep Neural Learning, is a subset of machine learning, an application of AI, where machines imitate the workings of the human brain and employ artificial neural networks to process the information.

TensorFlow is an open source library created and released by google for numerical computation and building deep learning models.

In the traditional machine learning, most of the applied features need to be identified by a domain expert in order to reduce complexity of the data. Whereas the biggest advantage of Deep Learning algorithm is it tries to learn high-level features from data in incremental manner, which makes process simpler and popular. Deep Learning techniques outperform other techniques when data size is large and complex, and this technique is behind many high-end innovations.

Deep learning is one of the newest technological advances in artificial intelligence and machine learning. This Deep Learning with Keras and TensorFlow course is designed to help you master deep learning techniques and build deep learning models using the Keras and TensorFlow frameworks. These frameworks are used in deep neural networks and machine learning research, which in turn contributes to development and implementation of artificial neural networks.

Offered in Partnership with

SimpliLearn

Course Delivery

• Online self-paced learning
• Virtual classroom training

Total: 25+ hours online blended learning

Benefits

The Deep Learning market size currently surging at unprecedented growth rate. Industrial sectors like healthcare, information technology, fin-tech, and e-commerce need professionals with deep learning skills.

Skills to be Learned

• Keras and TensorFlow Framework
• PyTorch and its elements
• Image Classification
• Artificial Neural Networks
• Autoencoders
• Deep Neural Networks
• Conventional Neural Networks
• Recurrent Neural Networks
• ADAM Adagrad and Momentum

Award

Deep Learning with Keras and Tensorflow “Certificate of Achievement”

Awarding Organisation

SimpliLearn

Learning Outcomes

This course aims at achieving following objectives:

• Understand concepts of Keras and TensorFlow, its main functions, operations, and the execution pipeline
• Implement deep learning algorithms, understand neural networks, and traverse layers of data abstraction
• Master and comprehend advanced topics such as convolutional neural networks, recurrent neural networks, training deep networks, and high-level interfaces
• Build deep learning models using Keras and TensorFlow frameworks and interpret the results
• Understand the language and fundamental concepts of artificial neural networks, application of autoencoders, and Pytorch and its elements
• Troubleshoot and improve deep learning models
• Build your own deep learning project
• Differentiate between machine learning, deep learning, and artificial intelligence

Assessments

• Passing Quizz at end of lessons (75% score)
• Attendance of online virtual classes
• Satisfactory completion of Course-end Projects

Project 1 – PUBG Players Finishing Placement Prediction

Create a model that predicts players’ finishing placement based on their final stats, on a scale of 1 (first place) to 0 (last place).

Project 2 – Lending Club Loan Data Analysis

Create a model that predicts whether a loan will go into default using the historical data.

Who Should Enrol

Demand for skilled Deep Learning Engineers is booming across a wide range of industries. Target learners include:

• Software and IT professionals interested in analytics
• Data scientists
• Business/ data analysts who want to understand deep learning techniques
• Anyone with an interest in deep learning

Prerequisites

It is recommended that you first complete the following courses in order to improve your ability to understand the deep learning course’s concepts:

• Programming Fundamentals
• Statistics Essentials
• Concepts about Machine Learning

Course Overview

Section 1 – Deep Learning with TensorFlow (self learning)

Lesson 01 – Welcome

Lesson 02 – Introduction to Tensorflow

Lesson 03 – Convolutional Networks

Lesson 04 -Recurrent Neural Network

Lesson 05 – Restricted Boltzmann Machines (RBM)

Lesson 06 -Autoencoders

Lesson 07 – Course Summary

Section 2 – Deep Learning with Keras and TensorFlow (Live Online Classes)

Section 3 – Practice Projects

Accessible Period of Course

1 year from date of enrolment

*Note: We reserve the right to revise/change any of the course content &/or instructor at our sole & absolute discretion, without prior notice to learner.