# Linear algebra¶

Now that you can store and manipulate data, let’s briefly review the subset of basic linear algebra that you’ll need to understand most of the models. We’ll introduce all the basic concepts, the corresponding mathematical notation, and their realization in code all in one place. If you’re already confident in your basic linear algebra, feel free to skim or skip this chapter.

```
In [1]:
```

```
from mxnet import nd
```

## Scalars¶

If you never studied linear algebra or machine learning, you’re probably used to working with one number at a time. And know how to do basic things like add them together or multiply them. For example, in Palo Alto, the temperature is \(52\) degrees Fahrenheit. Formally, we call these values \(scalars\). If you wanted to convert this value to Celsius (using metric system’s more sensible unit of temperature measurement), you’d evaluate the expression \(c = (f - 32) * 5/9\) setting \(f\) to \(52\). In this equation, each of the terms \(32\), \(5\), and \(9\) is a scalar value. The placeholders \(c\) and \(f\) that we use are called variables and they stand in for unknown scalar values.

In mathematical notation, we represent scalars with ordinary lower cased letters (\(x\), \(y\), \(z\)). We also denote the space of all scalars as \(\mathcal{R}\). For expedience, we’re going to punt a bit on what precisely a space is, but for now, remember that if you want to say that \(x\) is a scalar, you can simply say \(x \in \mathcal{R}\). The symbol \(\in\) can be pronounced “in” and just denotes membership in a set.

In MXNet, we work with scalars by creating NDArrays with just one element. In this snippet, we instantiate two scalars and perform some familiar arithmetic operations with them, such as addition, multiplication, division and exponentiation.

```
In [2]:
```

```
x = nd.array([3.0])
y = nd.array([2.0])
print('x + y = ', x + y)
print('x * y = ', x * y)
print('x / y = ', x / y)
print('x ** y = ', nd.power(x,y))
```

```
x + y =
[5.]
<NDArray 1 @cpu(0)>
x * y =
[6.]
<NDArray 1 @cpu(0)>
x / y =
[1.5]
<NDArray 1 @cpu(0)>
x ** y =
[9.]
<NDArray 1 @cpu(0)>
```

We can convert any NDArray to a Python float by calling its `asscalar`

method. Note that this is typically a bad idea. While you are doing
this, NDArray has to stop doing anything else in order to hand the
result and the process control back to Python. And unfortunately isn’t
very good at doing things in parallel. So avoid sprinkling this
operation liberally throughout your code or your networks will take a
long time to train.

```
In [3]:
```

```
x.asscalar()
```

```
Out[3]:
```

```
3.0
```

## Vectors¶

You can think of a vector as simply a list of numbers, for example
`[1.0,3.0,4.0,2.0]`

. Each of the numbers in the vector consists of a
single scalar value. We call these values the *entries* or *components*
of the vector. Often, we’re interested in vectors whose values hold some
real-world significance. For example, if we’re studying the risk that
loans default, we might associate each applicant with a vector whose
components correspond to their income, length of employment, number of
previous defaults, etc. If we were studying the risk of heart attack in
hospital patients, we might represent each patient with a vector whose
components capture their most recent vital signs, cholesterol levels,
minutes of exercise per day, etc. In math notation, we’ll usually denote
vectors as bold-faced, lower-cased letters (\(\mathbf{u}\),
\(\mathbf{v}\), \(\mathbf{w})\). In MXNet, we work with vectors
via 1D NDArrays with an arbitrary number of components.

```
In [4]:
```

```
x = nd.arange(4)
print('x = ', x)
```

```
x =
[0. 1. 2. 3.]
<NDArray 4 @cpu(0)>
```

We can refer to any element of a vector by using a subscript. For
example, we can refer to the \(4\)th element of \(\mathbf{u}\)
by \(u_4\). Note that the element \(u_4\) is a scalar, so we
don’t bold-face the font when referring to it. In code, we access any
element \(i\) by indexing into the `NDArray`

.

```
In [5]:
```

```
x[3]
```

```
Out[5]:
```

```
[3.]
<NDArray 1 @cpu(0)>
```

## Length, dimensionality and shape¶

Let’s revisit some concepts from the previous section. A vector is just
an array of numbers. And just as every array has a length, so does every
vector. In math notation, if we want to say that a vector \(x\)
consists of \(n\) real-valued scalars, we can express this as
\(\mathbf{x} \in \mathcal{R}^n\). The length of a vector is commonly
called its \(dimension\). As with an ordinary Python array, we can
access the length of an NDArray by calling Python’s in-built `len()`

function.

