AI & Deep Learning with TensorFlow

Name: AI & Deep Learning with TensorFlow
Item: AI & Deep Learning with TensorFlow
Rating: 4.5

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Edureka's Deep Learning in TensorFlow with Python Certification Training is curated by industry professionals as per the industry requirements & demands. You will master the concepts such as SoftMax function, Autoencoder Neural Networks, Restricted Boltzmann Machine (RBM) and work with libraries like Keras & TFLearn. The course has been specially curated by industry experts with real-time case studies.

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60 days of Cloud Lab access

Why should you take AI & Deep Learning with TensorFlow?

TensorFlow could be a game-changer in the future of AI - Google

Google stakes it's future on a piece of software: TensorFlow - MIT

TensorFlow is used heavily in Google's Speech Recognition System

Instructor-led AI & Deep Learning with TensorFlow live online classes

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Curriculum

Introduction to Deep Learning

Learning Objectives:

In this module, you’ll get an introduction to Deep Learning and understand how Deep Learning solves problems which Machine Learning cannot. Understand fundamentals of Machine Learning and relevant topics of Linear Algebra and Statistics.

Topics:

Deep Learning: A revolution in Artificial Intelligence
Limitations of Machine Learning
What is Deep Learning?
Advantage of Deep Learning over Machine learning
3 Reasons to go for Deep Learning
Real-Life use cases of Deep Learning
Review of Machine Learning: Regression, Classification, Clustering, Reinforcement Learning, Underfitting and Overfitting, Optimization

Hands-On

Implementing a Linear Regression model for predicting house prices from Boston dataset
Implementing a Logistic Regression model for classifying Customers based on a Automobile purchase dataset

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Understanding Neural Networks with TensorFlow

Learning Objectives:

In this module, you’ll get an introduction to Neural Networks and understand it’s working i.e. how it is trained, what are the various parameters considered for its training and the activation functions that are applied.

Topics:

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
Creating a Model
Step by Step - Use-Case Implementation

Hands-On

Building a single perceptron for classification on SONAR dataset

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Deep dive into Neural Networks with TensorFlow

Learning Objectives:

In this module, you’ll understand backpropagation algorithm which is used for training Deep Networks. You will know how Deep Learning uses neural network and backpropagation to solve the problems that Machine Learning cannot.

Topics:

Understand limitations of a Single Perceptron
Understand Neural Networks in Detail
Illustrate Multi-Layer Perceptron
Backpropagation – Learning Algorithm
Understand Backpropagation – Using Neural Network Example
MLP Digit-Classifier using TensorFlow
TensorBoard

Hands-On

Building a multi-layered perceptron for classification of Hand-written digits

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Master Deep Networks

Learning Objectives:

In this module, you’ll get started with the TensorFlow framework. You will understand how it works, its various data types & functionalities. In addition, you will create an image classification model.

Topics:

Why Deep Networks
Why Deep Networks give better accuracy?
Use-Case Implementation on SONAR dataset
Understand How Deep Network Works?
How Backpropagation Works?
Illustrate Forward pass, Backward pass
Different variants of Gradient Descent
Types of Deep Networks

Hands-On

Building a multi-layered perceptron for classification on SONAR dataset

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Convolutional Neural Networks (CNN)

Learning Objectives:

In this module, you’ll understand convolutional neural networks and its applications. You will learn the working of CNN, and create a CNN model to solve a problem.

Topics:

Introduction to CNNs
CNNs Application
Architecture of a CNN
Convolution and Pooling layers in a CNN
Understanding and Visualizing a CNN

Hands-On

Building a convolutional neural network for image classification. The model should predict the difference between 10 categories of images.

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Recurrent Neural Networks (RNN)

Learning Objectives:

In this module, you’ll understand Recurrent Neural Networks and its applications. You will understand the working of RNN, how LSTM are used in RNN, what is Recursive Neural Tensor Network Theory, and finally you will learn to create a RNN model.

Topics:

Introduction 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

Hands-On

Building a recurrent neural network for SPAM prediction.

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Restricted Boltzmann Machine (RBM) and Autoencoders

Learning Objectives:

In this module, you’ll understand RBM & Autoencoders along with their applications. You will understand the working of RBM & Autoencoders, illustrate Collaborative Filtering using RBM and understand what are Deep Belief Networks.

Topics:

Restricted Boltzmann Machine
Applications of RBM
Collaborative Filtering with RBM
Introduction to Autoencoders
Autoencoders applications
Understanding Autoencoders

Hands-On

Building a Autoencoder model for classification of handwritten images extracted from the MNIST Dataset

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Keras API

Learning Objectives:

In this module, you’ll understand how to use Keras API for implementing Neural Networks. The goal is to understand various functions and features that Keras provides to make the task of neural network implementation easy.

