Price  18,000.00  15,000.00

Course Features
Language: English
7 hours on-demand video
11 Lessons
Study Level: Intermediate
Certificate of Completion


Deep Learning Course (with TensorFlow)

Deep Learning Course (with TensorFlow)

  • 23rd Feb 2019 (5 Weekends) | Sat-Sun Training Mode: Online | 02:00 PM – 05:00 PM IST
  • 23rd March 2019 (5 Weekends) | Sat-Sun Training Mode: Online | 02:00 PM – 05:00 PM IST

Deep Learning with TensorFlow Training Course

Deep Learning course would teach you how to apply Google’s library in deep learning to enable you to solve real-world problems. Deep learning with tensorflow course would help you identify the various data types and how they are applied in order to solve some of the world’s largest problems. You would also learn the different types of TensorFlow, how they work, what are their main functions and how they can be applied to solve some of the world’s problems. You would also learn the basics concepts of TensorFlow and deep learning as well as learn how TensorFlow is applied for backpropagation to enable the tuning of the weights and biases simultaneously while the neural networks are being trained. Also, this Deep Learning with TensorFlow would cover the various types of deep architectures and how they work and function. With just the “Hello world”, this course would teach you how TensorFlow is used in curve fitting, classification, regression and minimization of errors.

Main Features

  • Machine learning understanding and mastery
  • Intensive instructor-led teaching, assistance, supervision and we’ll design examples
  • The real world application of TensorFlow and deep learning with examples and hands-on assignments
  • Would help you master the basics of TensorFlow, deep learning and how it relates to machine learning.

Learning Objectives

For Individuals
  • Deep Learning with TensorFlow course would offer them self-paced learning, contents, models and designs that were created by industry experts
For Businesses
  • Would offer them cooperate solutions that would help minimize human errors
  • Would enhance and advance individual and teamwork
  • Would offer blended learning that is accompanied by instructor-led supervision and examples
  • Would advance individual and team reporting

Deep Learning with TensorFlow Course Description

Most of the data that is available today is unstructured and unlabeled, this has made it difficult to capture data in images, sounds etc. The need for deep learning and TensorFlow has become ever important

  • To make you better understand machine learning and deep learning
  • To help you understand how deep learning can be applied with TensorFlow to solve certain world problems

Deep Learning with TensorFlow course is designed for everyone that seeks to improve data and learning.

Deep Learning with TensorFlow Curriculum

1. Introduction

1.1 Introduction to Deep Learning


What is a neural network?
Supervised Learning with Neural Networks – Python
How Deep Learning is different from Machine Learning

2. Overview of Machine Learning Concept

2.1 Overview of Machine Learnin

Quick tour of the types of machine learning


What is Machine Learning?
Supervised Machine Learning algorithms
K-Nearest Neighbors (KNN) concept and application
Naive Bayes concept and application
Logistic Regression concept and application
Classification Trees concept and application
Unsupervised Machine Learning algorithms
Clustering with K-means concept and application
Hierarchial Clustering concept and application

3. Tensor Flow Essentials

3.1 A quick refresh on Tensor Flow Essentials:


Representing tensors
Creating operators and excuting with sessions
Introduction Jupyter notebook for TensorFlow coding
TensorFlow variables
Visualizing data using TensorBoard

4. ML Algorithm - Linear Regression in TensorFlow:

4.1 A quick crash course on ML Algorithm


Regression problems
Linear regression applications
Available datasets
Coding Linear Regression with TensorFlow – Case study

5. Deep Neural Networks in TensorFlow

5.1 Introduction to Deep Neural Networks in TensorFlow


Basic Neural Nets
Single Hidden Layer Model
Multiple Hidden Layer Model

6. Convolutional Neural Networks

6.1 Overview to Convolutional Neural Networks


Introduction to Convolutional Neural Networks
Input Pipeline
Introduction to RNN, LSTM, GRU

7. Reinforcement Learning in Tensorflow

7.1 Overview to Reinforcement Learning in Tensorflow


Concept of Reinforcement Learning
Simple model applying Reinforcement Learning in TensorFlow

8. Hands on Deep Learning Application with TensorFlow

8.1 Overview to Hands on Deep Learning Application


Example Application – Case study
Hands on building the Deep Learning application with TensorFlow


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