₹6,999
₹6,999
₹6,999
Total Course Cost
Total Course Cost
Total Course Cost
Foundations of Deep Learning, Design. Train. Transform the future of AI.
Foundations of Deep Learning, Design. Train. Transform the future of AI.
Foundations of Deep Learning, Design. Train. Transform the future of AI.
Coming Soon..
Coming Soon..
Coming Soon..
Course Overview
Master the fundamental and advanced concepts of deep learning by building and training neural networks from scratch. This immersive course takes you through a journey starting with the basics of artificial neural networks and expands into cutting-edge architectures like Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Transformers, and Generative Adversarial Networks (GANs).
Through structured theory and hands-on projects, you will
Build deep learning models to solve real-world problems
Build deep learning models to solve real-world problems
Build deep learning models to solve real-world problems
Apply them in areas like image recognition, natural language processing, and generative modeling
Apply them in areas like image recognition, natural language processing, and generative modeling
Apply them in areas like image recognition, natural language processing, and generative modeling
Learn industry-standard tools such as PyTorch, scikit-learn, NumPy, and more
Learn industry-standard tools such as PyTorch, scikit-learn, NumPy, and more
Learn industry-standard tools such as PyTorch, scikit-learn, NumPy, and more
Understand both low-level architecture design and high-level optimization strategies
Understand both low-level architecture design and high-level optimization strategies
Understand both low-level architecture design and high-level optimization strategies
Whether you’re a tech enthusiast, AI engineer in the making, or a professional looking to upskill — this course will give you the deep tech fluency required to work on and lead AI initiatives in any organization.
Who Should Take This Course
Early-stage data scientists and ML engineers aiming to build real deep learning proficiency
Early-stage data scientists and ML engineers aiming to build real deep learning proficiency
Early-stage data scientists and ML engineers aiming to build real deep learning proficiency
Professionals transitioning into AI development from data or software engineering backgrounds
Professionals transitioning into AI development from data or software engineering backgrounds
Professionals transitioning into AI development from data or software engineering backgrounds
Final-year students and research aspirants interested in computer vision, NLP, or generative AI
Final-year students and research aspirants interested in computer vision, NLP, or generative AI
Final-year students and research aspirants interested in computer vision, NLP, or generative AI
Engineers and developers who already know Python and want to start building with PyTorch
Engineers and developers who already know Python and want to start building with PyTorch
Engineers and developers who already know Python and want to start building with PyTorch
You will learn
Core principles of deep learning and neural networks
Core principles of deep learning and neural networks
Core principles of deep learning and neural networks
Practical design and implementation of CNNs, RNNs, and GANs
Practical design and implementation of CNNs, RNNs, and GANs
Practical design and implementation of CNNs, RNNs, and GANs
Optimization and evaluation techniques for deep learning models
Optimization and evaluation techniques for deep learning models
Optimization and evaluation techniques for deep learning models
How to build models for
How to build models for
How to build models for
Image classification
Image classification
Image classification
Sentiment analysis
Sentiment analysis
Sentiment analysis
Face generation
Face generation
Face generation
Text summarization
Text summarization
Text summarization
Advanced model tuning with hyperparameters and transfer learning
Advanced model tuning with hyperparameters and transfer learning
Advanced model tuning with hyperparameters and transfer learning
Course Structure
Module 1 - Introduction to Deep Learning
Module 1 - Introduction to Deep Learning
Module 1 - Introduction to Deep Learning
Understand what deep learning is and why it matters
Understand what deep learning is and why it matters
Understand what deep learning is and why it matters
Understand what deep learning is and why it matters
Learn model evaluation techniques and metrics
Learn model evaluation techniques and metrics
Learn model evaluation techniques and metrics
Learn model evaluation techniques and metrics
Work with simple perceptrons, activation functions, and gradient descent
Work with simple perceptrons, activation functions, and gradient descent
Work with simple perceptrons, activation functions, and gradient descent
Work with simple perceptrons, activation functions, and gradient descent
Prevent overfitting with regularization and dropout
Prevent overfitting with regularization and dropout
Prevent overfitting with regularization and dropout
Prevent overfitting with regularization and dropout
Module 2 - Convolutional Neural Networks (CNNs)
Module 2 - Convolutional Neural Networks (CNNs)
Module 2 - Convolutional Neural Networks (CNNs)
Design and train image classification and segmentation models
Design and train image classification and segmentation models
Design and train image classification and segmentation models
Design and train image classification and segmentation models
Use CNN architectures like U-Net and ResNet
Use CNN architectures like U-Net and ResNet
Use CNN architectures like U-Net and ResNet
Use CNN architectures like U-Net and ResNet
Build object detection systems with bounding boxes
Build object detection systems with bounding boxes
Build object detection systems with bounding boxes
Build object detection systems with bounding boxes
Hands-on with PyTorch and image datasets
Hands-on with PyTorch and image datasets
