Machine and deep learning are some of those quantitative analysis skills that differentiate the data scientist from the other members of the team. The field of machine learning is the driving force of artificial intelligence. This course will teach you how to leverage deep learning and neural networks from this powerful tool for the purposes of data science. The technology we employ is TensorFlow 2.0, which is the state-of-the-art deep learning framework.

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create free accountWhat is machine learning, deep learning and AI? How is it useful and is it really as important as people tend to believe?

Welcome to Machine Learning

What does the course cover

Neural networks are more or less what we mean by 'deep learning' nowadays. In this section we explain the main rationale behind simple feed-forward neural networks.

Introduction to neural networks

Training the model

Types of machine learning

The linear model

Graphical representation

The objective function

L2-norm loss

Cross-entropy loss

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One-parameter gradient descent

N-parameter gradient descent

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Here, we will show you how to install the Jupyter Notebook (the environment we will use to code in Python) and how to import the relevant libraries. Because this course is based in Python, we will be working with several popular libraries: NumPy, SciPy, scikit-learn and TensorFlow 2.0.

Setting up the environment - Do not skip, please!

Why Python and why Jupyter

Installing Anaconda

Jupyter Dashboard - Part 1

Jupyter Dashboard - Part 2

Installing TensorFlow 2.0

To understand the inner workings of Neural Networks, we start with a simple example (called 'minimal example'). It is a naïve network, basically equivalent to a linear regression.

Outline

Generating the data (optional)

Initializing the variables

Training the model

Having created the simple net, we 'translate' it to TensorFlow. This is our way of taking a simple, well-understood problem to introduce the syntax and logic of TensorFlow.

TensorFlow outline

TensorFlow 2 intro

A note on coding in TensorFlow

Types of file formats in TensorFlow and data handling

Model layout - inputs, outputs, targets, weights, biases, optimizer and loss

Interpreting the result and extracting the weights and bias

Customizing your model

To have 'deep learning' we need 'deep' neural networks. In this section, we explain what exactly it means to be deep and focus on other important characteristics like width and activation functions. Finally, we explore the backpropagation algorithm.

The layer

What is a deep net

Really understand deep nets

Why do we need non-linearities

Activation functions

Softmax activation

Backpropagation

Backpropagation - intuition

Neural networks are extremely good at modeling the data at hand. That's why we can often 'learn the data TOO WELL.' This is called overfitting. Of course, there are numerous ways to prevent this from happening, which we explore in that section.

Underfitting and overfitting

Underfitting and overfitting. A classification example

Train vs validation

Train vs validation vs test

N-fold cross validation

Early stopping - motivation and types

When the model is learning, it is searching for better and better solutions to the problem at hand. However, it starts from some initial values for its parameters. It matters what our starting point is, and that's what initialization is all about.

Initializaiton

Types of simple initializations

Xavier's initialization

There is a trade-off between having a fast model and an accurate model. In this section, we explore different optimization algorithms, based on the gradient descent logic, as well as learning rate schedules and batching.

SGD&Batching

Local minima pitfalls

Momentum

Learning rate schedules

Learning rate schedules. A picture

Adaptive learning schedules

Adaptive moment estimation

Preprocessing is a crucial step relevant to any modeling problem. While there are dozens of different preprocessing techniques, there are several that are commonly employed for almost all neural networks.

Preprocessing

Basic preprocessing

Standardization

Dealing with categorical data

One-hot vs binary

Once we have learned all the relevant theory, we are ready to jump into deep waters. We explore the 'Hello world' of deep learning - the MNIST dataset, where we classify 60,000 images into 10 classes (the 10 digits: 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10).

The dataset

How to tackle the MNIST

Importing the relevant libraries and loading the data

Preprocess the data - create a validation dataset and scale the data

Preprocess the data - shuffle and batch the data

Outline the model

Select the loss and the optimizer

Learning

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Testing the model

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Data science without an application is nothing but research. Since we at 365 believe that the skills you acquire should be relevant for your work, we finish the course with a business case, where we implement all the deep learning knowledge you've acquired.

Exploring the dataset and identifying predictors

Outlining the business case solution

Balancing the dataset

Preprocessing the data

Load the preprocessed data

Learning and interpreting the result

Setting an early stopping mechanism

Testing the model

MODULE 3

This course is part of Module 3 of the 365 Data Science Program. The complete training consists of four modules, each building upon your knowledge from the previous one. Expanding on your statistical and programming skills from Modules 1 and 2, Module 3 is designed to improve your programming skills and develop your advanced statistical thinking. You will learn how to build complete linear and logistic regression models, how to cluster data, and how to build deep learning models with TensorFlow 2.0.

See All ModulesReal-life project and data. Solve them on your own computer as you would in the office.

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