Linear algebra

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From the course by IBM

Applied AI with DeepLearning

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IBM

Applied AI with DeepLearning

93 оценки

>>> By enrolling in this course you agree to the End User License Agreement as set out in the FAQ. Once enrolled you can access the license in the Resources area <<< This course, Applied Artificial Intelligence with DeepLearning, is part of the IBM Advanced Data Science Certificate which IBM is currently creating and gives you easy access to the invaluable insights into Deep Learning models used by experts in Natural Language Processing, Computer Vision, Time Series Analysis, and many other disciplines. We’ll learn about the fundamentals of Linear Algebra and Neural Networks. Then we introduce the most popular DeepLearning Frameworks like Keras, TensorFlow, PyTorch, DeepLearning4J and Apache SystemML. Keras and TensorFlow are making up the greatest portion of this course. We learn about Anomaly Detection, Time Series Forecasting, Image Recognition and Natural Language Processing by building up models using Keras one real-life examples from IoT (Internet of Things), Financial Marked Data, Literature or Image Databases. Finally, we learn how to scale those artificial brains using Kubernetes, Apache Spark and GPUs. IMPORTANT: THIS COURSE ALONE IS NOT SUFFICIENT TO OBTAIN THE "IBM Watson IoT Certified Data Scientist certificate". You need to take three other courses where two of them are currently built. The Specialization will be ready late spring, early summer 2018 Using these approaches, no matter what your skill levels in topics you would like to master, you can change your thinking and change your life. If you’re already an expert, this peep under the mental hood will give your ideas for turbocharging successful creation and deployment of DeepLearning models. If you’re struggling, you’ll see a structured treasure trove of practical techniques that walk you through what you need to do to get on track. If you’ve ever wanted to become better at anything, this course will help serve as your guide. Prerequisites: Some coding skills are necessary. Preferably python, but any other programming language will do fine. Also some basic understanding of math (linear algebra) is a plus, but we will cover that part in the first week as well. If you choose to take this course and earn the Coursera course certificate, you will also earn an IBM digital badge. To find out more about IBM digital badges follow the link ibm.biz/badging.

From the lesson

Introduction to deep learning

Linear algebra6:03

Deep feed forward neural networks12:57

Convolutional Neural Networks4:34

Recurrent neural networks1:46

Auto encoders and representation learning2:39

Methods for neural network training8:23

Gradient Descent Updater Strategies6:39

How to choose the correct activation function3:08

The bias-variance tradeoff in deep learning3:32

Meet the Instructors

Romeo Kienzler
Chief Data Scientist, Course Lead
IBM Watson IoT
Niketan Pansare
Senior Software Engineer
IBM Research
Tom Hanlon
Training Director
Skymind
Max Pumperla
Deep Learning Engineer
Ilja Rasin
Data Scientist
IBM Watson Health

Welcome to the lecture on Linear Algebra.

So before we start, let's have a look at some important terms.

Let's start with Scalar.

Scalar is basically any number,

like 1, 5, 23, or 42.

But we are not limited to integers.

Also, real numbers are scalars, like 23.5, for example.

If you group a number of scalars together, you end up with a vector.

This is a vector of length four.

And this is one of length three.

And this is one of length five.

So vector can easily be confused with tuple.

So this guy here can be either a vector or a tuple.

But this is a tuple.

Can you see the difference?

So in a vector, each element has to have the same type, whereas in the tuple,

types can be mixed.

A Matrix is the big brother of a vector.

It's basically a list of equal-sized vectors.

Notice that the number of rows and

columns can be different, but each element has to have the same type.

A Matrix has n columns and m rows,

therefore we call these matrices m- by- n matrices.

So the left one is a 3- by- 4 matrix whereas the right one is a 2- by- 5

matrix.

Finally, the coolest guy is called the Tensor.

Here, you can see a 3D tensor which is basically nothing else like a matrix,

but in three dimensions.

Tensors can be quite handy in image processing, for example.

So you can have one dimension for the height, one for the width, and

one for colors, alpha channel, and focus information, for example.

So a tensor is more general term for special cases.

So, for example, a zero-dimensional tensor is a scalar,

a one-dimensional tensor is a vector, a two-dimensional tensor is a matrix.

So now, you can really show off in front of your colleagues

by knowing all those terms.

So, let's start with some math.

The most important mathematical operation on tensors

used in this course is multiplication.

We can multiple two scalars, so let's start with two integers, 2 and 3.

If you multiply them together we get 6.

But we can also multiply two vectors.

Note that the column-wise notation is the most common in math.

This multiplication is called the dot product or

scalar product because it returns a scalar.

We won't cover the cross product or the vector product here.

So that dot product simply takes the first element of the first vector and

multiplies it with the first element of the second vector.

Then it does the same for the second elements of the vectors.

And finally the rest.

So now, all those immediate products are summed up.

And the reciting sum is the solution to the dot product.

Generally speaking,

a vector multiplication is a linear combination of those.

This is very handy in deep learning, because one vector normally is used for

the data, and one vector is used for

the training weights, which you will see later.

Using this notation, we can also make use of GPUs or SIMD instruction sets on CPUs.

Note that both vectors need to be of the same size.

We will use NumPy as central library for linear algebra in Python.

So let's import it.

Now, we create two vectors, A and B.

Note, that those are implemented as one-dimensional NumPy arrays in Python.

Now we'll use the dot method of the NumPy area object in order to do

the calculation.

Not only a multiplication between two vectors is defined, also,

multiplication between a matrix and a vector.

So just take the first element of the first row and column of the matrix, and

multiply it with the first element of the vector.

[BLANK AUDIO] And then put it as the first element of the restarting vector.

Then repeat the steps for the second column's first element, and

the second element of the vector.

Finally, repeat the steps for the third column,

first row of the matrix with the third element of the vector.

And then, we repeat the same steps for the second row of the matrix.

And then we sum up all those components.

End result is a two element vector.

So this is nothing else than two vector dot products calculated in one go.

But the first vector is encoded in the first row of the matrix and

the second vector in the second row.

This is also very handy for SIMD or

GPU instruction sets where we can speed up those calculations dramatically.

Also, the notation becomes more concise.

As long as the number of columns matches the number of elements in the vector,

we can apply this calculation for arbitrary factors.

Vector matrix multiplication is just a special

case of matrix-matrix multiplication, but we do not need to cover this here.

Let's implement a vector matrix multiplication in NumPy.

We will start with the same vector, a and b,

and turn it into a matrix using a two dimensional NumPy array.

Now we can use the same overloaded dot method

in order to calculate the matrix vector multiplication.

Note that result is the same as two sequential vector-vector dot products.

But now, this function is expressed in a single call which can be

easily parallelized in an SIMD or GPU instruction set.

So this was enough math for now.

You should be able to understand most of the math in your networks.

So let's get started with our first neural network.

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