Deep Learning is a subset of machine learning that uses neural networks with many layers to model complex patterns in large datasets. It excels in tasks such as image and speech recognition, natural language processing, and autonomous systems. Deep Learning frameworks, implemented on powerful GPUs, support scalable and efficient training. It drives advancements in AI with applications across various industries.

• 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