Branch-AI/piscine/week03/day01

W3D01 Piscine AI - Data Science

Neural Networks

Last week you learnt about some Machine Learning algorithms as Random Forest or Gradient Boosting. Neural Networks are another type of Machine Learning algorithms that are intensively used because of their efficiency. Neural networks are a set of algorithms, modeled loosely after the human brain, that are designed to recognize patterns. They interpret sensory data through a kind of machine perception, labeling or clustering raw input. The patterns they recognize are numerical, contained in vectors, into which all real-world data, be it images, sound, text or time series, must be translated. Different types of neural networks exist and are specific to some use-cases. For example CNN for images, RNN or LSTMs for time-series or text, etc ...

Today we will focus on Artificial Neural Networks. The goal is to understand how do the neural networks work, train them on data and understand the challenges of training a neural network. The ressources below expalin very well the mecanisms behind neural networks, step by step.

However the exercices won't cover architectures as RNN, LSTM - used on sequences as time series or text, CNN - used a lot on images processing. One of the projects will require to know how to use the special architectures. To do so, I suggest that you go through this lesson: https://fr.coursera.org/specializations/deep-learning.

Exercises of the day

• Exercise 1 The neuron
• Exercise 2 Neural network
• Exercise 3 Log loss
• Exercise 4 Forward propagation
• Exercise 5 Regression

Virtual Environment

• Python 3.x
• NumPy
• Jupyter or JupyterLab

Version of NumPy I used to do the exercises: 1.18.1. I suggest to use the most recent one.

Ressources

• https://victorzhou.com/blog/intro-to-neural-networks/

• https://srnghn.medium.com/deep-learning-overview-of-neurons-and-activation-functions-1d98286cf1e4

• https://towardsdatascience.com/machine-learning-for-beginners-an-introduction-to-neural-networks-d49f22d238f9