Deep Learning Online Live Class by Experts
Our Training/Internship Process
Deep Learning - Syllabus, Fees & Duration
MODULE 1
- Introduction to Tensor Flow
- Computational Graph
- Key highlights
- Creating a Graph
- Regression example
- Gradient Descent
- TensorBoard
- Modularity
- Sharing Variables
- Keras Perceptrons
- What is a Perceptron?
- XOR Gate
MODULE 2
- Activation Functions
- Sigmoid
- ReLU
- Hyperbolic Fns, Softmax Artificial Neural Networks
- Introduction
- Perceptron Training Rule
- Gradient Descent Rule
MODULE 3
- Gradient Descent and Backpropagation
- Gradient Descent
- Stochastic Gradient Descent
- Backpropagation
- Some problems in ANN Optimization and Regularization
- Overfitting and Capacity
- Cross-Validation
- Feature Selection
- Regularization
- Hyperparameters
MODULE 4
- Introduction to Convolutional Neural Networks
- Introduction to CNNs
- Kernel filter
- Principles behind CNNs
- Multiple Filters
- CNN applications Introduction to Recurrent Neural Networks
- Introduction to RNNs
- Unfolded RNNs
- Seq2Seq RNNs
- LSTM
- RNN applications
MODULE 5
- Deep learning applications
- Image Processing
- Natural Language Processing
- Speech Recognition
- Video Analytics
This syllabus is not final and can be customized as per needs/updates
- Since 2001, Making IT Experts by Experts
- Duration: 40 Hours (Vary as per your skill)
- Course Fees
- Individual Live Classes (1:1)
- Batch Classes @ Low Fees
- Training on Your Time, Any Where
- Access to Recorded Videos
- Practical Internship on Projects
- 100% Placement Support by our jobsHybrid.com
- Training/Internship Certificate
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Course Highlights
Our Locations
Deep learning models in the real world could be used for driverless cars, money filtration, virtual assistants, facial recognition, and other applications.
. Every day, businesses collect massive volumes of data and analyze it to get actionable business insights.
Because there is a strong demand for skilled deep learning engineers in various fields, this deep learning course in Dindigul certification training is ideal for intermediate and advanced experts. One of the key benefits of employing deep learning is its capacity to perform feature engineering on its own. Deep learning algorithms are employed in a variety of industries, from automated driving to medical gadgets. Deep learning has become increasingly significant for commercial decision-making since it is very adept at processing such forms of data. Deep learning is important because it automates feature generation, works well with unstructured data, has improved self-learning capabilities, supports parallel and distributed algorithms, is cost-effective, has advanced analytics, and is scalable. Deep learning teaches using botorganizeded anorganizedtured data. Python is the language of deep learning. 
