Deep Learning with TensorFlow 2.0 Certification Training

Access Duration - 365 Days
4.5( 4 REVIEWS )
133 STUDENTS
£.00
 

What Will I Learn?

Understand the concept of Single Layer and Multi-Layer Perceptron by implementing them in Tensorflow 2.0
Learn about the working of CNN algorithm and classify the image using the trained model
Grasp the concepts on important topics like Transfer Learning, RCNN, Fast RCNN, RoI Pooling, Faster RCNN, and Mask RCNN
Understand the concept of RNN, GRU, and LSTM
Work on Emotion and Gender Detection project and strengthen your skill on OpenCV and CNN
Perform Auto-Image Captioning using CNN and LSTM

Overview

Deep Learning is a part of Machine Learning that focuses on algorithms that are inspired by the structure and function of the human brain. These algorithms are referred to as artificial neural networks. Neural networks learn to do things by considering examples, which is what we as humans do. Deep learning is behind a lot of the new technologies like driverless cars.

Tensorflow is Google’s library for deep learning and artificial intelligence. TensorFlow offers APIs that facilitates Machine Learning. TensorFlow also has a faster compilation time than other Deep Learning libraries such as Keras and Touch. TensorFlow supports both CPU and GPU computing devices.

In this ‘Deep Learning with TensorFlow 2.0 Certification Training’ course, you will be working on various real-time projects like Emotion and Gender Detection, Auto Image Captioning using CNN and LSTM, and so much more.

Why You Should Consider Taking this Course at Global Edulink?

Global Edulink is a leading online provider for several accrediting bodies, and provides learners the opportunity to take this exclusive course awarded by CPD. At Global Edulink, we give our fullest attention to our learners’ needs and ensure they have the necessary information required to proceed with the Course.  Learners who register will be given excellent support, discounts for future purchases and be eligible for a TOTUM Discount card and Student ID card with amazing offers and access to retail stores, the library, cinemas, gym memberships and their favourite restaurants.

  • Access Duration
  • Who is this Course for?
  • Entry Requirement
  • Method of Assessment
  • Certification
  • Awarding Body
  • Career Path & Progression

The course will be directly delivered to you, and you have 12 months access to the online learning platform from the date you joined the course.  The course is self-paced and you can complete it in stages, revisiting the lessons at any time.

  • Analytics Managers who are leading a team of analysts
  • Business Analysts who want to understand Deep Learning Techniques
  • Developers aspiring to be a 'Data Scientist'
  • Information Architects who want to gain expertise in Predictive Analytics
  • Analysts wanting to understand Data Science methodologies
 
  • Learners should be over the age of 16, and have a basic understanding of English, ICT and numeracy.
  • Basic programming knowledge in Python
  • Concepts about Machine Learning
In order to complete the course successfully, learners will take an online assessment. This online test is marked automatically, so you will receive an instant grade and know whether you have passed the course.
Upon the successful completion of the course, you will be awarded the ‘Deep Learning with TensorFlow 2.0 Training’ certificate by CPD.
CPD is an internationally recognised qualification that will make your CV standout and encourage employers to see your motivation at expanding your skills and knowledge in an enterprise.
Once you successfully complete the course, you will gain an accredited qualification that will prove your skills and expertise in the subject matter. With this qualification you can further expand your knowledge by studying related courses on this subject, or you can go onto get a promotion or salary increment in your current job role. Below given are few of the jobs this certificate will help you in, along with the average UK salary per annum according to http://payscale.com/
  • Business Analyst – Up to £58k per annum
  • Information Architect – Up to £55k per annum
  • Analytics Manager – Up to £88k per annum
 

Key Features

Gain an Accredited UK Qualification
Access to Excellent Quality Study Materials
Personalised Learning Experience
Support by Phone, Live Chat, and Email
Eligible for TOTUM Discount Card
UK Register of Learning Providers Reg No : 10053842

