This Deep Learning Training course will provide you with a basic understanding of the linear algebra, probabilities, and algorithms used in deep neural networks. After learning the difference between deep learning and machine learning, delegates will gain in-depth knowledge of the different types of neural networks such as feedforward, convolutional, and recursive. At the end of this course, delegates will be able to build complex models that help machines to solve real-world problems.
Course content
Introduction to Deep Learning
- Introduction to Deep Learning
- Define Deep Learning
- Neural Networks
- Deep Learning Applications
- Introduction to Deep Learning
- Necessity of Deep Learning over Machine Learning.
- History and evolution of various deep learning algorithms
- AI and how is Deep Learning one of the paths to AI in the recent era
- Types of Machine Learning and Deep Learning
- Why Deep Learning
How to Train ANNs
- Introduction
- Perceptron Learning Rule
- Gradient Descent Rule
- Minimize Cost Function
- Tuning Learning Rate
- Stochastic vs Batch Gradient Descent
Perceptron
- What is a Perceptron
- Logic Gates with Perceptron
- Activation Functions
- Sigmoid
- ReLU
- Softmax
- Hyperbolic Functions
Multi-Layer ANN
- Intro to MLP
- Forward Propagation
- Minimize Cost Function
Training Deep Neural Nets
- Vanishing/Exploding Gradients
- Xavier Initialization
- Leaky ReLUs and ELUs
- Batch Normalization
- Transfer Learning
- Naive Bayes
- Removing a Container
- Unsupervised Pre-Training
Introduction to TensorFlow
- Intro to TensorFlow
- Computational Graph
- Key Highlights
- Creating a Graph
- Regression Example
- Gradient Descent
- Saving and Restoring Models
- layers API
- Keras-Based networks
- TensorBoard
Convolutional Neural Networks
- Intro to CNNs
- Convolution Operation
- Kernel Filter
- Feature Maps
- Pooling
- CNN Architecture
- Implement CNN in Tensor Flow
Recurrent Neural Networks
- Intro to RNNs
- Unfolded RNNs
- Basic RNN Cell
- Dynamic RNN
- Training RNNs
- Time-series predictions
- LSTM
- Word Embedding’s
- Seq2Seq Models
- Implement RNN in TensorFlow
Keras
- Introduction of Keras
- Understanding of Keras Model Building Blocks
- Illustration of different Compositional Layers
- Process based use cases’ implementations
TFlearn
- Introduction of TFlearn
- Understanding of TFlearn Model Building Blocks
- Illustration of different Compositional Layers
- Step-wise use-cases implementations
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Course Prerequisites
- Basic knowledge of Python, linear algebra and probability.
Who can attend
- This course is intended for individuals who wish to learn how deep learning works and who may wish to develop their own applications using deep learning techniques.
- Experience with programming basics and fair understanding of the basics of statics mathematics is necessary.
Number of Hours: 40hrs
Certification
Key features
- One to One Training
- Online Training
- Fastrack & Normal Track
- Resume Modification
- Mock Interviews
- Video Tutorials
- Materials
- Real Time Projects
- Virtual Live Experience
- Preparing for Certification
FAQs
DASVM Technologies offers 300+ IT training courses with 10+ years of Experienced Expert level Trainers.
- One to One Training
- Online Training
- Fastrack & Normal Track
- Resume Modification
- Mock Interviews
- Video Tutorials
- Materials
- Real Time Projects
- Materials
- Preparing for Certification
Call now: +91-99003 49889 and know the exciting offers available for you!
We working and coordinating with the companies exclusively to get placed. We have a placement cell focussing on training and placements in Bangalore. Our placement cell help more than 600+ students per year.
Learn from experts active in their field, not out-of-touch trainers. Leading practitioners who bring current best practices and case studies to sessions that fit into your work schedule. We have a pool of experts and trainers are composed with highly skilled and experienced in supporting you in specific tasks and provide professional support. 24x7 Learning support from mentors and a community of like-minded peers to resolve any conceptual doubts. Our trainers has contributed in the growth of our clients as well as professionals.
All of our highly qualified trainers are industry experts with at least 10-12 years of relevant teaching experience. Each of them has gone through a rigorous selection process which includes profile screening, technical evaluation, and a training demo before they are certified to train for us. We also ensure that only those trainers with a high alumni rating continue to train for us.
No worries. DASVM technologies assure that no one misses single lectures topics. We will reschedule the classes as per your convenience within the stipulated course duration with all such possibilities. If required you can even attend that topic with any other batches.
DASVM Technologies provides many suitable modes of training to the students like:
- Classroom training
- One to One training
- Fast track training
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- Customized training
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You will receive DASVM Technologies recognized course completion certification & we will help you to crack global certification with our training.
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DASVM Technologies has a no refund policy. Fees once paid will not be refunded. If the candidate is not able to attend a training batch, he/she is to reschedule for a future batch. Due Date for Balance should be cleared as per date given. If in case trainer got cancelled or unavailable to provide training DASVM will arrange training sessions with other backup trainer.
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