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Workshop Machine Learning in TensorFlow/Keras​ Fundamentals


This Tensorflow/Keras fundamentals course is designed for users who want to dive into the realms of Artificial Intelligence.

 

Esta formação é ministrada em Inglês.

Em parceria com a entidade acreditada:

Cursos relacionados

Pré-requisitos

  • Knowledge of funcamental concepts of Python for Data Science

Nice to have:

  • Basic Linear Algebra
    • Vectors
    • Vectorial spaces
    • Matrix operations
    • Factorizations
  • Basic Probabilities and Statistics
    • Histograms, Heatmaps
    • Variance, Covariance, Correlation
    • Distributions: Gaussian, Poisson, etc.
    • Bayes Theory

Programa

  • Introduction
  • Machine Learning with sci-kit
  • Neural Networks in Tensorflow/Keras
  • Convolutional Neural Networks (peek)
  • NLP using Deep Learning
  • Reinforcement Learning
  • Recommender Systems

Introduction

  • What is ML?
  • Where can I find it in real life?
  • Why now?
  • What are the three main categories of ML?
    – Supervised learning
    – Unsupervised learning
    – Reinforcement learning (demo)
  • ML pipeline

Machine Learning with sci-kit

  • ML pipeline review
  • Scikit Python Library
  • Data representation
    • Feature matrix
    • Target array
    • Iris dataset example
  • Estimator API
  • Linear Regression
    • Simple Linear Regression
    • Model Evaluation
    • Polynomial Regression
  • Selecting the best model
  • The bias-variance trade-off
  • Logistic Regression
    • Who survives the Titanic?
  • Naive Bayes
    • Gaussian Naive Bayes
    • Multinomial Naive Bayes
    • Categorical Naive Bayes
  • k Nearest Neighbours
  • k-Means Clustering
  • Dimensionality reduction
    • Principal Components Analysis (PCA)
    • Singular Value Decomposition (SVD)
  • Decision Trees
  • Random Forests

Hands-on Lab:

  • Playing around with different values affecting the bias and the variance, calculating precision, recall, F1 and F2-scores, comparing different models on the training and testing accuracies
  • Doing a little bit of data preprocessing, analyzing the difference between categorical and numerical data, plotting some relevant statistical values and visually inspecting the correlation between features

Neural Networks in Tensorflow/Keras

  • Artificial Neural Networks (ANNs)
    – Neurons
    – Layers
    – Activation Functions
    – More vocabulary
  • Popular Frameworks
  • Keras
  • Linear Regression
    – Defining Models in Keras
    – Training and predicting
  • Fashion MNIST example

Hands-on Lab:

  • Creating our first custom neural network model
  • Choosing the number of layers and the number of neurons per layer
  • Tweaking the learning rate
  • Training the neural network on real world data

Convolutional Neural Networks (peek)

  • Motivation behind CNNs
  • CNN Building blocks
    – Convolution Layers
    – Pooling Layers
  • CNNs in Keras
  • Data Augmentation
  • Architectures

NLP using Deep Learning

  • Spam detector
  • Sentiment analyzer
  • Autocomplete

Reinforcement Learning

  • Frozen Lake demo
  • Flappy Bird demo

Recommender Systems

  • Data preparation
  • Cosine distance
  • SVD for recommender systems
  • Autoencoder demo

Outras Edições

Workshop Machine Learning in TensorFlow/Keras​ Fundamentals

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