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Weekdays Regular
(Class 1Hr - 1:30Hrs) / Per Session
Weekdays Regular
(Class 1Hr - 1:30Hrs) / Per Session
Weekend Regular
(Class 3hr - 3:30Hrs) / Per Session
Weekend Fasttrack
(Class 4:30Hr - 5:00Hrs) / Per Session
No Interest Financing start at ₹ 5000 / month
Top Skills You Will Gain
- LTSM Basics
- Linear Regression With Tensorflow
- Activation Functions
- Deep Neural Networks
- Convolutional Networks
- MNIST Data Classification
- Training RBMs
- Autoencoders, The RNN Model
Deep Learning Course with TensorFlow Course Key Features 100% Money Back Guarantee
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Deep Learning Course with TensorFlow Course Curriculam
Trainers Profile
Trainers are certified professionals with 10+ years of experience in their respective domains as well as they are currently working with Top MNCs. As all Trainers from Deep Learning Course with TensorFlow Course are respective domain working professionals so they are having many live projects, trainers will use these projects during training sessions.
Pre-requisites
Should have familiarity with programming fundamentals, a fair understanding of the basics of statistics and mathematics, and a good understanding of machine learning concepts.
Syllabus of Deep Learning Course with TensorFlow Online Course Download syllabus
- 1. Introduction to Deep Learning
- 2. Introduction to Numpy
- 3. Introduction to Tensorflow and Keras
- 1. Solution of Equations, row and column Interpretation
- 2. Vector Space Properties
- 3. Partial Derivative of Polynomial and Two conditions for Local Minima
- 4. Physical Interpretation of gradient (Direction of Maximum Change)
- 5. Matrix Vector Multiplication
- 6. EVD and interpretation of Eighen Vectors
- 7. Linear Independence and Rank of Matrix
- 8. Orthonormal Matrices, Projection Matrices, Vandemonde Matrix, Markov Matrix, Symmetric, Block Diagonal
- 1. Intuition behind Linear Regression, classification
- 2. Grid Search
- 3. Gradient Descent
- 4. Training Pipeline
- 5. Metrics ROC Curve, Precision Recall Curve
- 6. Calculating Entropy
- 1. Evolution of Perceptrons, Hebbs Principle, Cat Experiment
- 2. Single layer NN
- 3. Tensorflow Code
- 4. Multilayer NN
- 5. Back propagation, Dynamic Programming
- 6. Mathematical Take on NN
- 7. Function Approximator
- 8. Link with Linear Regression
- 9. Dropout and Activation
- 10. Optimizers and Loss Functions
- 1. 1D and 2D Convolution
- 2. Why CNN for Images and speech?
- 3. Convolution Layer
- 4. Coding Convolution Layer
- 5. Learning Sharpening using single convolution Layer in Tensor-Flow
- 1. Convolution
- 2. Pooling
- 3. Activation
- 4. Dropout
- 5. Batch Normalization
- 6.Object Classification
- 7. Creating Batch in Tensorflow and Normalize
- 8. Training MNIST and CIFAR datasets
- 9. Understanding a pre-trained Inception Architecture
- 10. Input Augmentation Techniques for Images
- 1. Finetuning last layers of CNN Model
- 2. Selecting appropriate Loss
- 3. Adding a new class in the last Layer
- 4. Making a model Fully Convolutional for Deployment
- 5. Finetune Imagenet for Cats vs Dog Classification.
- 1. Different types of problem in Objects
- 2. Difficulties in Object Detection and Localization
- 3. Fast RCNN
- 4. Faster RCNN
- 5. YOLO v1-v3
- 6. SSD
- 7. MobileNet
- 1. Image Compression Simple Autoencoder
- 2. Denoising Autoencoder
- 3. Variational Autoencoder and Reparematrization Trick
- 4. Robust Word Embedding using Variational Autoencoder
- 1. Evolution of Recurrent Structures
- 2. LSTM, RNN, GRU, Bi-RNN, Time-Dense
- 3. Learning a Sine Wave using RNN in Tensorflow
- 4. Creating Autocomplete for Harry Potter in Tensorflow
- 1. Generative vs Discrimative Models
- 2. Theory of GAN
- 3. Simple Distribution Generator in Tensorflow using MCMC (Markov Chain Monte Carlo)
- 4. DCGAN,WGANs for Images
- 5. InfoGANs, CycleGANs and Progressive GANs
- 6. Creating a GAN for generating Manga Art
- 1. Model Free Prediction
- 2. Monte Carlo Prediction and TD Learning
- 3. Model Free Control with REINFORCE and SARSA Learning
- 4. Assignment : Implementation of REINFORCE and SARSA Learning in Gridworld
- 5. Off policy vs On Policy Learning
- 6. Importance Sampling for Off Policy Learning
- 7. Q Learning
- 1. Understanding Deep Learning as Function Approximator
- 2. Theory of Behavioral Cloning and Deep Q Learning
- 3. Revisiting Point Collector Example in Unity and
- 4. Assignment : Training Cartpole Example via Deep Q Learning
- 1. Face Detection using Yolo-v3
- 2. Building Autocomplete Feature using RNNs
- 3. Real-time Depth Prediction and Pose Estimation
- 4. How is Deep Learning used in Autonomous Driver Assistant systems
- 5. Tips and Tricks for scaling and easy Deployment of Deep Learning Models
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Mock Interviews
- Mock interviews by Learnovita give you the platform to prepare, practice and experience the real-life job interview. Familiarizing yourself with the interview environment beforehand in a relaxed and stress-free environment gives you an edge over your peers.
