Deep Learning Course (with Keras and Tensorflow) Certification Training by Simplilearn: Fee, Duration, How to Apply

Quick Facts

particular	details
Medium of instructions English	Mode of learning Self study, Virtual Classroom	Mode of Delivery Video and Text Based	Frequency of Classes Weekdays, Weekends

Course overview

The Deep Learning course (with Keras and Tensorflow) certification training course, will educate participants on deep learning concepts and models using Keras and TensorFlow frameworks, to carry out deep learning algorithms. The Deep Learning course (with Keras and Tensorflow) certification training online provides 34 hours of blended learning, industry projects, and dedicated project mentoring sessions from industry experts that will help you prepare for a career as a Deep Learning engineer.

The Deep Learning course (with Keras and Tensorflow) certification training offers you a competitive edge in solving applications without human intervention. The training provides you with ways of expanding the limits of what a computer system can accurately inspect. The Deep Learning course (with Keras and Tensorflow) certification training online will help you differentiate between Deep Learning, Machine Learning, and Artificial Intelligence.

Deep Learning course (with Keras and Tensorflow) certification training by Simplilearn benefits IT developers and testers, data analysts, junior data scientists, analytics professionals, software engineers, statisticians, students, and professionals indulging in data across every field. The Deep Learning course (with Keras and Tensorflow) certification training online will help the candidates in creating deep learning models to interpret the results in building a deep learning project.

Course and certificate fees

certificate availability

Yes

certificate providing authority

Simplilearn

Who it is for

Data analyst Statistician Data scientist Software engineer

The Deep Learning course with (Keras and Tensorflow) certification training course is highly suitable for professionals interested in getting acquainted with deep learning concepts across industries like IT, finance, and more. Some common profiles include –

Software Engineers
Software Developers
Testers
Data Analysts
Statistician

Eligibility criteria

Certification Qualifying Details

The Deep Learning course (with Keras and Tensorflow) certification training shall be awarded once the course is completed.

What you will learn

Knowledge of deep learning

Once you complete the Deep Learning course (with Keras and Tensorflow) certification training syllabus, you will be adept in skills such as –

Use an open-sourced framework like TensorFlow and a high-level API like Keras and benefit from the vast library of deep neural networks
With improved processing power you can use these two frameworks for deep learning projects in the most popular languages like C, C++, Python, Java, Android, Linux, Windows, IoS, and many more
Learn to use PyTorch and its elements which will give you simpler and faster Python codes to generate dynamic computational graphs.
The use of PyTorch will ensure better optimization of data.
Analyse visual elements with the help of Convolutional Neural Networks (CNN), while working on image classification
Speed up your image classifying process and increase its efficiency
Use ANNs in pattern recognition software, while also utilising them in making machine learning models
Learn to use Autoencoders to develop models of image processing, anomaly detection, machine translation, and other deep learning models
Utilise Deep Neural Networks in a variety of models based on deep learning for increased computation power, like speech recognition, audio recognition, image analysis, and machine vision
Analyse large pools of raw and unsegmented data with the use of rich recurrent neural networks
Understand the role of optimizers to establish and reform the weight parameters in diminishing the loss function

The syllabus

Section 1 - Deep Learning with Keras and Tensorflow (IBM)

Lesson 01: Deep Learning with Keras and Tensorflow (IBM)

1.01 Deep Learning with Keras and Tensorflow (IBM)

Section 2 - Deep Learning with Keras and Tensor Flow (Live Classes)

Lesson 1 - Course introduction

Introduction
Accessing Practice Lab

Lesson 2 - AI and Deep learning introduction

What is AI and Deep learning
Brief History of AI
Recap: SL, UL and RL
Deep learning : successes last decade
Demo & discussion: Self driving car object detection
Applications of Deep learning
Challenges of Deep learning
Demo & discussion: Sentiment analysis using LSTM
Fullcycle of a deep learning project
Key Takeaways
Knowledge Check

Lesson 4 - Deep Neural Network & Tools

Deep Neural Network : why and applications
Designing a Deep neural network
How to choose your loss function?
Tools for Deep learning models
Keras and its Elements
Demo Code: Build a deep learning model using Keras (Assisted)
Tensorflow and Its ecosystem
Demo Code: Build a deep learning model using Tensorflow (Assisted)
TFlearn
Pytorch and its elements
Key Takeaways
Knowledge Check
Lesson-end Project: Build a deep learning model using Pytorch with Cifar10 dataset