Topics:

Define Keras
How to compose Models in Keras
Sequential Composition
Functional Composition
Predefined Neural Network Layers
What is Batch Normalization
Saving and Loading a model with Keras
Customizing the Training Process
Using TensorBoard with Keras
Use-Case Implementation with Keras

Hands-On

Build a model using Keras to do sentiment analysis on twitter data reactions on GOP debate in Ohio

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TFLearn API

Learning Objectives:

In this module, you’ll understand how to use TFLearn API for implementing Neural Networks. The goal is to understand various functions and features that TFLearn provides to make the task of neural network implementation easy.

Topics:

Define TFLearn
Composing Models in TFLearn
Sequential Composition
Functional Composition
Predefined Neural Network Layers
What is Batch Normalization
Saving and Loading a model with TFLearn
Customizing the Training Process
Using TensorBoard with TFLearn
Use-Case Implementation with TFLearn

Hands-On

Build a recurrent neural network using TFLearn to do image classification on hand-written digits

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In-Class Project

Learning Objectives:

In this module, you should learn how to approach and implement a project end to end. The instructor will share his industry experience and related insights that will help you kickstart your career in this domain. In addition, we will be having a QA and doubt clearing session for you.

Topics:

How to approach a project?
Hands-On project implementation
What Industry expects?
Industry insights for the Machine Learning domain
QA and Doubt Clearing Session

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Course Description

About Deep Learning in TensorFlow with Python Certification Training

In this Deep Learning in TensorFlow with Python Training we will learn about what is AI, explore neural networks, understand deep learning frameworks, implement various machine learning algorithms using Deep Networks. We will also explore how different layers in neural networks does data abstraction and feature extraction using Deep Learning.

Edureka’s Deep Learning in TensorFlow training is designed to make you a Data Scientist by providing you rich hands-on training on Deep Learning in TensorFlow with Python. This course is a stepping stone in your Data Science journey using which you will get the opportunity to work on various Deep Learning projects.

What are the objectives of our Deep Learning In TensorFlow with Python Course?

Deep Learning in TensorFlow with Python Training is designed by industry experts to make you a Certified Deep Learning Engineer. The Deep Learning in TensorFlow course offers:

In-depth knowledge of Deep Neural Networks
Comprehensive knowledge of various Neural Network architectures such as Convolutional Neural Network, Recurrent Neural Network, Autoencoders
Implementation of Collaborative Filtering with RBM
The exposure to real-life industry-based projects which will be executed using TensorFlow library
Rigorous involvement of an SME throughout the AI & Deep Learning Training to learn industry standards and best practices

Why Should you go for Deep Learning in TensorFlow with Python Certification Training?

Deep Learning is one of the most accelerating and promising fields, among all the technologies available in the IT market today. To become an expert in this technology, you need a structured training with the latest skills as per current industry requirements and best practices.

Besides strong theoretical understanding, you will be working on various real-life data projects using different neural network architectures as a part of solution strategy.

Additionally, you will receive guidance from a Deep Learning expert who is currently working in the industry on real-life projects.

What are the skills that you will be learning with our Deep Learning in TensorFlow Certification Training?

Deep Learning in TensorFlow with Python Training will help you to become a Deep Learning Engineer. It will hone your skills by offering you comprehensive knowledge on Deep Learning in TensorFlow. It will also acquaint you with the required hands-on experience for solving real-time industry-based Deep Learning projects. During this course you will be trained by our expert instructors on:

Deep Learning and TensorFlow Concepts
Working with Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN)
Proficiency in Long short-term memory (LSTM)
Implementing Keras, TFlearn, Autoencoders
Implementing Restricted Boltz-mann Machine (RBM)
Knowledge of Neural Networks & Natural Language Processing (NLP)
Using Python with TensorFlow Libraries
Perform Text Analytics
Perform Text Processing

Who should go for this Deep Learning in TensorFlow with Python Course?

The TensorFlow with Python Training is for all the professionals who are passionate about Deep Learning and want to go ahead and make their career as a Deep Learning Engineer. It is best suited for individuals who are:

Developers aspiring to be a 'Data Scientist'
Analytics Managers who are leading a team of analysts
Business Analysts who want to understand Deep Learning (ML) Techniques
Information Architects who want to gain expertise in Predictive Analytics
Analysts wanting to understand Data Science methodologies

However, Deep learning is not just focused on one industry or skill set, it can be used by anyone to enhance their portfolio.