- Our mock interviews will be conducted by industry experts with an average experience of 7+ years. So you’re sure to improve your chances of getting hired!
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Deep Learning Course with TensorFlow Training Objectives
- Tensorflow is the most popular and apparently best Deep Learning Framework out there. Tensorflow can be used to achieve all of these applications. The reason for its popularity is the ease with which developers can build and deploy applications.
- Yes. It's worth to study. Without Tensorflow we can't train the models in deeplearning.
- Maybe Its an Big Advantage to your future profession.
- Deep practical knowledge & Hands-on lab.
- Real-time project use cases & scenarios from the various Industries.
- Mock Tests and discussing various questions.
- LearnoVita has been actively involved in 100% Job Placement Assistance as a value-added service in the Technical Program. With the backup of an advanced training curriculum and real-time business projects, we have a very consistent and growing Job Placement and Track Record.
- Market entry to various countries and jobs in major corporate.
- Immediate job opportunities after Completion of training.
- Active Coordination with students from the stage of preparing a professional CV/Resume to attend Interviews and securing a Job.
- Preliminary Preparation ensures that our students are able to perform confidently in Interviews even it was their First Interview.
- You must be comfortable with variables, linear equations, graphs of functions, histograms, and statistical means.
- You should be a good programmer. Ideally, you should have some experience programming in Python because the programming exercises are in Python.
- However, it is not necessary for you to learn the machine learning algorithms that are not a part of machine learning in order to learn deep learning. Instead, if you want to learn deep learning then you can go straight to learning the deep learning models if you want to.
- TensorFlow is an open-source library developed by Google primarily for deep learning applications. It also supports traditional machine learning. TensorFlow was originally developed for large numerical computations without keeping deep learning in mind.
- TensorFlow: Data and Deployment: DeepLearning.AI.
- Advanced Machine Learning with TensorFlow on Google Cloud Platform: Google Cloud.
- IBM AI Engineering: IBM.
- Getting Started with Tensorflow 2: Coursera Project Network.
- Build a strong foundation of deep learning
- But only studying will never help you unless you apply the concepts practically. TensorFlow gives you that platform, and when you gain experience of using it, you'll understand what problems are encountered while designing models to solve real-world issues.
Exam & Certification
- Participate and Complete One batch of Deep Learning Course with TensorFlow Training Course
- Successful completion and evaluation of any one of the given projects
- Complete 85% of the Deep Learning Course with TensorFlow Certification course
- Successful completion and evaluation of any one of the given projects
- Oracle Certified Associate (OCA)
- Oracle Certified Professional (OCP)
- Oracle Certified Expert (OCE)
- Oracle Certified Master (OCM)
- Learn About the Certification Paths.
- Write Code Daily This will help you develop Coding Reading and Writing ability.
- Refer and Read Recommended Books Depending on Which Exam you are Going to Take up.
- Join LearnoVita Online Training Course That Gives you a High Chance to interact with your Subject Expert Instructors and fellow Aspirants Preparing for Certifications.
- Solve Sample Tests that would help you to Increase the Speed needed for attempting the exam and also helps for Agile Thinking.

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Software Testing, CapgeminiDeep Learning Course with TensorFlow Course FAQ's
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- LearnoVita is offering you the most updated, relevant, and high-value real-world projects as part of the training program.
- All training comes with multiple projects that thoroughly test your skills, learning, and practical knowledge, making you completely industry-ready.
- You will work on highly exciting projects in the domains of high technology, ecommerce, marketing, sales, networking, banking, insurance, etc.
- After completing the projects successfully, your skills will be equal to 6 months of rigorous industry experience.
- We will reschedule the classes as per your convenience within the stipulated course duration with all such possibilities.
- View the class presentation and recordings that are available for online viewing.
- You can attend the missed session, in any other live batch.