Introduction to Deep Learning
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This document provides an introduction to deep learning. It defines deep learning as a machine learning technique that analyzes and learns from data to make predictions. Deep learning uses artificial neural networks inspired by the brain's neural network, consisting of connected artificial neurons that transmit signals. The document discusses network layers that extract representations from input data, activation functions, gradient descent, loss functions, epochs, and predictions. It provides examples of real-world deep learning applications like image classification, facial recognition, and self-driving cars.
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Introduction to Deep Learning
- 1. INTRODUCTIONTO DEEP LEARNING Felicia O’Garro | Code Crew Meetup | February 4, 2021
- 2. WHAT IS DEEP LEARNING? • Technique that can be used to implement Machine Learnin g • Analyzes and learns from data to make prediction s • Learning can be supervised, semi-supervised and unsupervised
- 3. ARTIFICIAL NEURAL NETWORK • Algorithms inspired by the structure and function of the brain’s neural networ k • Consist of connected units called arti fi cial neurons that can transmit signals to each other
- 4. ARTIFICIAL NEURAL NETWORK CON’T
- 5. TRAIN/TEST SPLIT • Technique for evaluating performance • Training se t • Validation se t • Testing set
- 6. LAYERS • Building blocks of a neural networ k • Extract representations from the data fed into layers
- 7. ACTIVATION FUNCTIONS • Sigmoid - transforms input to a value between 0 and 1 • Hyperbolic tangent (Tanh) - transforms input to a value between -1 and 1 • ReLu - transforms input to a max of 0 or the input itself
- 9. LOSS FUNCTIONS • Sparse Categorical Cross-Entrop y • Binary Cross-Entrop y • Mean Squared Erro r • Mean Absolute Erro r • And more…
- 10. EPOCHS
- 11. PREDICTIONS
- 12. CODE WALKTHROUGH
- 13. REAL WORLD USE CASES • Image Classi fi catio n • Facial Recognition • Self Driving Cars • Art Creation • Music Compositio n • Fraud Detectio n • Game s • Chatbot s • Clothing Designs
- 14. RESOURCES • PythonTutorial 2020 • Khan Academy Linear Algebra • fast.ai • Kaggle • Google Colab • PyTorch • Tensor fl ow • Coursera Deep Learning Specialization • Deep Lizard • Hands-on Machine Learning with Scikit-Learn, Keras &Tensor fl ow
- 16. THANKS MUCH!!!