What are the pre-requisites for this Deep Learning in TensorFlow with Python Course?

Required Pre-requisites

Basic programming knowledge in Python
Concepts about Machine Learning

Edureka offers you complimentary self-paced courses:

Statistics and Machine learning algorithms
Python Essentials

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Watch Lesson 1 (Recorded)

Module 1: Introduction to Deep Learning

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Certification

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Testimonial Reviews

Dheerendra YadavProject Lead at HCL Technologies, Ghaziabad, Uttar Pradesh, India

Earlier I had taken training in different technologies from other institutes and companies but no do ... Read More

Praveen KonkisaBI Architect & Hadoop Specialist at Teradata

I have taken Informatica, Hadoop, R-programming, Spark and Scala and several other training's from p ... Read More

Rahul KushwahDevops Software Developer and AWS Certified Solutions Architect

Edureka is BEST in provide e-learning courses for all software programs including latest technologie ... Read More

Sudhiranjan SarmaAnalytics and Information management, Cognizant

My experience with edureka about Python is much impressed, with such quality of the training. We get ... Read More

Janardhan SingamaneniPrincipal Data Engineer at Staples

I took kafka and datascience classes with EDUREKA and its overall nice. After thorough scanning of a ... Read More

Rupali SharmaProject Manager | PMP Certified | Virginia

I would definitely like to give more than 5 stars to Edureka, i have been associated with more than ... Read More

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Project

How will I execute the practicals?

You will do your Assignments/Case Studies using Jupyter Notebook that is already installed on your Cloud Lab environment whose access details will be available on your LMS. You will be accessing your Cloud Lab environment from a browser. For any doubt, the 24*7 support team will promptly assist you.

What is CloudLab?

CloudLab is a cloud-based Jupyter Notebook which is pre-installed with TensorFlow and Python packages on cloud-lab environment. It is offered by Edureka as a part of Deep Learning with TensorFlow course where you can execute all the in-class demos and work on real-life projects in a fluent manner.

You’ll be able to access the CloudLab via your browser which requires minimal hardware configuration. In case, you get stuck in any step, our support ninja team is ready to assist 24x7.

What are the projects included in this Deep Learning in TensorFlow with Python Certification Training?

Edureka's TensorFlow Certification Training includes the following case studies:

Create an image classifier using CNN, and classify images in one of the predefined 100 classes
Create a script generator using LSTM, and generate scripts for any popular novel that might interest you
Choose a dataset of your own, explore the different challenges faced on the dataset domain and try to solve one of them with any neural network architecture covered in this course

Training Features

Instructor-led Sessions

30hrs of Online Live Instructor-led Classes. Weekend class: 10 sessions of 3 hours each. Weekday class: 15 sessions of 2 hours each

Real-life Case Studies

Towards the end of the Course, you will work on a real life case study.

Assignments

Each class has practical assignments which shall be finished before the next class and helps you to apply the concepts taught during the class.

Lifetime Access

You get lifetime access to the Learning Management System (LMS). Class recordings and presentations can be viewed online from the LMS.

24 x 7 Expert Support

We have 24x7 online support team available to help you with any technical queries you may have during the course.

Certification

End of the course, you will be working on a project. Edureka certifies you as a "Deep Learning Engineer" based on the project.

Forum

We have a community forum for all our customers wherein you can enrich their learning through peer interaction and knowledge sharing.

FAQ's

What if I miss a class?

You will never miss a lecture at edureka! You can choose either of the two options:

View the recorded session of the class available in your LMS.
You can attend the missed session, in any other live batch.

Will I get placement assistance?

To help you in this endeavor, we have added a resume builder tool in your LMS. Now, you will be able to create a winning resume in just 3 easy steps. You will have unlimited access to use these templates across different roles and designations. All you need to do is, log in to your LMS and click on the "create your resume" option.

Can I attend a demo session before enrollment?

We have limited number of participants in a live session to maintain the Quality Standards. So, unfortunately participation in a live class without enrollment is not possible. However, you can go through the sample class recording and it would give you a clear insight about how are the classes conducted, quality of instructors and the level of interaction in a class.

Who are the instructors?

All the instructors at edureka! are practitioners from the Industry with minimum 10-12 yrs of relevant IT experience. They are subject matter experts and are trained by edureka for providing an awesome learning experience to the participants.

What if I have more queries?

Just give us a CALL at +91 98702 76459/1844 230 6365 (US Tollfree Number) OR email at sales@edureka.co

What is the average salary of Deep Learning and TensorFlow Certified Engineer?

According to payscale.com, the median salary for Deep Learning and TensorFlow Certified Engineer tops $120,000 per year.

