Deep learning is a technique for implementing machine learning. It uses neural networks to learn. For this, Google has created Tensorflow as a framework to create deep learning model. Tensorflow is the most popular and powerful open source machine learning/deep learning framework. Tensorflow has many powerful Machine Learning API such as Neural Network, Convolutional Neural Network, Recurrent Neural Network, Word Embedding, Seq2Seq, Meta Learning, Reinforcement Learning and Generative Adversarial Networks (GAN). By using machine learning, lot of complex applications can be developed with great accuracy, no matter if the data is images, videos, text or even audiodata.

This course will dig deeper into the hidden layers of abstraction using raw data. You will also learn how to train your machine to craft new features to make sense of deeper layers of data. With the help of some practical examples, participants will be able to understand the concept of Deep Learning using Tensorflow library.

Course Highlights

● Basic Tensorflow operations

● Neural Network for Regression

● Neural Network for Classification

● Convolutional Neural Network for Images data

● Recurrent Neural Network for Text data

● Transfer Learning

● Tensorflow Hub

Course Objectives

After successfully completing this course, participants should be able to...

1. Understand how to use Tensorflow library for machine learning and deep learning

2. Differentiate Convolutional Neural Network and Recurrent Neural Network

3. Understand how to train data using CNN and RNN

Who this course is for:

1. Students/Lecturers

2. Researchers

3. Engineers & Data Scientist

Level: Intermediate

Age Group: 17 and Above

Additional Note: Please bring your own laptop for hands-on training with Linux OS/Windows.