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
- Agra
- Ahmedabad
- Alappuzha
- Aurangabad
- Bangalore
- Belgaum
- Bellary
- Bhubaneswar
- Chennai
- Cochin
- Coimbatore
- Delhi
- Dindigul
- Erode
- Gulbarga
- Guntur
- Gurgaon
- Haryana
- Hyderabad
- Idukki
- Indore
- Jaipur
- Kalyan
- Kannur
- Kanpur
- Kasaragod
- Kerala
- Kolkata
- Kollam
- Kottayam
- Kozhikode
- Lucknow
- Madurai
- Malappuram
- Mangaluru
- Mumbai
- Mysuru
- Nagpur
- Nashik
- Navi Mumbai
- Nizamabad
- Noida
- Palakkad
- Pathanamthitta
- Pune
- Punjab
- Salem
- Shimla
- Solapur
- Surat
- Thane
- Thanjavur
- Thirunelveli
- Thoothukudi
- Thrissur
- Tiruchirappalli
- Tiruppur
- Trivandrum
- Vadodara
- Varanasi
- Vellore
- Vijayawada
- Visakhapatnam
- Wayanad
Course Highlights
Our Locations
The foundations of deep learning and neural networks are covered, as well as techniques for improving neural networks, strategies for organizing and completing machine learning projects, convolutional neural networks, and their applications, recurrent neural networks and their methods and applications, and advanced topics such as deep reinforcement learning, generative adversarial networks, and adversarial attacks.
Rather than being numerical, the majority of the data is in an unstructured format, such as audio, image, text, and video. Companies like to hire people who have completed this deep learning course. Deep learning algorithms are employed in a variety of industries, from automated driving to medical gadgets.
Deep learning is a subset of machine learning (ML), which is essentially a three-layer neural network. One of the key benefits of employing deep learning is its capacity to perform feature engineering on its own. Deep learning is a type of learning that entails Specialization in Aurangabad will assist you in learning the fundamental ideas of deep learning, as well as understanding the problems, repercussions, and capacities of deep learning, as well as allowing you to contribute to the advancement of cutting-edge technology. 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. Artificial neural network systems are created on the human brain in deep learning, a subcategory of Machine Learning. Deep learning powers a variety of AI (artificial intelligence) services and applications that automate and perform physical operations without the need for human participation. 
