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Deep Learning Course with TensorFlow Certification
Deep Learning Course with TensorFlow Certification
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Live Online Classes starting on 17th Dec 2022
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Why enroll for Deep Learning Training - TensorFlow Certification?
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Why Deep Learning Training - TensorFlow Certification from edureka!
Live Interactive Learning
- World-Class Instructors
- Expert-Led Mentoring Sessions
- Instant doubt clearing
Lifetime Access
- Course Access Never Expires
- Free Access to Future Updates
- Unlimited Access to Course Content
24x7 Support
- One-On-One Learning Assistance
- Help Desk Support
- Resolve Doubts in Real-time
Hands-On Project Based Learning
- Industry-Relevant Projects
- Course Demo Dataset & Files
- Quizzes & Assignments
Industry Recognised Certification
- Edureka Training Certificate
- Graded Performance Certificate
- Certificate of Completion
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About your Deep Learning Training - TensorFlow Certification
Skills Covered
- Python Programming
- Image Classification
- Image Processing
- Text Processing
- Collaborative Filtering
- Sentiment Analysis
Tools Covered
Deep Learning with TensorFlow Course Curriculum
Curriculum Designed by Experts
Introduction to Deep Learning
11 Topics
Topics:
- 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
Hands-on:
- Single Layer Perceptron
Skills You will Learn:
- Curse of Dimensionality
- Single Layer Perceptron
Getting Started with TensorFlow 2.0
14 Topics
Topics:
- 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 using Simple Neural Network in TensorFlow 2.x
- Improving the model
- Adding Hidden Layer
- Adding Dropout
- Using Adam Optimizer
Hands-on:
- Classifying handwritten digits using TensorFlow 2.0
Skills You will Learn:
- Installing and Working with TensorFlow 2.0
Convolution Neural Network
12 Topics
Topics:
- 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
Hands-on:
- Saving and Loading a Model
- Face Detection using OpenCV
Skills You will Learn:
- Image Classification using CNN
- Face Detection using OpenCV
Regional CNN
20 Topics
Topics:
- 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
Hands-on:
- Transfer Learning
- Object Detection
Skills You will Learn:
- Transfer Learning
- Object Detection
- Mask R-CNN
Boltzmann Machine & Autoencoder
9 Topics
Topics:
- 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
Hands-on:
- Implement RBM
- Simple encoder
Skills You will Learn:
- RBM
- Autoencoders
Generative Adversarial Network(GAN)
7 Topics
Topics:
- 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
Hands-on:
- Implement Generative Adversarial Network
Skills You will Learn:
- Generative Adversarial Network
Boltzmann Machine & Autoencoder
9 Topics
Topics:
- 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
Hands-on:
- Implement RBM
- Simple encoder
Skills You will Learn:
- RBM
- Autoencoders
Emotion and Gender Detection
5 Topics
Topics:
- Where do we use Emotion and Gender Detection?
- How does it work?
- Emotion Detection architecture
- Face/Emotion detection using Haar Cascade
- Implementation on Colab
Hands-on:
- Implement Emotion and Gender Detection
Skills You will Learn:
- Emotion and Gender Detection
Introduction RNN and GRU
14 Topics
Topics:
- 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
Hands-on:
- Implement COVID RNN GRU
Skills You will Learn:
- RNN
- GRU
LSTM
18 Topics
Topics:
- 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
Hands-on:
- Intent Detection using LSTM
Skills You will Learn:
- LSTM
- Sequence Prediction
- Sequence Classification
- Sequence Generation
Auto Image Captioning Using CNN LSTM
9 Topics
Topics:
- 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
Hands-on:
- Auto Image Captioning
Skills You will Learn:
- Auto Image Captioning
- CNN for image processing
- LSTM or text processing
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Deep Learning Certification Description
About the Deep Learning with TensorFlow Course
Edureka's Deep Learning with TensorFlow course focuses on step by step guide to Deep Learning concepts with extensive hands-on using Python’s open-source software library for machine learning and artificial intelligence: TensorFlow. This Online Deep Learning course has been packed up with a lot of real-life examples, where you can apply the learned content to use.
