Deep Learning with Tensor Flow 2.0 Certification Training



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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

Learning Objective: In this module, you will study TensorFlow 2.0, its installation procedures, and build a simple neural network to predict handwritten digits.

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
Learning Objective: In this module, you will learn about CNN and explore much about the aspects.

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
Learning Objective: In this module, you will learn about the concept and working of RCNN and why it came into existence.

Topics Covered:
* Regional-CNN
* Selective Search Algorithm
* Bounding Box Regression
* 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
Learning Objective: In this module, you will learn about the Boltzmann Machine and its implementation.

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
Learning Objective: In this module, you will learn about the generative adversarial model and its implementation.

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
Learning Objective: In this module, you will learn how to classify each emotion into a facial expression.

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
Learning Objective: In this module, you will learn about the Feedforward network and recurrent neural network and how an RNN network works.

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
Learning Objective: IN this module, you will learn about the architecture of LSTM and the importance of gates in the LSTM.

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
Learning Objective: In this module, you will learn about how to implement Auto image using pre-trained model inception V3 and LSTM for processing the text.

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.

If you are good in Python and want to start a career in becoming a Data Scientist, this deep learning course is perfect for you.

* 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.
After this TensorFlow certification, you will be able to:
* 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

If you are opting for a deep learning course, the necessary prerequisites include programming knowledge in Python and concepts of Machine Learning. If professionals want to ace this TensorFlow certification, then they can develop skills in Python for AI-ML, and statistics, and machine learning.
You need a system with good internet connectivity. In any doubt, the 24*7 support line assists with all your queries and questions.

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.
For all the projects, you can use the lab environment created for the deep learning course certification.


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.

Our team is with each student 24/7. They need not worry about anything. Just ask your queries about the Tensorflow certification course, and we will make sure that it gets solved as soon as possible.
We hope that till now, you have seen any of our study clips. And we think that's all because you need not look further as we are good at keeping promises. We promise to enhance your growth in the automation field using deep learning course certification.
Only instructors who are experts in the domain and possess more than 10 years of experience are selected to teach after a stringent and tedious process. After shortlisting, all the instructors undergo a three months long training program.
Most of the CertOcean's learners have reported a hike in their salary and position after completing the Tensorflow certification. This training is well-recognized in the IT industry and indulges in both practical and theoretical learning.
We provide support to all the learners, even if they have completed their course training way before. Once you have registered with us, we will take care of all your educational needs and demands, resolving all your functional and technical queries.
CertOcean's Tensorflow certification course will assist you throughout the course and help you master the concepts and practical implementation of technology for the course duration.

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