We can also access a vector’s length via its `.shape`

attribute. The
shape is a tuple that lists the dimensionality of the NDArray along each
of its axes. Because a vector can only be indexed along one axis, its
shape has just one element.

```
In [6]:
```

```
x.shape
```

```
Out[6]:
```

```
(4,)
```

Note that the word dimension is overloaded and this tends to confuse
people. Some use the *dimensionality* of a vector to refer to its length
(the number of components). However some use the word *dimensionality*
to refer to the number of axes that an array has. In this sense, a
scalar *would have* \(0\) dimensions and a vector *would have*
\(1\) dimension.

**To avoid confusion, when we say 2D array or 3D array, we mean an array
with 2 or 3 axes repespectively. But if we say :math:`n`-dimensional
vector, we mean a vector of length :math:`n`.**

```
In [7]:
```

```
a = 2
x = nd.array([1,2,3])
y = nd.array([10,20,30])
print(a * x)
print(a * x + y)
```

```
[2. 4. 6.]
<NDArray 3 @cpu(0)>
[12. 24. 36.]
<NDArray 3 @cpu(0)>
```

## Matrices¶

Just as vectors generalize scalars from order \(0\) to order \(1\), matrices generalize vectors from \(1D\) to \(2D\). Matrices, which we’ll typically denote with capital letters (\(A\), \(B\), \(C\)), are represented in code as arrays with 2 axes. Visually, we can draw a matrix as a table, where each entry \(a_{ij}\) belongs to the \(i\)-th row and \(j\)-th column.

We can create a matrix with \(n\) rows and \(m\) columns in
MXNet by specifying a shape with two components `(n,m)`

when calling
any of our favorite functions for instantiating an `ndarray`

such as
`ones`

, or `zeros`

.

```
In [8]:
```

```
A = nd.arange(20).reshape((5,4))
print(A)
```

```
[[ 0. 1. 2. 3.]
[ 4. 5. 6. 7.]
[ 8. 9. 10. 11.]
[12. 13. 14. 15.]
[16. 17. 18. 19.]]
<NDArray 5x4 @cpu(0)>
```

Matrices are useful data structures: they allow us to organize data that has different modalities of variation. For example, rows in our matrix might correspond to different patients, while columns might correspond to different attributes.

We can access the scalar elements \(a_{ij}\) of a matrix \(A\)
by specifying the indices for the row (\(i\)) and column (\(j\))
respectively. Leaving them blank via a `:`

takes all elements along
the respective dimension (as seen in the previous section).

We can transpose the matrix through `T`

. That is, if \(B = A^T\),
then \(b_{ij} = a_{ji}\) for any \(i\) and \(j\).

```
In [9]:
```

```
print(A.T)
```

```
[[ 0. 4. 8. 12. 16.]
[ 1. 5. 9. 13. 17.]
[ 2. 6. 10. 14. 18.]
[ 3. 7. 11. 15. 19.]]
<NDArray 4x5 @cpu(0)>
```

## Tensors¶

Just as vectors generalize scalars, and matrices generalize vectors, we can actually build data structures with even more axes. Tensors give us a generic way of discussing arrays with an arbitrary number of axes. Vectors, for example, are first-order tensors, and matrices are second-order tensors.

Using tensors will become more important when we start working with images, which arrive as 3D data structures, with axes corresponding to the height, width, and the three (RGB) color channels. But in this chapter, we’re going to skip past and make sure you know the basics.

```
In [10]:
```

```
X = nd.arange(24).reshape((2, 3, 4))
print('X.shape =', X.shape)
print('X =', X)
```

```
X.shape = (2, 3, 4)
X =
[[[ 0. 1. 2. 3.]
[ 4. 5. 6. 7.]
[ 8. 9. 10. 11.]]
[[12. 13. 14. 15.]
[16. 17. 18. 19.]
[20. 21. 22. 23.]]]
<NDArray 2x3x4 @cpu(0)>
```

## Basic properties of tensor arithmetic¶

Scalars, vectors, matrices, and tensors of any order have some nice properties that we’ll often rely on. For example, as you might have noticed from the definition of an element-wise operation, given operands with the same shape, the result of any element-wise operation is a tensor of that same shape. Another convenient property is that for all tensors, multiplication by a scalar produces a tensor of the same shape. In math, given two tensors \(X\) and \(Y\) with the same shape, \(\alpha X + Y\) has the same shape (numerical mathematicians call this the AXPY operation).

```
In [11]:
```

```
a = 2
x = nd.ones(3)
y = nd.zeros(3)
print(x.shape)
print(y.shape)
print((a * x).shape)
print((a * x + y).shape)
```

```
(3,)
(3,)
(3,)
(3,)
```

Shape is not the the only property preserved under addition and multiplication by a scalar. These operations also preserve membership in a vector space. But we’ll postpone this discussion for the second half of this chapter because it’s not critical to getting your first models up and running.

