Hand Emojji Images Get 50% off on all courses.

Our Top Course
React Js
(15 Reviews)
$15 $25
Java Program
(15 Reviews)
$10 $40
Web Design
(15 Reviews)
$10 $20
Web Design
(15 Reviews)
$20 $40

Deep Learning Course!

Understand the basic architecture and functioning of neural networks, including neurons, layers, activation functions, and backpropagation.!

Best Seller Icon Bestseller
3,494 students

Key Course Feature

Video Images
Preview this course
₹23,000 ₹46,000
3 days left!
Enroll Now
  • Course MajorNeural Networks
  • Course MajorCNNs
  • Course MajorRNNs
  • Course MajorDeep Learning
  • Course MajorTensorFlow
Show More
Card image

What you'll learn

Neural Network Fundamentals

Understand the basic architecture and functioning of neural networks, including neurons, layers, activation functions, and backpropagation.

Deep Learning Frameworks

Gain proficiency in popular deep learning frameworks like TensorFlow and PyTorch, learning how to build, train, and deploy deep learning models efficiently.

Convolutional Neural Networks (CNNs)

Master CNNs for image recognition tasks, learning about convolutional layers, pooling layers, and techniques for improving model performance.

Recurrent Neural Networks (RNNs):

Explore RNNs for sequence data analysis, understanding concepts like long short-term memory (LSTM) and gated recurrent units (GRU) for tasks such as natural language processing and time series prediction.

Transfer Learning and Pre-trained Models

Learn how to leverage pre-trained models and transfer learning techniques to accelerate model training and improve performance on tasks with limited data.

Advanced Topics

Dive into advanced topics such as generative adversarial networks (GANs), reinforcement learning, and attention mechanisms, enabling you to tackle complex deep learning problems and stay at the forefront of AI research.

Show More

Course Content

  • Understanding the fundamentals of neural networks
  • Introduction to deep learning frameworks: TensorFlow and PyTorch
  • Overview of deep learning applications and real-world examplesb

  • Basic components of neural networks: neurons, layers, and activation functions
  • Designing and building neural network architectures for different tasks
  • Hyperparameter tuning and optimization techniques

  • Principles of CNNs for image recognition and computer vision tasks
  • Convolutional layers, pooling layers, and fully connected layers
  • Implementing CNN architectures for image classification and object detection

  • Introduction to RNNs for sequential data processing
  • Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) architectures
  • Applications of RNNs in natural language processing and time series analysis

  • Leveraging pre-trained models and transfer learning techniques
  • Fine-tuning pre-trained models for specific tasks
  • Understanding and using popular pre-trained models such as VGG, ResNet, and BERT

  • Introduction to advanced architectures such as autoencoders and generative adversarial networks (GANs)
  • Attention mechanisms for sequence-to-sequence tasks
  • Building and training advanced deep learning models for specific applications

  • Techniques for optimizing deep learning models for performance and efficiency
  • Model compression and quantization for deployment on resource-constrained devices
  • Deployment strategies for deep learning models in production environments

  • Understanding the ethical considerations and biases in deep learning models
  • Responsible AI practices for mitigating risks and ensuring fairness
  • Regulatory frameworks and guidelines for deploying deep learning models

  • Advanced topics in computer vision, including object detection, segmentation, and image generation
  • State-of-the-art architectures for computer vision tasks
  • Practical applications and case studies in computer vision

  • Advanced techniques in NLP, including word embeddings, sequence-to-sequence models, and transformers
  • Building deep learning models for text classification, sentiment analysis, and machine translation
  • Applications of deep learning in NLP, such as chatbots and language generation

  • Applying deep learning techniques to a real-world project
  • Solving a specific problem or conducting research using acquired skills
  • Presenting project findings and insights to peers and stakeholders

Industry recognized certification

  • Techlearnerhub certification is trusted by 10,000+ companies in industry for hiring.
  • Get physical copy of certificate to your address
Certificate Images


Kumaraswamy Raj

The Deep Learning Course trainer is an accomplished expert in the field of artificial intelligence, possessing a wealth of practical experience and academic knowledge. With a deep understanding of neural network architectures, optimization techniques, and cutting-edge advancements in deep learning, they serve as invaluable guides on the journey to mastering this complex field. Their teaching style is characterized by clarity, patience, and a genuine passion for empowering students to unlock the potential of deep learning. Through hands-on exercises, real-world projects, and insightful discussions, they create a dynamic learning environment where students can explore and experiment with advanced deep learning concepts confidently. As mentors, they inspire curiosity, foster creativity, and instill a sense of responsibility towards ethical AI practices, preparing students to make meaningful contributions to the ever-evolving landscape of artificial intelligence.


Course Rating

Enquiry Now

For details about the course

Call Us: +91 991609 1230

Deep Learning Course!
₹23,000 ₹46,000