Deep Learning with Tensor Flow 2.0 Certification Training
CertOcean’s Deep Learning with Tensor flow course is developed and
taught by experienced industry professionals in verse with the market's
modern academic requirements and demands. The deep learning course is
for learners who want to master popular algorithms like the CNN, LSTM,
RCNN, RNN, and RBM using the Tensor Flow 2.0 package in Python. You
will work on several real-time projects during this course duration,
including Emotion and Gender Detection, Auto image captioning using CNN,
LSTM, and many others. This Tensorflow certification course provides
and upliftment and nourishment to your career and helps you explore a
much more profitable career opportunity.
Why should you go for this Deep Learning with Tensor flow certification?
*
Owing to its prominent name in the market, TensorFlow 2.0 is a widely
used Deep Learning library, wholly developed and managed by Google.
*
This Deep Learning course from Cert Ocean includes Keras, which is now
integrated with TensorFlow 2.0, making it more powerful and modern.
*
As per job boards, the average salary of professionals with TensorFlow
certification is approximately $113,000 per year. This annual salary is
generous for people looking to achieve a hike in their current pay
scale.
Course Curriculum
Learning Objective: In this module, you will study the concepts of Deep learning and learn how it differs from Machine Learning.
Topics Covered:
* 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
Topics Covered:
* Introduction to TensorFlow 2.x
* Installing TensorFlow 2.x
* Defining Sequence model layers
* Activation Function
* Layer Types
* Model Compilation
* Model Optimizer
* Model Loss Function
* Model Training
* Digit Classification employing the Simple Neural Network in TensorFlow 2.x
* Improving the model
* Adding Hidden Layer
* Adding Dropout
* Using Adam Optimizer
Topics Covered:
* 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
Topics Covered:
* 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
Topics Covered:
* What is the Boltzmann Machine (BM)?
* Identify the issues with BM.
* Why did RBM come into the picture?
* Step by step implementation of RBM
* Distribution of Boltzmann Machine
* Understanding Autoencoders
* Architecture of Autoencoders
* Brief on types of Autoencoders
* Applications of Autoencoders
Topics Covered:
* 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
Topics Covered:
* Where can you use Emotion & Gender Detection?
* How does it work?
* Emotion Detection architecture
* Face/Emotion detection using Haar Cascades
* Implementation on Colab
Topics covered:
* Where do we use Emotion & Gender Detection?
* How does it work?
* Emotion Detection architecture
* Face/Emotion detection using Haar Cascades
* Implementation on Colab
Topics Covered:
* 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 the current time step
Topics Covered:
* Auto Image Captioning
* COCO dataset
* Pre-trained model
* Inception V3 model
* The architecture of Inception V3
* Modify the last layer of the pre-trained model
* Freeze model
* CNN for image processing
* LSTM or text processing
Course Description
This course teaches you everything you need to know about TensorFlow and its related context. Moreover, the deep learning course is known for shaping the future of professionals, allowing them to achieve high skills and make good money. This course is perfect for people who are looking to move up the ladder in their professional careers.
* The course includes algorithms dependent on the most recent TensorFlow 2.0
* Keras is presently incorporated with TensorFlow 2.0, accordingly making it all the more remarkable.
* Composing codes in TensorFlow is substantially simpler when contrasted with the past adaptation.
* TensorFlow 2.0 is currently the most generally utilized library for Deep Learning.
* The course will give you a combined knowledge of text and picture processing.
* Get yourself presented and prepared with TensorFlow 2.0.
* Comprehend the idea of Single-Layer and Multi-Layer Perceptron by actualizing them in Tensorflow 2.0
* Find out about the working of CNN calculation and order the picture utilizing the prepared model.
* Handle the ideas on significant points like Transfer Learning, RCNN, Fast RCNN, RoI Pooling, Faster RCNN, and Mask R-CNN
* Comprehend the idea of Boltzmann machine and Auto Encoders
* Actualize Generative Adversarial Network in TensorFlow 2.0
* Work on Emotion and Gender Detection extend and reinforce your expertise on OpenCV and CNN.
* Comprehend the idea of RNN, GRU, and LSTM
* Perform Auto-Image Captioning utilizing CNN and LSTM
We curate this Deep Learning course for all those professionals who want to study Deep Learning and want to make their future in the same field, as a Deep Learning Engineer or a Data Scientist. This TensorFlow certification is best suited for professions who are:
* Developers are positioning their career as a 'Data Scientist.'
* Analytical Managers who are driving a group of experts
* Business Analysts who need to learn and understand Deep Learning Techniques
* Information Architects who need to pick up aptitude in Predictive Analytics
* Analysts needing to comprehend Data Science philosophies and methodologies
The following are some case studies and demos that are a part of the Deep learning course.
? Arranging manually written digits utilizing TensorFlow 2.0
? Arrange the pictures of style dataset into various classifications utilizing Multiple
Layer Perceptron
? Arranging Dog and Cat utilizing CNN in TensorFlow 2.0
? Use CNN to arrange each face dependent on the outward appearance.
? Comprehend the idea of Transfer Learning
? Perform object recognition utilizing RCNN
? Perform picture denoising utilizing the Autoencoders
? Perform Emotion and sex location utilizing OpenCV and CNN
? Use CNN and LSTM to perform Auto Image Captioning.
Features
Instructor-led Live Sessions
30 Hours of Online Live Instructor-led Classes. Training Schedule: 10 sessions of 3 hours each.
Assignments
Each class will be followed by practical assignments.
Lifetime Access
Students will get lifetime access to all the course materials where presentations, quizzes, installation guides, and class recordings are available.
24/7 Expert Support
We provide 24/7 support to all the students, thereby resolving technical queries.
Certification
Once you complete your final project, you will receive the deep learning with a tensor flow from CertOcean.
Frequently Asked Questions (FAQs):
Candidates will never miss lectures in CERTOCEAN's deep learning course as they can either view the recorded session or attend the next live batch.
