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.

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

8. GRADIENT DESCENT

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!!!