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Deep Learning Course (with Keras & TensorFlow) Certification Training

Course Description

34 hours

Deep Learning Course (with Keras & TensorFlow) Certification Training

This Deep Learning course with Tensorflow certification training is developed by industry leaders and aligned with the latest best practices. You’ll master deep learning concepts and models using Keras and TensorFlow frameworks and implement deep learning algorithms, preparing you for a career as Deep Learning Engineer.

Deep Learning Course Overview

In this Deep Learning course with Keras and Tensorflow certification training, you will become familiar with the language and fundamental concepts of artificial neural networks, PyTorch, autoencoders, and more. Upon completion, you will be able to build deep learning models, interpret results, and build your own deep learning project.

Eligibility

Demand for skilled Deep Learning Engineers is booming across a wide range of industries, making this Deep Learning course with Keras and Tensorflow certification training well-suited for professionals at the intermediate to advanced level. We recommend this course particularly for Software Engineers, Data Scientists, Data Analysts, and Statisticians with an interest in deep learning.

Pre-requisites

Participants in this Deep Learning online course should have familiarity with programming fundamentals, a fair understanding of the basics of statistics and mathematics, and a good understanding of machine learning concepts.

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Course Syllabus

Lesson 1 – Welcome!

1.1 Welcome!
1.2 Learning Objectives

Lesson 2 – Introduction to Tensorflow

2.1 Learning Objectives
2.2 Introduction to TensorFlow
2.3 TensorFlow’s Hello World
2.4 Tensorflow Hello World
2.5 Linear Regression With Tensorflow
2.6 Logistic Regression With Tensorflow
2.7 Activation Functions
2.8 Intro to Deep Learning
2.9 Deep Neural Networks

Lesson 3 – Convolutional Networks

3.1 Learning Objectives
3.2 Intro to Convolutional Networks
3.3 CNN for Classifications
3.4 CNN Architecture
3.5 Understanding Convolutions
3.6 CNN with MNIST Dataset

Lesson 4 – Recurrent Neural Network

4.1 Learning Objectives
4.2 The Sequential Problem
4.3 The RNN Model
4.4 The LSTM Model
4.5 Applying RNNs to Language Modeling
4.6 LTSM Basics
4.7 MNIST Data Classification With RNN/LSTM
4.8 Applying RNN/LSTM to Language Modelling
4.9 Applying RNN/LSTM to Character Modelling

Lesson 5 – Restricted Boltzmann Machines (RBM)

5.1 Learning Objectives
5.2 Intro to RBMs
5.3 Training RBMs
5.4 RBM MNIST
5.5 Collaborative Filtering With RBM

Lesson 6 – Autoencoders

6.1 Learning Objectives
6.2 Intro to Autoencoders
6.3 Applying RNNs to Language Modelling
6.4 Autoencoders
6.5 DBN MNIST

Lesson 7 – Course Summary

7.1 Course Summary
Unlocking IBM Certificate

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 course online will help you differentiate between Deep Learning, Machine Learning, and Artificial Intelligence.

Once you complete the Deep Learning course (with Keras and Tensorflow) certification training, 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 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.

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

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 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.

$495.00

course Syllabus
  • Vast selection of courses and labs Access
  • Unlimited access from all devices
  • Learn from industry expert instructors
  • Assessment quizzes and monitor progress
  • Vast selection of courses and labs Access
  • Blended Learning with Virtual Classes
  • Access to new courses every quarter
  • 100% satisfaction guarantee

You Will Get Certification After Completetion This Course.

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Frequently Asked Questions

How does online education work on a day-to-day basis?
Instructional methods, course requirements, and learning technologies can vary significantly from one online program to the next, but the vast bulk of them use a learning management system (LMS) to deliver lectures and materials, monitor student progress, assess comprehension, and accept student work. LMS providers design these platforms to accommodate a multitude of instructor needs and preferences.
Is online education as effective as face-to-face instruction?
Online education may seem relatively new, but years of research suggests it can be just as effective as traditional coursework, and often more so. According to a U.S. Department of Education analysis of more than 1,000 learning studies, online students tend to outperform classroom-based students across most disciplines and demographics. Another major review published the same year found that online students had the advantage 70 percent of the time, a gap authors projected would only widen as programs and technologies evolve.
Do employers accept online degrees?
All new learning innovations are met with some degree of scrutiny, but skepticism subsides as methods become more mainstream. Such is the case for online learning. Studies indicate employers who are familiar with online degrees tend to view them more favorably, and more employers are acquainted with them than ever before. The majority of colleges now offer online degrees, including most public, not-for-profit, and Ivy League universities. Online learning is also increasingly prevalent in the workplace as more companies invest in web-based employee training and development programs.
Is online education more conducive to cheating?
The concern that online students cheat more than traditional students is perhaps misplaced. When researchers at Marshall University conducted a study to measure the prevalence of cheating in online and classroom-based courses, they concluded, “Somewhat surprisingly, the results showed higher rates of academic dishonesty in live courses.” The authors suggest the social familiarity of students in a classroom setting may lessen their sense of moral obligation.
How do I know if online education is right for me?
Choosing the right course takes time and careful research no matter how one intends to study. Learning styles, goals, and programs always vary, but students considering online courses must consider technical skills, ability to self-motivate, and other factors specific to the medium. Online course demos and trials can also be helpful.
What technical skills do online students need?
Our platform typically designed to be as user-friendly as possible: intuitive controls, clear instructions, and tutorials guide students through new tasks. However, students still need basic computer skills to access and navigate these programs. These skills include: using a keyboard and a mouse; running computer programs; using the Internet; sending and receiving email; using word processing programs; and using forums and other collaborative tools. Most online programs publish such requirements on their websites. If not, an admissions adviser can help.

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