It is designed to help you understand the important concepts and techniques used in Deep Learning. You will be able to build your own deep learning model for image and text classification. Towards the end of this Deep Learning with TensorFlow Certification course, we will be discussing various practical use cases of Deep Learing algorithms such as CNN, RNN, and LSTM to enhance your learning experience.
Why Should you go for Deep Learning Training with TensorFlow Certification ?
If you are good in Python and want to start your journey towards the path of a Data Scientist then this course is definitely for you
What are the objectives of our Deep Learning with TensorFlow Training?
After completing this Deep Learning with TensorFlow Certification course, you will be able to:
Edureka offers the best online course for Deep Learning. Enroll now with our Deep Learning with TensorFlow training and get a chance to learn from industrial giants.
Who should go for this Deep Learning with TensorFlow online course?
What are the pre-requisites for this Deep Learning Certification Course?
The pre-requisites for Edureka's Deep Learning with TensorFlow course include the basic programming knowledge in Python and fundamental understanding of Machine Learning. However, you will be provided with complimentary self-paced modules as a pre-requisite in your LMS once you enroll in this course.
Why should you consider our Deep Learning with TensorFlow course?
Deep Learning with TensorFlow Training will hone your skills by offering you comprehensive knowledge on Deep Learning in TensorFlow. It will also acquaint you with the required hands-on experience for solving real-time industry-based Deep Learning projects. This Deep Learning with TensorFlow course from Edureka will teach you the essential concepts from scratch and enable you to launch your dream career in this domain.
Why Learn Deep Learning with TensorFlow?
TensorFlow, integrated with Keras, is a game-changer for the future of Artificial Intelligence. TensorFlow makes it much easier for the company’s engineers to translate new approaches to artificial intelligence into practical code, improving services such as search and the accuracy of speech recognition. Deep Learning is one of the most accelerating and promising subfields of AI and Data Science. To become an expert in this technology, you need a structured training with the latest skills as per current industry requirements and best practices. Besides strong theoretical understanding, you need to work on various real-life data projects using different neural network architectures as a part of solution strategy.
What specific job roles will I apply for after completing this Deep Learning Training?
Several job roles are available in the recent market after completing this Deep Learning course. Some of the job roles are
How to enroll in this Deep Learning Training course online?
You can easily enroll through our website using your email address and Phone number. Our 24x7 Support team is helping you complete the enrollment and registration process. If you need clarification about this online Deep Learning Course, you can use the chatbots and Edureka support team will contact you immediately.
What are the modules I can learn from this Online NLP training course?
Edureka’s online Deep Learning with TensorFlow course modules are
What are the system requirements for our Deep Learning Certification Training?
Any system with 4GB of RAM and a decent HDD with the latest version of Windows, Linux, or MacOS is compatible. You don’t have to worry about the system requirements as you will be doing your practicals on a Cloud LAB environment. This environment already contains all the necessary software that will be required to execute your practicals.
How will I execute the practicals?
You will do your Assignments/Case Studies using Jupyter Notebook that is already installed on your Cloud LAB environment whose access details will be available on your LMS. You will be accessing your Cloud LAB environment from a browser. For any doubt, the 24*7 support team will promptly assist you.
Deep Learning with TensorFlow Certification Projects
Industry: Healthcare
Problem Statement: Create a Face Mask Detector using CNN and OpenCV trained on the set of 1376 images consisting of two classes – with_mask and without_mask.Industry: Social Media
Problem Statement: Build a model using Keras to do sentiment analysis on twitter data reactions on GOP debate in Ohio.Deep Learning Certification
To unlock the Edureka’s Deep Learning with TensorFlow Training course completion certificate, you must ensure the following:
- Completely participate in this Deep Learning with TensorFlow Training Course
- Evaluation and completion of the assessments and projects listed.