Course Curriculum

1: Introduction to Deep Learning
What is Deep Learning?
Curse of Dimensionality
Machine Learning vs. Deep Learning
Use cases of Deep Learning
Human Brain vs. Neural Network
What is Perceptron?
Learning Rate 
Epoch
Batch Size
Activation Function
Single Layer Perceptron
2: Getting Started with TensorFlow 2.0
Introduction to TensorFlow 2.x
Installing TensorFlow 2.x
Defining Sequence model layers
Activation Function
Layer Types
What is Convolution
Model Optimizer
Model Loss Function
Model Training
Digit Classification using Simple Neural Network in TensorFlow 2.x
Improving the model
Adding Hidden Layer
Adding Dropout
Using Adam Optimizer
3: Convolution Neural Network
Image Classification Example
What is Convolution
Convolutional Layer Network
Convolutional Layer
Filtering
ReLU Layer
Pooling
Data Flattening
Fully Connected Layer
Predicting a cat or a dog
Saving and Loading a Model
Face Detection using OpenCV
4: Regional CNN
Regional-CNN
Selective Search Algorithm
Bounding Box Regression
SVM in RCNN
Pre-trained Model
Model Accuracy 
Model Inference Time 
Model Size Comparison
Transfer Learning
Object Detection – Evaluation
mAP
IoU
RCNN – Speed Bottleneck
Fast R-CNN
RoI Pooling
Fast R-CNN – Speed Bottleneck
Faster R-CNN
Feature Pyramid Network (FPN)
Regional Proposal Network (RPN)
Mask R-CNN
5: Boltzmann Machine & Autoencoder
What is Boltzmann Machine (BM)?
Identify the issues with BM
Why did RBM come into picture?
Step by step implementation of RBM
Distribution of Boltzmann Machine
Understanding Autoencoders
Architecture of Autoencoders
Brief on types of Autoencoders 
Applications of Autoencoders
6: Generative Adversarial Network(GAN)
Which Face is Fake?
Understanding GAN
What is Generative Adversarial Network?
How does GAN work?
Step by step Generative Adversarial Network implementation
Types of GAN
Recent Advances: GAN
7: Emotion and Gender Detection
Where do we use Emotion and Gender Detection? $
How does it work?
Emotion Detection architecture
Face/Emotion detection using Haar Cascade
Implementation on Colab
8: Introduction RNN and GRU
Issues with Feed Forward Network
Recurrent Neural Network (RNN)
Architecture of RNN
Calculation in RNN
Backpropagation and Loss calculation
Applications of RNN
Vanishing Gradient
Exploding Gradient
What is GRU?
Components of GRU
Update gate
Reset gate
Current memory content
Final memory at current time step
9: LSTM
What is LSTM?
Structure of LSTM
Forget Gate
Input Gate
Output Gate
LSTM architecture
Types of Sequence-Based Model
Sequence Prediction
Sequence Classification
Sequence Generation
Types of LSTM
Vanilla LSTM
Stacked LSTM
CNN LSTM
Bidirectional LSTM
How to increase the efficiency of the model?
Backpropagation through time
Workflow of BPTT
10: Auto Image Captioning Using CNN LSTM
Auto Image Captioning
COCO dataset
Pre-trained model
Inception V3 model
Architecture of Inception V3
Modify last layer of pre-trained model
Freeze model
CNN for image processing
LSTM or text processing

Students feedback

4.5

Average rating (4)
4.5
5 Star
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1 Star
    W B

    Warren Burgess

    January 02, 2021
    Beautifully designed

    The course was beautifully designed and explained in a very good manner. Thank you very much for your effort. This also provides a great opportunity for the people to develop their career.

    B A

    Bailey Anderson

    December 01, 2020
    Worth the time

    Worth the time and efforts. Easy to follow and learn! Lessons were well structured with parallel accent on both theory and code related with the use of TensorFlow library.

    A B

    Abel Berry

    November 29, 2020
    Broad content

    The course content is comprehensive and my advise for students would be, plan to allocate some quality time if you want to complete this course.

    A H

    Aurelia Hill

    October 13, 2020
    Good pace of learning

    I enjoyed the pace of learning in this course, a bit daunting because it makes one realise how little one knows about this topic.

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