## Sums and means¶

The next more sophisticated thing we can do with arbitrary tensors is to
calculate the sum of their elements. In mathematical notation, we
express sums using the \(\sum\) symbol. To express the sum of the
elements in a vector \(\mathbf{u}\) of length \(d\), we can
write \(\sum_{i=1}^d u_i\). In code, we can just call `nd.sum()`

.

```
In [12]:
```

```
print(x)
print(nd.sum(x))
```

```
[1. 1. 1.]
<NDArray 3 @cpu(0)>
[3.]
<NDArray 1 @cpu(0)>
```

We can similarly express sums over the elements of tensors of arbitrary shape. For example, the sum of the elements of an \(m \times n\) matrix \(A\) could be written \(\sum_{i=1}^{m} \sum_{j=1}^{n} a_{ij}\).

```
In [13]:
```

```
print(A)
print(nd.sum(A))
```

```
[[ 0. 1. 2. 3.]
[ 4. 5. 6. 7.]
[ 8. 9. 10. 11.]
[12. 13. 14. 15.]
[16. 17. 18. 19.]]
<NDArray 5x4 @cpu(0)>
[190.]
<NDArray 1 @cpu(0)>
```

A related quantity is the *mean*, which is also called the *average*. We
calculate the mean by dividing the sum by the total number of elements.
With mathematical notation, we could write the average over a vector
\(\mathbf{u}\) as \(\frac{1}{d} \sum_{i=1}^{d} u_i\) and the
average over a matrix \(A\) as
\(\frac{1}{n \cdot m} \sum_{i=1}^{m} \sum_{j=1}^{n} a_{ij}\). In
code, we could just call `nd.mean()`

on tensors of arbitrary shape:

```
In [14]:
```

```
print(nd.mean(A))
print(nd.sum(A) / A.size)
```

```
[9.5]
<NDArray 1 @cpu(0)>
[9.5]
<NDArray 1 @cpu(0)>
```

## Dot products¶

So far, we’ve only performed element-wise operations, sums and averages. And if this was we could do, linear algebra probably wouldn’t deserve it’s own chapter. However, one of the most fundamental operations is the dot product. Given two vectors \(\mathbf{u}\) and \(\mathbf{v}\), the dot product \(\mathbf{u}^T \mathbf{v}\) is a sum over the products of the corresponding elements: \(\mathbf{u}^T \mathbf{v} = \sum_{i=1}^{d} u_i \cdot v_i\).

```
In [15]:
```

```
x = nd.arange(4)
y = nd.ones(4)
print(x, y, nd.dot(x, y))
```

```
[0. 1. 2. 3.]
<NDArray 4 @cpu(0)>
[1. 1. 1. 1.]
<NDArray 4 @cpu(0)>
[6.]
<NDArray 1 @cpu(0)>
```

Note that we can express the dot product of two vectors `nd.dot(u, v)`

equivalently by performing an element-wise multiplication and then a
sum:

```
In [16]:
```

```
nd.sum(x * y)
```

```
Out[16]:
```

```
[6.]
<NDArray 1 @cpu(0)>
```

Dot products are useful in a wide range of contexts. For example, given
a set of weights \(\mathbf{w}\), the weighted sum of some values
\({u}\) could be expressed as the dot product
\(\mathbf{u}^T \mathbf{w}\). When the weights are non-negative and
sum to one \(\left(\sum_{i=1}^{d} {w_i} = 1\right)\), the dot
product expresses a *weighted average*. When two vectors each have
length one (we’ll discuss what *length* means below in the section on
norms), dot products can also capture the cosine of the angle between
them.

## Matrix-vector products¶

Now that we know how to calculate dot products we can begin to understand matrix-vector products. Let’s start off by visualizing a matrix \(A\) and a column vector \(\mathbf{x}\).

We can visualize the matrix in terms of its row vectors

where each \(\mathbf{a}^T_{i} \in \mathbb{R}^{m}\) is a row vector representing the \(i\)-th row of the matrix \(A\).

Then the matrix vector product \(\mathbf{y} = A\mathbf{x}\) is simply a column vector \(\mathbf{y} \in \mathbb{R}^n\) where each entry \(y_i\) is the dot product \(\mathbf{a}^T_i \mathbf{x}\).

So you can think of multiplication by a matrix \(A\in \mathbb{R}^{m \times n}\) as a transformation that projects vectors from \(\mathbb{R}^{m}\) to \(\mathbb{R}^{n}\).