- You must complete the course and earn a minimum score of 80% in the assessment.
The demand for Deep Learning has grown immensely in the recent years as Artificial Intelligence, Machine Learning, and Data Science continue to gain popularity among businesses globally. Deep Learning Engineer has become one of the most lucrative career options, with ample opportunities. Deep Learning has gained much traction in today's time because of its computational models that are composed of multiple processing layers, the networks can create multiple levels of abstraction to represent the data. Professionals interested in advancing their careers in AIML and Data Science can do so by earning a Deep Learning certification.
Beginners with a working knowledge of Python programming and familiarity with Machine Learning algorithms will find it easy to graso the concepts of Deep Learning which requires an appropriate direction and a well-structured training path. Beginners interested in a career in Deep Learning can sign up for our training and earn certificates to demonstrate their expertise in this domain.
The demand for Deep Learning is on the rise and there are many profitable employment possibilities and positions in tech organizations, making this the ideal time for candidates to enroll and earn this certification. Due to the wide range of job options and prospects available in the field of AIML and Data Science, learning Deep Learning with TensorFlow will enable you to become a well-rounded data scientist and AI professional.
Our Deep Learning with TensorFlow certification course is designed to develop skills and evaluate candidates' knowledge. Following the completion of this certification, you will have access to a wide range of job possibilities. Some of the most important employment roles include Deep Learning Engineer, Data Scientist, ML Engineer, Data Engineer, Data Analyst, Analytics Manager, and others.
Please visit the page which will guide you through the Top Deep Learning Interview Questions and Answers.
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Deep Learning Course FAQ
What if I miss Deep Learning Training class?
"You will never miss a lecture at Edureka! You can choose either of the two options:
- View the recorded session of the class available in your LMS.
- You can attend the missed session, in any other live batch."
What if I have queries after I complete this Deep Learning Certification Training course?
Your access to the Support Team is for a lifetime and will be available 24/7. The team will help you in resolving queries, during and after the Deep Learning course.
How soon after Signing up would I get access to the Deep Learning with TensorFlow Certification Course Content?
Post-enrolment to deep Learning course, the LMS access of deep learning with TensorFlow course will be instantly provided to you and will be available for a lifetime. You will be able to access the complete set of previous class recordings, PPTs, PDFs, assignments of our best deep learning course. Moreover, access to our 24x7 support team will be granted instantly as well. You can start learning right away after signing up for the deep learning online course.
Is the course material accessible to the students even after the Deep learning Certification course is over?
Yes, access to this Deep Learning course material will be available for a lifetime once you have enrolled in the Deep Learning course.
Will I get placement assistance?
More than 70% of Edureka Learners have reported change in job profile (promotion), work location (onsite), lateral transfers & new job offers. Edureka's certification is well recognized in the IT industry as it is a testament to the intensive and practical learning you have gone through and the real-life projects you have delivered in the deep learning online course.
How much do deep learning engineers make?
According to Glassdoor, Deep Learning Engineer’s average salary in India is around 8.3 Lakhs per annum.
How useful is Deep Learning?
Deep Learning is highly useful for analyzing and understanding large amounts of Data which makes the process much less time-consuming for organizations. To learn more check our deep learning syllabus.
What are some practical examples to demonstrate the usage of Deep Learning?
Deep Learning is used for Virtual Assistants, AI, new age technologies like Self Driving Cars, Disaster Management , Satellite Imagery and much more.
How much does the TensorFlow Exam cost?
The TensorFlow Exam Cost is $100 with one exam attempt.
What is the duration of TensorFlow Examination?
The TensorFlow Exam is for 5 Hours.
What is the Validity of TensorFlow Certification?
The TensorFlow Certification is valid for 3 Years from the date the Digital Badge is issued.
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