Deep Learning
Master neural networks, computer vision, CNNs, RNNs, and model deployment using TensorFlow and PyTorch.
Scroll to explore
Duration
2 Months
Learning Mode
Live Online + Labs
Hands-on Work
4 Real Projects
Credential
Verified Certificate
Technologies & Tools You'll Master
Hands-on experience with production tools and frameworks utilized by modern tech enterprises
About The Course
Practical curriculum engineered to take you from foundational principles to deployment-ready expertise
Explore the technology behind modern Artificial Intelligence, from image recognition and speech processing to intelligent language systems. This practical Deep Learning course teaches you how neural networks learn from data and how to build deep learning models using Python and TensorFlow or PyTorch. Learn through hands-on implementation, guided coding exercises, and real-world projects. Duration: 2 Months | Mode: Online Instructor-Led Training | Tools: Python, NumPy, Pandas, Matplotlib, TensorFlow/Keras or PyTorch, Google Colab.
Core Competencies & Skills
Course Curriculum & Syllabus
Module 1: Deep Learning Fundamentals
- Introduction to Deep Learning
- Machine Learning vs. Deep Learning
- Applications of Deep Learning
- Introduction to neural networks
- Neurons, weights and biases
- Activation functions
Module 2: Neural Network Implementation
- Forward propagation
- Loss functions
- Backpropagation
- Gradient descent
- Optimizers: SGD and Adam
- Building neural networks with TensorFlow/Keras or PyTorch
Module 3: Training and Improving Models
- Training, validation and test datasets
- Epochs, batch size and learning rate
- Overfitting and underfitting
- Dropout and regularization
- Early stopping
- Model performance evaluation
Module 4: Convolutional Neural Networks (CNN)
- Introduction to computer vision
- Convolution and filters
- Pooling layers
- CNN architecture
- Image preprocessing and augmentation
- Building an image classification model
Module 5: Advanced Deep Learning Architectures
- Introduction to RNNs
- Sequential data processing
- LSTM and GRU fundamentals
- Introduction to attention mechanisms
- Transfer learning
- Pretrained models and fine-tuning
Module 6: Practical AI Applications
- Image classification pipelines
- Introduction to natural language processing with neural networks
- Model saving and loading
- Using pretrained models
- Building a simple application interface
- Model deployment fundamentals
- Ethical considerations and responsible AI
What You Will Be Able To Build
Enterprise Real-World Projects
Image Classification System
Train a CNN to classify images into predefined categories.
Plant Disease Detection
Build an image classification model to identify plant disease categories from leaf images.
Handwritten Digit Recognition
Train a neural network to recognize handwritten digits using a standard image dataset.
Transfer Learning for Medical Image Classification
Use a pretrained deep learning model to classify a suitable medical image dataset for educational and research purposes.
Career Paths & In-Demand Roles
Frequently Asked Questions
Ready to Master Deep Learning?
Have questions about upcoming batches, syllabus deep-dives, or prerequisites? Chat directly with our technical mentors and admissions coordinators on WhatsApp.
⚡ Quick Response · Flexible Weekday/Weekend Batches · Certificate Included