Why learn AI & Deep Learning with TensorFlow?

Deep Learning is one of the most exciting and promising segments of Artificial Intelligence and machine learning technologies. The software industry now-a-days is moving towards machine intelligence. In fact, concepts within AI like Deep Learning with TensorFlow has become necessary in every sector generating lot of job opportunities worldwide.

What is the best way to learn Deep Learning with Tensorflow?

This deep learning course with TensorFlow is all you need to master different deep learning algorithms and their implementation using TensorFlow, which is an open-source software library developed by Google building deep neural networks. By the end of this online instructor-led TensorFlow Training, you will be able to tackle real life industry grade projects based on deep learning and TensorFlow.

What are the skills needed to master Deep Learning with Tensorflow?

In order to master Deep Learning with Tensorflow you need following skills:

Understanding of Tensorflow concepts, its main functions, operations and the pipeline of execution

Building and training Deep Learning Models using TensorFlow

Understanding the working of Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) and their applications

Proficiency in working with TFlearn, Autoencoders, Keras

Improving accuracy of deep learning models

Implementation of Restricted Boltz-mann Machine (RBM)

Performing Text Processing and Analysis

What is the future scope of Deep Learning and Tensorflow?

Deep learning is being applied on most of the AI related areas for better performance. Lots and lots companies are moving into Deep Learning to improve their model accuracy and therefore, making their product more efficient. There is an ample opportunity to apply Deep Learning & TensorFlow in the field of medicine, precision agriculture, etc.

Why take online Deep Learning with Tensorflow Certification course? How is it better than offline course?

The online Deep Learning with TensorFlow Certification course can be attended from anywhere as per your ease. The major key takeaways of an online TensorFlow Training over other offline trainings are:

Latest Course Curriculum as per current industry standards and demands
Live & Interactive Session: The class will be taken by an industry expert in an interactive fashion where you can clarify all your doubts in the class itself.
Industry Grade Projects and Case Studies: In order to strengthen your subject knowledge, you will be provided with various industry grades projects and case studies.
Lifetime Access & 24x7 support: You will be awarded with lifetime access to all of the the course content as well as class recordings which you can go through again anytime later. Besides this, we have our 24x7 support ninjas ready to clarify any of your doubts or help you out once you get stuck anywhere.

How can a beginner learn Deep Learning with Tensorflow?

Deep Learning with TensorFlow Certification Training by Edureka has been carefully designed in such a way that both beginners and experts can go through it without facing any difficulties. The instructor will be available for you throughout the training period to ensure that you are able grasp all the concepts perfectly. Besides this, the 24x7 support ninjas are available to help you with all your issues and doubts.

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AI & Deep Learning with TensorFlow

Why should you take AI & Deep Learning with TensorFlow?

Instructor-led AI & Deep Learning with TensorFlow live online classes

Financing Options

Curriculum

Introduction to Deep Learning

Understanding Neural Networks with TensorFlow

Deep dive into Neural Networks with TensorFlow

Master Deep Networks

Convolutional Neural Networks (CNN)

Recurrent Neural Networks (RNN)

Restricted Boltzmann Machine (RBM) and Autoencoders

Keras API

TFLearn API

In-Class Project

Course Description

About Deep Learning in TensorFlow with Python Certification Training

What are the objectives of our Deep Learning In TensorFlow with Python Course?

Why Should you go for Deep Learning in TensorFlow with Python Certification Training?

What are the skills that you will be learning with our Deep Learning in TensorFlow Certification Training?

Who should go for this Deep Learning in TensorFlow with Python Course?

What are the pre-requisites for this Deep Learning in TensorFlow with Python Course?

Testimonial Reviews

Video Reviews

Project

How will I execute the practicals?

What is CloudLab?

What are the projects included in this Deep Learning in TensorFlow with Python Certification Training?

Training Features

Instructor-led Sessions

Real-life Case Studies

Assignments

Lifetime Access

24 x 7 Expert Support

Certification

Forum

FAQ's

What if I miss a class?

Will I get placement assistance?

Can I attend a demo session before enrollment?

Who are the instructors?

What if I have more queries?

What is the average salary of Deep Learning and TensorFlow Certified Engineer?

Why learn AI & Deep Learning with TensorFlow?

What is the best way to learn Deep Learning with Tensorflow?

What are the skills needed to master Deep Learning with Tensorflow?

What is the future scope of Deep Learning and Tensorflow?

Why take online Deep Learning with Tensorflow Certification course? How is it better than offline course?

How can a beginner learn Deep Learning with Tensorflow?

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