• Basic knowledge of programming languages, particularly Python, will be beneficial. 
• A fundamental understanding of machine learning concepts, linear algebra, and calculus is helpful. 
• Familiarity with basic computer operations and data processing will enhance learning in Deep Learning.

Module 1: Introduction to Deep Learning

• What is Deep Learning?
  • Evolution from Machine Learning to Deep Learning.
  • Applications in various domains (Computer Vision, NLP, Healthcare, etc.).
• Understanding Neural Networks
  • Perceptrons and multilayer perceptrons (MLP).
  • Biological inspiration and the working of artificial neurons.
• Key Terminologies
  • Epochs, batches, and iterations.
  • Forward and backward propagation.
  • Loss functions and optimization.

Module 2: Mathematics for Deep Learning

• Linear Algebra
  • Vectors, matrices, and operations (dot product, matrix multiplication).
  • Eigenvalues and Singular Value Decomposition (SVD).
• Calculus
  • Derivatives and partial derivatives.
  • Chain rule for backpropagation.
• Probability and Statistics
  • Probability distributions.
  • Bayes’ theorem and its role in DL models.
• Optimization Techniques
  • Gradient Descent and its variants (SGD, Adam, RMSProp).

Module 3: Fundamentals of Neural Networks

• Architecture of Neural Networks
  • Input layer, hidden layers, and output layer.
  • Activation functions: Sigmoid, ReLU, Tanh, Softmax.
• Training Neural Networks
  • Loss functions: MSE, Cross-Entropy.
  • Weight initialization techniques.
  • Overfitting and underfitting.
• Improving Model Performance
  • Regularization techniques: Dropout, L1/L2 regularization.
  • Learning rate schedules and early stopping.

Module 4: Convolutional Neural Networks (CNNs)

• Introduction to CNNs
  • Why CNNs for image data.
  • Convolutional operations and filters.
• CNN Components
  • Pooling layers (Max Pooling, Average Pooling).
  • Fully connected layers and feature maps.
• Architectures of CNNs
  • LeNet, AlexNet, VGG, ResNet, and EfficientNet.
• Applications
  • Image classification, object detection, and semantic segmentation.

Module 5: Recurrent Neural Networks (RNNs)

• Introduction to RNNs
  • Sequential data and temporal dependencies.
  • Working of RNNs and the vanishing gradient problem.
• Variants of RNNs
  • Long Short-Term Memory (LSTM).
  • Gated Recurrent Units (GRU).
• Applications
  • Text generation, language modeling, and speech recognition.

Module 6: Deep Learning for Natural Language Processing (NLP)

• Word Embeddings
  • Word2Vec, GloVe, and FastText.
  • Contextual embeddings (ELMo, BERT).
• Sequence Models
  • Attention mechanism and Transformer architecture.
  • Introduction to GPT and BERT models.
• Applications
  • Sentiment analysis, machine translation, and chatbots.

Module 7: Autoencoders and Generative Models

• Autoencoders
  • Vanilla Autoencoders and their components.
  • Variational Autoencoders (VAEs).
• Generative Adversarial Networks (GANs)
  • Introduction to GANs and their architecture.
  • Applications of GANs: Image generation, style transfer.
• Applications
  • Anomaly detection and data denoising.

Module 8: Advanced Deep Learning Architectures

• Transfer Learning
  • Pre-trained models (Inception, ResNet, MobileNet).
  • Fine-tuning and feature extraction.
• Deep Reinforcement Learning
  • Basics of reinforcement learning.
  • Q-learning and Deep Q-Networks (DQN).
• Capsule Networks
  • Introduction and differences from CNNs.
  • Applications in advanced image recognition.

Module 9: Tools and Frameworks

• Deep Learning Frameworks
  • TensorFlow and PyTorch.
  • Keras for prototyping.
• Development Environments
  • Jupyter Notebooks, Google Colab, Kaggle.
• Hardware Accelerators
  • Using GPUs and TPUs for faster training.

Module 10: Model Deployment

• Exporting Models
  • ONNX format and TensorFlow SavedModel.
  • Conversion for mobile and edge devices.
• Deployment Techniques
  • REST APIs using Flask/FastAPI.
  • Deployment on cloud platforms: AWS, GCP, Azure.
• Performance Optimization
  • Quantization and pruning.
  • Tools for inference optimization (TensorRT, OpenVINO).

Module 11: Ethics and Bias in AI

• AI Fairness
  • Identifying and mitigating bias in models.
  • Explainability and interpretability (SHAP, LIME).
• Ethical Challenges
  • Adverse impacts of deepfakes and misuse of DL models.
  • Privacy concerns and data handling regulations.

Module 12: Real-World Applications

• Computer Vision
  • Image classification, facial recognition, and medical imaging.
• NLP
  • Machine translation, sentiment analysis, and summarization.
• Robotics
  • Path planning, obstacle avoidance, and robotic arm control.
• Healthcare
  • Predictive diagnostics, drug discovery, and personalized medicine.

40 Days (also available fast track course with short term duration)

• Flexible Schedules
• Live Online Training

• Training by highly experienced and certified professionals
• No slideshow (PPT) training, fully Hand-on training
• Interactive session with interview QA’s
• Real-time projects scenarios & Certification Help
• 24 X 7 Support