These transformations turn out to be quite useful. For example, we can represent rotations as multiplications by a square matrix. As we’ll see in subsequent chapters, we can also use matrix-vector products to describe the calculations of each layer in a neural network.

Expressing matrix-vector products in code with `ndarray`

, we use the
same `nd.dot()`

function as for dot products. When we call
`nd.dot(A, x)`

with a matrix `A`

and a vector `x`

, `MXNet`

knows
to perform a matrix-vector product. Note that the column dimension of
`A`

must be the same as the dimension of `x`

.

```
In [17]:
```

```
nd.dot(A, x)
```

```
Out[17]:
```

```
[ 14. 38. 62. 86. 110.]
<NDArray 5 @cpu(0)>
```

## Matrix-matrix multiplication¶

If you’ve gotten the hang of dot products and matrix-vector multiplication, then matrix-matrix multiplications should be pretty straightforward.

Say we have two matrices, \(A \in \mathbb{R}^{n \times k}\) and \(B \in \mathbb{R}^{k \times m}\):

To produce the matrix product \(C = AB\), it’s easiest to think of \(A\) in terms of its row vectors and \(B\) in terms of its column vectors:

Note here that each row vector \(\mathbf{a}^T_{i}\) lies in \(\mathbb{R}^k\) and that each column vector \(\mathbf{b}_j\) also lies in \(\mathbb{R}^k\).

Then to produce the matrix product \(C \in \mathbb{R}^{n \times m}\) we simply compute each entry \(c_{ij}\) as the dot product \(\mathbf{a}^T_i \mathbf{b}_j\).

You can think of the matrix-matrix multiplication \(AB\) as simply
performing \(m\) matrix-vector products and stitching the results
together to form an \(n \times m\) matrix. Just as with ordinary dot
products and matrix-vector products, we can compute matrix-matrix
products in `MXNet`

by using `nd.dot()`

.

```
In [18]:
```

```
B = nd.ones(shape=(4, 3))
nd.dot(A, B)
```

```
Out[18]:
```

```
[[ 6. 6. 6.]
[22. 22. 22.]
[38. 38. 38.]
[54. 54. 54.]
[70. 70. 70.]]
<NDArray 5x3 @cpu(0)>
```

## Norms¶

Before we can start implementing models, there’s one last concept we’re going to introduce. Some of the most useful operators in linear algebra are norms. Informally, they tell us how big a vector or matrix is. We represent norms with the notation \(\|\cdot\|\). The \(\cdot\) in this expression is just a placeholder. For example, we would represent the norm of a vector \(\mathbf{x}\) or matrix \(A\) as \(\|\mathbf{x}\|\) or \(\|A\|\), respectively.

All norms must satisfy a handful of properties: 1. \(\|\alpha A\| = |\alpha| \|A\|\) 2. \(\|A + B\| \leq \|A\| + \|B\|\) 3. \(\|A\| \geq 0\) 4. If \(\forall {i,j}, a_{ij} = 0\), then \(\|A\|=0\)

To put it in words, the first rule says that if we scale all the
components of a matrix or vector by a constant factor \(\alpha\),
its norm also scales by the *absolute value* of the same constant
factor. The second rule is the familiar triangle inequality. The third
rule simple says that the norm must be non-negative. That makes sense,
in most contexts the smallest *size* for anything is 0. The final rule
basically says that the smallest norm is achieved by a matrix or vector
consisting of all zeros. It’s possible to define a norm that gives zero
norm to nonzero matrices, but you can’t give nonzero norm to zero
matrices. That’s a mouthful, but if you digest it then you probably have
grepped the important concepts here.

If you remember Euclidean distances (think Pythagoras’ theorem) from grade school, then non-negativity and the triangle inequality might ring a bell. You might notice that norms sound a lot like measures of distance.

In fact, the Euclidean distance \(\sqrt{x_1^2 + \cdots + x_n^2}\) is a norm. Specifically it’s the \(\ell_2\)-norm. An analogous computation, performed over the entries of a matrix, e.g. \(\sqrt{\sum_{i,j} a_{ij}^2}\), is called the Frobenius norm. More often, in machine learning we work with the squared \(\ell_2\) norm (notated \(\ell_2^2\)). We also commonly work with the \(\ell_1\) norm. The \(\ell_1\) norm is simply the sum of the absolute values. It has the convenient property of placing less emphasis on outliers.