Lesson 5 - Deep Neural Net optimization, tuning, interpretability

Optimization algorithms
SGD, Momentum, NAG, Adagrad, Adadelta , RMSprop, Adam
Batch normalization
Demo Code: Batch Normalization (Assisted)
Exploding and vanishing gradients
Hyperparameter tuning
Interpretability
Key Takeaways
Knowledge Check
Lesson-end Project: Hyperparameter Tunning With Keras Tuner

Lesson 6 - Convolutional Neural Network

Success and history
CNN Network design and architecture
Demo code: CNN Image Classification (Assisted)
Deep convolutional models
Key Takeaways
Knowledge Check
Lesson-end Project: Image Classification

Lesson 7 - Recurrent Neural Networks

Sequence data
Sense of time
RNN introduction
LSTM ( retail sales dataset kaggle)
Demo code: Stock Price Prediction with LSTM (Assisted)
Demo code: Multiclass Classification using LSTM (Unassisted)
Demo code: Sentiment Analysis using LSTM (Assisted)
GRUs
LSTM Vs GRUs
Key Takeaways
Knowledge Check
Lesson-end Project: Stock Price Forecasting

Lesson 8 - Autoencoders

Introduction to Autoencoders
Applications of Autoencoders
Autoencoder for anomaly detection
Demo code: Autoencoder model for MNIST data (Assisted)
Key Takeaways
Knowledge Check
Lesson-end Project: Anomaly detection with Keras

Section 3 - Practice Projects

Practice Projects

PUBG Players Finishing Placement Prediction

Math Refresher

Lesson 01: Course Introduction

1.01 About Simplilearn
1.02 Introduction to Mathematics
1.03 Types of Mathematics
1.04 Applications of Math in Data Industry
1.05 Learning Path
1.06 Course Components

Lesson 02: Probability and Statistics

2.01 Learning Objectives
2.02 Basics of Statistics and Probability
2.03 Introduction to Descriptive Statistics
2.04 Measures of Central Tendencies
2.05 Measures of Asymmetry
2.06 Measures of Variability
2.07 Measures of Relationship
2.08 Introduction to Probability
2.09 Key Takeaways
2.10 Knowledge check

Lesson 03: Coordinate Geometry

3.01 Learning Objectives
3.02 Introduction to Coordinate Geometry
3.03 Coordinate Geometry Formulas
3.04 Key Takeaways
3.05 Knowledge Check

Lesson 04: Linear Algebra

4.01 Learning Objectives
4.02 Introduction to Linear Algebra
4.03 Forms of Linear Equation
4.04 Solving a Linear Equation
4.05 Introduction to Matrices
4.06 Matrix Operations
4.07 Introduction to Vectors
4.08 Types and Properties of Vectors
4.09 Vector Operations
4.10 Key Takeaways
4.11 Knowledge Check

Lesson 05: Eigenvalues Eigenvectors and Eigendecomposition

5.01 Learning Objectives
5.02 Eigenvalues
5.03 Eigenvectors
5.04 Eigendecomposition
5.05 Key Takeaways
5.06 Knowledge Check

Lesson 06: Introduction to Calculus

6.01 Learning Objectives
6.02 Basics of Calculus
6.03 Differential Calculus
6.04 Differential Formulas
6.05 Integral Calculus
6.06 Integration Formulas
6.07 Key Takeaways
6.08 Knowledge Check

Admission details

Filling the form

For the application process, follow the steps below:

Visit the official website of the providers.
Click on Enroll now button and it will redirect to a new page.
If applicants have a coupon then they have to apply this or just click on the Proceed button.
Fill in the necessary learner's details like name, email, and contact number and proceed.
Pay the necessary fee and save the receipt of the transaction.

Evaluation process

The Deep Learning course with (Keras and Tensorflow) certification training certification exam contains five categories and students will have to complete these five models, one from each category.

Deep Learning course (with Keras and Tensorflow) certification training by Simplilearn exam duration will be five hours. The TensorFlow Developer certification exam costs INR 7,432 or USD $100, which includes three exam attempts.

