Introduction to Deep Learning
Deep learning leverages multi-layered neural networks to learn complex representations. This track introduces artificial neural networks, activation functions, and the backpropagation algorithm. Learners explore how hierarchical feature learning enables breakthroughs in perception tasks. Emphasis is placed on optimizing models using techniques such as stochastic gradient descent. Applications include image recognition, speech processing, and language modeling. This marks entry into modern AI development.
Deep Learning Elements:
- Neural network fundamentals
- Training mechanisms
- Activation and optimization principles
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Introduction to Deep Learning Conference Speakers
Recommended Sessions
- Capstone Project
- Classification Algorithms and Decision Systems
- Clustering and Unsupervised Pattern Discovery
- Convolutional Neural Networks (CNNs)
- Data Preparation and Feature Processing
- Deployment of ML Models
- Dimensionality Reduction and Feature Selection
- Ensemble Learning Approaches
- Ethics and Responsible AI
- Exploratory Data Analysis and Visualization
- Feature Engineering and Representation Design
- Foundations of Machine Learning
- Generative Models and Creativity in AI
- Introduction to Deep Learning
- Model Evaluation and Performance Validation
- Natural Language Processing Basics
- Neural Network Architecture and Optimization
- Optimization and Hyperparameter Tuning
- Recurrent Neural Networks (RNNs)
- Regression-Based Learning Models
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