Certificate in Deep Learning with TensorFlow by Vskills: Fee, Duration, How to Apply

Quick Facts

particular	details
Medium of instructions English	Mode of learning Self study	Mode of Delivery Video and Text Based

Course overview

Many of the companies, all around the world, face a huge mountain of data as the world progresses and transforms more into an open book. The issue with it is that it has made evaluating that data and making a scheme on it that much difficult with its quantity.

Deep Learning solves this issue by processing all that data in a better and faster way even if their network structures are complex in nature. However, there are not many people who can design Deep Learning models and train them for such a task.

This is where Certificate in Deep Learning with TensorFlow comes in. The course by Vskills teaches the candidates how they can tailor their Deep Learning models and measure them to the task of processing larger datasets and go through them even if they are complex in nature. Furthermore, it teaches the applicants the techniques behind the distribution of work in both high and low TensorFlow.

Course and certificate fees

Fees information

₹ 3,499

The fee amount for this programme is Rs. 3,499.

Certificate in Deep Learning with Tensor Flow Fees Details

Head	Amount
Programme Fees	Rs. 3,499

certificate availability

Yes

certificate providing authority

Vskills

Who it is for

Data scientist

The certificate in Deep Learning with Tensor Flow is ideal for:

Job seekers willing to work with MNCs, PSUs and IT companies
Data science professionals
Students and professionals across different industries

Eligibility criteria

Certification Qualifying Details

The final exam will qualify the applicants for the certificate. The exam will be conducted online and will be an hour long. The qualifying percentage of the exam will be 50% and the total score of the exam would be 50 marks. There will not be negative marking.

What you will learn

Knowledge of deep learning

When the applicants have finished their work, they would have a firm understanding of the following topics:

First of all, they would be introduced to Keras and how to design and compile a model using it.
After that, they will be introduced to TensorFlow and Tensor Board and learn about the types of Parallelism.
Then, they will learn the role of Google Cloud Machine Learning Engine and how to set up an Estimator.
They will then learn Visualization and face recognition systems.
Then the candidates will learn the know how behind optimizing functions.
Finally, the applicants will learn how to train Generative Adversarial Networks.

The syllabus

Installation

Software Installation

Keras introduction

Introduction
Keras Backends
Design and Compile a Model
Model Training, Evaluation, and Prediction
Training with Data Augmentation
Training with Transfer Learning and Data Augmentation

Scaling deep learning using Keras and TensorFlow

Introduction to TensorFlow
Introduction to TensorBoard
Types of Parallelism in Deep Learning – Synchronous and Asynchronous
Distributed TensorFlow
Configuring Keras to use TensorFlow for Distributed Problems

Training, tuning and serving our model in the cloud

Introduction
Introduction to Google Cloud Machine Learning Engine
Datasets, Feature Columns, and Estimators
Representing Data in TensorFlow
Quick Dive into TensorFlow Estimators
Creating Data Input Pipelines
Setting Up Our Estimator
Packaging Our Model
Training with Google Cloud ML Engine
Hyperparameter Tuning in the Cloud
Deploying Our Model for Prediction
Creating Our Prediction API

Setting up the Deep Learning Background

TensorFlow for Building Deep Learning Models
Basic Syntaxes, Function Optimization, Variables, and Placeholders
TensorBoard for Visualization

Training deep feed-forward neural networks with TensorFlow

Start by Loading the Imported Dataset
Building the Layers of the Neural Network in TensorFlow
Optimizing the Softmax Cross Entropy Function
Using DNN Predicting Whether Breast Cancer Cells Are Benign or Not

Applying CNN on two real datasets

Importing the Two Datasets Using TensorFlow and Sklearn API
Writing the TensorFlow Code to Add Convolutional and Pooling Layers
Using tf.train.AdamOptimizer API to Optimize CNN
Implementing CNN to Create a Face Recognition System

Exercise RNN to solve two time series problems

Understanding the RNN and the Need for LSTM
Implementing RNN
Monthly Riverflow Prediction of Turtle River in Ontario
Implement LSTM Project to Predict Decimal Number of Given Binary Representation

Using autoencoders to efficiently represent data

Encoder and Decoder for Efficient Data Representation
TensorFlow Code Using Linear Autoencoder to Perform PCA on a 4D Dataset
Using Stacked Autoencoders for Representation on MNIST Dataset
Build a Deep Autoencoder to Reduce Latent Space of LFW Face Dataset

Generative adversarial network for creating synthetic data

Generative Adversarial Networks for Creating Synthetic Dataset
Downloading and Setting Up the (Microsoft Research Asia) Geolife Project Dataset
Coding the Generator and Discriminator Using TensorFlow
Training GANs to Create Synthetic GPS Based Trajectories

Admission details

The process of the admission begins with candidates registering for the programme after which they have given the content materials for the certification exam. The candidates then prepare for the exam and give it. If they qualify, they receive the desired certificate.

Filling the form

Candidates who are interested in the programme will have to go through the below-mentioned process to get admission to the course.

Step 1: The first step is to visit the official website of the course.

Step 2: When on the website, the candidates will have to find and click the icon saying Buy Now.

Step 3: After the candidates have done that, they have to register on the website and log in using the account.

Step 4: Next step would be to choose and confirm the programme they want to take part in.

Step 5: They will then have to choose the method they will pay with for the course.

Step 6: Finally, the candidates are required to make payment for the course.

Evaluation process

To get a Certificate in Deep Learning with TensorFlow, the candidate must take the test and secure as many as 50 percent marks or more.

How it helps

The course teaches the candidates how to design and train deep learning models for it to evaluate complex datasets that are great in size. The recipients of the course will be able to make accurate decisions on high dimensional data because of the processed units for featured extractions and large volume data transformation.

A certificate in such a course will come in very handy for professionals or job seekers as the course is although relatively rare, its use is quite popular. The certificate ranks the applicants higher than most people in the competition list and increases their chance of selection for both job and promotion.

FAQs

How does the course work?

The course provides the preparation material for the exam that qualifies the candidates for the certification, and it is given after the candidates have registered on the website.

What does the course offer for preparation?

Besides the learning material which the course provides in both soft and hard copy, it also offers mock test papers, sample question papers and tutorials for better preparation.

How will the certification exam be conducted?

The final exam of certification will be held online for the convenience of the applicants. When the participants feel as if they are ready for the exam they can give it from any place in the world at any time.

Which companies hire Deep Learning professionals certified from VSkills?

Consultancies, MNCs and IT Companies like JP Morgan Chase, Wipro, Capgemini, Zensar, Infor, TCS and many others like them hire Deep learning professionals from VSkills.

What are the specialities of the course?

The course offers a government certification and the certification is valid for life. Furthermore, the course provides access to its e-learning materials for life. The tag of VSkills certified is also very valuable.