How it helps

Upon completion of the Deep Learning course with (Keras and Tensorflow) certification training, participants will acquire an industry-recognized course completion certificate with lifelong validity after the completion of the course. You can find lucrative roles as a data scientist or a machine learning engineer in diverse industries such as healthcare, information technology, fin-tech, and e-commerce. As a certified Deep Learning professional, you can earn up to 13 Lakhs per annum.

In fact, several top recruiters such as Accenture, Oracle, Walmart, NVIDIA, Microsoft, and many more are always on the lookout for certified Data Learning professionals so you can get opportunities without fail.

FAQs

How many attempts can I take to pass the Deep Learning course with (Keras and Tensorflow) certification training exam?

You can attempt the Deep Learning course with (Keras and Tensorflow) certification training exam up to three times.

What are the prerequisites for the Deep Learning course with (Keras and Tensorflow) certification training?

There are no prerequisites for the Deep Learning course (with Keras and Tensorflow) certification training by Simplilearn; however knowledge of programming, statistics, and machine learning will help.

What is the Deep Learning course (with Keras and Tensorflow) certification online course?

The certification training course familiarises you with the language and the elementary concepts of deep learning and you learn how to create deep learning models.

Will I receive the Deep Learning course (with Keras and Tensorflow) certification training certificate after I complete the training online course?

Yes, you will receive the completion certificate after you finish the online self-learning training, practical exercises, and the two on-hand industry-based projects.

What is the exam duration of TensorFlow Developer certification?

You will have 5 hours to complete and submit the exam.

In how many attempts do I have to pass Tensorflow Developer certification exam?

You have three attempts to pass the TensorFlow Developer exam.

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Deep Learning Course (with Keras and Tensorflow) Certification Training

Online

Quick Facts

Course overview

The highlights

Program offerings

Course and certificate fees

certificate availability

certificate providing authority

Who it is for

Eligibility criteria

What you will learn

The syllabus

Section 1 - Deep Learning with Keras and Tensorflow (IBM)

Lesson 01: Deep Learning with Keras and Tensorflow (IBM)

Section 2 - Deep Learning with Keras and Tensor Flow (Live Classes)

Lesson 1 - Course introduction

Introduction

Accessing Practice Lab

Lesson 2 - AI and Deep learning introduction

What is AI and Deep learning

Brief History of AI

Recap: SL, UL and RL

Deep learning : successes last decade

Demo & discussion: Self driving car object detection

Applications of Deep learning

Challenges of Deep learning

Demo & discussion: Sentiment analysis using LSTM

Fullcycle of a deep learning project

Key Takeaways

Knowledge Check

Lesson 3 - Artificial Neural Network

Biological Neuron Vs Perceptron

Shallow neural network

Training a Perceptron

Demo code: Perceptron ( linear classification) (Assisted)

Backpropagation

Role of Activation functions & backpropagation

Demo code: Backpropagation (Assisted)

Demo code: Activation Function (Unassisted)

Optimization

Regularization

Dropout layer

Key Takeaways

Knowledge Check

Lesson-end Project (MNIST Image Classification)

Lesson 4 - Deep Neural Network & Tools

Deep Neural Network : why and applications

Designing a Deep neural network

How to choose your loss function?

Tools for Deep learning models

Keras and its Elements

Demo Code: Build a deep learning model using Keras (Assisted)

Tensorflow and Its ecosystem

Demo Code: Build a deep learning model using Tensorflow (Assisted)

TFlearn

Pytorch and its elements

Key Takeaways

Knowledge Check

Lesson-end Project: Build a deep learning model using Pytorch with Cifar10 dataset

Lesson 5 - Deep Neural Net optimization, tuning, interpretability

Optimization algorithms

SGD, Momentum, NAG, Adagrad, Adadelta , RMSprop, Adam

Batch normalization

Demo Code: Batch Normalization (Assisted)

Exploding and vanishing gradients

Hyperparameter tuning

Interpretability

Key Takeaways

Knowledge Check

Lesson-end Project: Hyperparameter Tunning With Keras Tuner

Lesson 6 - Convolutional Neural Network

Success and history

CNN Network design and architecture

Demo code: CNN Image Classification (Assisted)

Deep convolutional models

Key Takeaways

Knowledge Check

Lesson-end Project: Image Classification