Deep_Learning_Algorithms_Presentation.pptx

Deep_Learning_Algorithms_Presentation.pptx

• 1.
  Introduction to DeepLearning • Definition: A subset of Machine Learning based on artificial neural networks with representation learning. • Focus: Learn patterns from large amounts of data using layered neural networks. • Inspiration: Mimics the human brain.
• 2.
  Types of DeepLearning • 1. Feedforward Neural Networks (FNN) • 2. Convolutional Neural Networks (CNN) • 3. Recurrent Neural Networks (RNN) • 4. Generative Adversarial Networks (GAN) • 5. Autoencoders
• 3.
  Feedforward Neural Networks (FNN) •Simplest type of artificial neural network. • Data flows in one direction (input → output). • Used for basic prediction tasks. • Example: Predicting housing prices.
• 4.
  Convolutional Neural Networks (CNN) •Designed for image data. • Uses convolutional layers to extract spatial features. • Common in image recognition, computer vision. • Example: Handwritten digit recognition (MNIST).
• 5.
  Recurrent Neural Networks(RNN) • Designed for sequential data. • Has feedback connections (can use previous outputs as inputs). • Common in NLP and time-series analysis. • Example: Sentiment analysis, stock forecasting.
• 6.
  Long Short-Term Memory(LSTM) • A type of RNN designed to remember long- term dependencies. • Avoids vanishing gradient problem. • Used in language modeling, speech recognition.
• 7.
  Generative Adversarial Networks (GAN) •Consists of a Generator and a Discriminator. • Generator creates fake data; Discriminator tries to detect fake vs. real. • Used in image generation, deepfake creation.
• 8.
  Autoencoders • Unsupervised neuralnetworks that learn to compress and reconstruct data. • Used for noise reduction, dimensionality reduction. • Example: Anomaly detection.
• 9.
  Deep Learning Frameworks •Popular Tools: • - TensorFlow • - PyTorch • - Keras • - MXNet • Provide APIs and utilities to build, train and deploy DL models.
• 10.
  Applications of DeepLearning • 1. Computer Vision: Image classification, face recognition • 2. Natural Language Processing: Translation, chatbots • 3. Healthcare: Disease detection from scans • 4. Autonomous Vehicles: Object detection, path planning
• 11.
  Steps to ApplyDeep Learning • 1. Define problem & gather data • 2. Preprocess and split data • 3. Choose model architecture • 4. Train the model • 5. Evaluate performance • 6. Deploy and monitor
• 12.
  Summary of DeepLearning Algorithms • Algorithm | Purpose | Application • CNN | Spatial data | Image recognition • RNN/LSTM | Sequence data | Language models • GAN | Data generation | Art, deepfakes • Autoencoders | Feature learning | Anomaly detection
• 13.
  Final Thoughts • Deeplearning excels with large data. • Choose the right model for the task. • Understand limitations: interpretability, compute requirements.
• 14.
  Questions & Discussion
• 15.
  References • 1. DeepLearning by Ian Goodfellow • 2. TensorFlow and PyTorch documentation • 3. Coursera Deep Learning Specialization