Deep Learning with TensorFlow 2.0
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.
5 hours 
28 exercises 
1 assignments 
15 quizzes 
63 downloadables
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Section 1
Introduction
What 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
Section 2
Neural networks Intro
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
One-parameter gradient descent
N-parameter gradient descent
Section 3
Setting up the environment
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
Section 4
Minimal example
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
Section 5
Introduction to TensorFlow 2
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
Section 6
Deep nets overview
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
Section 7
Overfitting
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
Section 8
Initializaiton
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
Section 9
Optimizers
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
Section 10
Preprocessing
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
Section 11
Deeper example
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
Testing the model
Section 12
Business case
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
Machine and Deep Learning
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.
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Practice
Real-life project and data. Solve them on your own computer as you would in the office.
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Trust the other 500,000 students
The course is in-depth and is delivered at a steady pace with eye catching visuals. The instructors go through all the basics really well. They try not to over-simplify the material, you get a good sense аof how deep Data Science is in the course. Great job!!!
This course is amazing! After watching the video carefully and doing all the exercises, I am even capable of having discussions with Machine learning major Master’s students! High standard course with reasonable pricing.
Very clear and in-depth explanation of data science and how all the inter-related concepts apply in real life business environment. Absolutely great for beginners! Best data science course I have come across so far!
I would highly recommend the course to any beginner who wants to venture into the world of Data Science. The concepts are very well explained and there is an emphasis on practical application which really helps create a better understanding of the concepts.
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