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Deep Learning Training Program

Dive deep into neural networks — from perceptrons to transformers. Build, train and deploy CNNs, RNNs and LLM-based architectures for vision, language and time-series problems.

Course Highlights

What You'll Master

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Neural Network Fundamentals

Master forward/backpropagation, activation functions and optimizers like Adam.

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Computer Vision with CNNs

Build image classifiers, object detectors and medical imaging models.

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Sequence Models & Transformers

Work with RNNs, LSTMs and modern Transformer architectures for NLP.

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Production-ready Deployment

Serve deep learning models via Flask/FastAPI with GPU-aware deployment practices.

Curriculum

Detailed Modules

  • Review of Machine Learning fundamentals
  • Introduction to Artificial Neural Networks
  • Perceptron and Multi-Layer Perceptron (MLP)
  • Activation Functions (ReLU, Sigmoid, Tanh, Softmax)
  • Forward Propagation and Backpropagation Algorithm
  • Loss Functions (Cross-Entropy, MSE)
  • Gradient Descent Variants (SGD, Momentum, Adam)
  • Introduction to TensorFlow and PyTorch
  • Model building using Sequential and Functional APIs
  • GPU acceleration basics
  • Model training workflow; saving and loading models
  • Overfitting and Underfitting in Deep Networks
  • Dropout and Batch Normalization
  • Early Stopping and Learning Rate Scheduling
  • Hyperparameter tuning
  • Image representation and tensors; the Convolution operation
  • Filters, Feature Maps, Padding and Stride
  • Pooling layers (Max, Average) and Fully Connected Layers
  • Transfer Learning
Computer Vision Projects
  • Image Classification System
  • Face Mask Detection System
  • Medical Image Classification (X-ray / MRI)
  • Object Detection (YOLO / Faster R-CNN)
  • Sequential data understanding & Recurrent Neural Networks
  • Vanishing and Exploding Gradient Problem
  • Long Short-Term Memory (LSTM) & Gated Recurrent Units (GRU)
  • Sequence-to-Sequence Models
NLP & Time-Series Projects
  • Sentiment Analysis System
  • Stock Price Prediction (Time-Series)
  • Chatbot Development
  • Language Translation System
  • Limitations of RNNs & the Attention Mechanism
  • Transformer Architecture & Self-Attention
  • Pretrained Models & Fine-Tuning Large Language Models
Transformer Projects
  • Text Classification using Transformer Models
  • Question Answering System
  • Resume Screening Automation
  • Named Entity Recognition (NER) System
  • Generative Adversarial Networks (GANs)
  • Autoencoders & Variational Autoencoders (VAE)
  • Transfer Learning and Fine-Tuning
  • Model Interpretability & Ethical Considerations in AI
  • Model serialization; building APIs using Flask or FastAPI
  • Deployment basics (cloud introduction)
  • GPU vs CPU deployment considerations
  • Model monitoring and maintenance
  • Problem identification, dataset preparation & model architecture design
  • Training, optimization & performance evaluation
  • Business interpretation, deployment, technical documentation
Learning Outcomes
  • Design and train deep neural networks
  • Apply CNNs for computer vision tasks
  • Use RNNs/LSTMs for sequence modeling & implement transformer-based NLP solutions
  • Build an industry-ready portfolio with advanced AI projects
Tools Covered

Tools You'll Master

memory TensorFlow flare PyTorch layers Keras visibility OpenCV smart_toy Hugging Face api Flask / FastAPI

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