Hands-on with PyTorch and image datasets
Hands-on with PyTorch and image datasets
Module 3 - Recurrent Neural Networks & Transformers
Module 3 - Recurrent Neural Networks & Transformers
Module 3 - Recurrent Neural Networks & Transformers
Understand time series and sequential data modeling
Understand time series and sequential data modeling
Understand time series and sequential data modeling
Build text classification and language modeling systems using LSTMs and GRUs
Build text classification and language modeling systems using LSTMs and GRUs
Build text classification and language modeling systems using LSTMs and GRUs
Build text classification and language modeling systems using LSTMs and GRUs
Get introduced to BERT and GPT models
Get introduced to BERT and GPT models
Get introduced to BERT and GPT models
Get introduced to BERT and GPT models
Learn how transformers have revolutionized NLP
Learn how transformers have revolutionized NLP
Learn how transformers have revolutionized NLP
Learn how transformers have revolutionized NLP
Module 4 - Building Generative Adversarial Networks (GANs)
Module 4 - Building Generative Adversarial Networks (GANs)
Module 4 - Building Generative Adversarial Networks (GANs)
Understand GAN architecture, loss functions, and training dynamics
Understand GAN architecture, loss functions, and training dynamics
Understand GAN architecture, loss functions, and training dynamics
Build image generators using CycleGANs and Deep Convolutional GANs
Build image generators using CycleGANs and Deep Convolutional GANs
Build image generators using CycleGANs and Deep Convolutional GANs
Build image generators using CycleGANs and Deep Convolutional GANs
Explore model optimization, adversarial techniques, and real-world generative applications
Explore model optimization, adversarial techniques, and real-world generative applications
Explore model optimization, adversarial techniques, and real-world generative applications
Explore model optimization, adversarial techniques, and real-world generative applications
Skills You’ll Gain
Deep Learning Foundations
Deep Learning Foundations
Deep Learning Foundations
Neural Network Architecture Design
Neural Network Architecture Design
Neural Network Architecture Design
CNN, RNN, GAN Implementation
CNN, RNN, GAN Implementation
CNN, RNN, GAN Implementation
PyTorch Framework Expertise
PyTorch Framework Expertise
PyTorch Framework Expertise
Image and Text Processing
Image and Text Processing
Image and Text Processing
Hyperparameter Tuning & Model Optimization
Hyperparameter Tuning & Model Optimization
Hyperparameter Tuning & Model Optimization
Transfer Learning and Autoencoders
Transfer Learning and Autoencoders
Transfer Learning and Autoencoders
NLP with Transformers (BERT, GPT-3)
NLP with Transformers (BERT, GPT-3)
NLP with Transformers (BERT, GPT-3)
Tools You’ll Work With
PyTorch
PyTorch
PyTorch
NumPy & Pandas
NumPy & Pandas
NumPy & Pandas
Scikit-learn
Scikit-learn
Scikit-learn
Matplotlib & Seaborn
Matplotlib & Seaborn
Matplotlib & Seaborn
Jupyter Notebooks
Jupyter Notebooks
Jupyter Notebooks
Pretrained LLMs (BERT, GPT)
Pretrained LLMs (BERT, GPT)
Pretrained LLMs (BERT, GPT)
Transfer Learning APIs
Transfer Learning APIs
Transfer Learning APIs
Prerequisites
To get the best out of this course, you should have:
To get the best out of this course, you should have:
To get the best out of this course, you should have:
Intermediate Python knowledge
Intermediate Python knowledge
Intermediate Python knowledge
Experience with NumPy, Pandas, and Matplotlib
Experience with NumPy, Pandas, and Matplotlib
Experience with NumPy, Pandas, and Matplotlib
Understanding of basic ML concepts: vectors, matrices, linear algebra
Understanding of basic ML concepts: vectors, matrices, linear algebra
Understanding of basic ML concepts: vectors, matrices, linear algebra
Some knowledge of calculus, derivatives, and feedforward neural networks
Some knowledge of calculus, derivatives, and feedforward neural networks
Some knowledge of calculus, derivatives, and feedforward neural networks
Familiarity with Jupyter notebooks and machine learning frameworks
Familiarity with Jupyter notebooks and machine learning frameworks
Familiarity with Jupyter notebooks and machine learning frameworks
Course Duration
4–5 Weeks
4–5 Weeks
4–5 Weeks
20–25 Hours Total (Flexible Schedule)
20–25 Hours Total (Flexible Schedule)
20–25 Hours Total (Flexible Schedule)
Real-world mini-projects and model-building labs
Real-world mini-projects and model-building labs
Real-world mini-projects and model-building labs
Why Take This Course?
Learn how deep learning models are built from scratch, and how they’re applied in the real world
Learn how deep learning models are built from scratch, and how they’re applied in the real world
Learn how deep learning models are built from scratch, and how they’re applied in the real world
Gain the skills that top tech companies demand in AI roles
Gain the skills that top tech companies demand in AI roles
Gain the skills that top tech companies demand in AI roles
Go from zero to building deployable models in just weeks
Go from zero to building deployable models in just weeks
Go from zero to building deployable models in just weeks
Work on real datasets — and leave with portfolio-ready projects
Work on real datasets — and leave with portfolio-ready projects
Work on real datasets — and leave with portfolio-ready projects
Start Your AI & Cloud Journey
Build real skills with practical, beginner-friendly courses — designed to help you break into tech, no matter your background.
Deepberg.ai © 2025 All rights reserved.
Start Your AI & Cloud Journey
Build real skills with practical, beginner-friendly courses — designed to help you break into tech, no matter your background.
Deepberg.ai © 2025 All rights reserved.
Start Your AI & Cloud Journey
Build real skills with practical, beginner-friendly courses — designed to help you break into tech, no matter your background.
Deepberg.ai © 2025 All rights reserved.