To calculate the \(\ell_2\) norm, we can just call `nd.norm()`

.

```
In [19]:
```

```
nd.norm(x)
```

```
Out[19]:
```

```
[3.7416575]
<NDArray 1 @cpu(0)>
```

To calculate the L1-norm we can simply perform the absolute value and then sum over the elements.

```
In [20]:
```

```
nd.sum(nd.abs(x))
```

```
Out[20]:
```

```
[6.]
<NDArray 1 @cpu(0)>
```

## Norms and objectives¶

While we don’t want to get too far ahead of ourselves, we do want you to
anticipate why these concepts are useful. In machine learning we’re
often trying to solve optimization problems: *Maximize* the probability
assigned to observed data. *Minimize* the distance between predictions
and the ground-truth observations. Assign vector representations to
items (like words, products, or news articles) such that the distance
between similar items is minimized, and the distance between dissimilar
items is maximized. Oftentimes, these objectives, perhaps the most
important component of a machine learning algorithm (besides the data
itself), are expressed as norms.

## Intermediate linear algebra¶

If you’ve made it this far, and understand everything that we’ve
covered, then honestly, you *are* ready to begin modeling. If you’re
feeling antsy, this is a perfectly reasonable place to move on. You
already know nearly all of the linear algebra required to implement a
number of many practically useful models and you can always circle back
when you want to learn more.

But there’s a lot more to linear algebra, even as concerns machine learning. At some point, if you plan to make a career of machine learning, you’ll need to know more than we’ve covered so far. In the rest of this chapter, we introduce some useful, more advanced concepts.

### Basic vector properties¶

Vectors are useful beyond being data structures to carry numbers. In addition to reading and writing values to the components of a vector, and performing some useful mathematical operations, we can analyze vectors in some interesting ways.

One important concept is the notion of a vector space. Here are the conditions that make a vector space:

**Additive axioms**(we assume that x,y,z are all vectors): \(x+y = y+x\) and \((x+y)+z = x+(y+z)\) and \(0+x = x+0 = x\) and \((-x) + x = x + (-x) = 0\).**Multiplicative axioms**(we assume that x is a vector and a, b are scalars): \(0 \cdot x = 0\) and \(1 \cdot x = x\) and \((a b) x = a (b x)\).**Distributive axioms**(we assume that x and y are vectors and a, b are scalars): \(a(x+y) = ax + ay\) and \((a+b)x = ax +bx\).

### Special matrices¶

There are a number of special matrices that we will use throughout this tutorial. Let’s look at them in a bit of detail:

**Symmetric Matrix**These are matrices where the entries below and above the diagonal are the same. In other words, we have that \(M^\top = M\). An example of such matrices are those that describe pairwise distances, i.e. \(M_{ij} = \|x_i - x_j\|\). Likewise, the Facebook friendship graph can be written as a symmetric matrix where \(M_{ij} = 1\) if \(i\) and \(j\) are friends and \(M_{ij} = 0\) if they are not. Note that the*Twitter*graph is asymmetric - \(M_{ij} = 1\), i.e. \(i\) following \(j\) does not imply that \(M_{ji} = 1\), i.e. \(j\) following \(i\).**Antisymmetric Matrix**These matrices satisfy \(M^\top = -M\). Note that any arbitrary matrix can always be decomposed into a symmetric and into an antisymmetric matrix by using \(M = \frac{1}{2}(M + M^\top) + \frac{1}{2}(M - M^\top)\).**Diagonally Dominant Matrix**These are matrices where the off-diagonal elements are small relative to the main diagonal elements. In particular we have that \(M_{ii} \geq \sum_{j \neq i} M_{ij}\) and \(M_{ii} \geq \sum_{j \neq i} M_{ji}\). If a matrix has this property, we can often approximate \(M\) by its diagonal. This is often expressed as \(\mathrm{diag}(M)\).**Positive Definite Matrix**These are matrices that have the nice property where \(x^\top M x > 0\) whenever \(x \neq 0\). Intuitively, they are a generalization of the squared norm of a vector \(\|x\|^2 = x^\top x\). It is easy to check that whenever \(M = A^\top A\), this holds since there \(x^\top M x = x^\top A^\top A x = \|A x\|^2\). There is a somewhat more profound theorem which states that all positive definite matrices can be written in this form.

## Conclusions¶

In just a few pages (or one Jupyter notebook) we’ve taught you all the
linear algebra you’ll need to understand a good chunk of neural
networks. Of course there’s a *lot* more to linear algebra. And a lot of
that math *is* useful for machine learning. For example, matrices can be
decomposed into factors, and these decompositions can reveal
low-dimensional structure in real-world datasets. There are entire
subfields of machine learning that focus on using matrix decompositions
and their generalizations to high-order tensors to discover structure in
datasets and solve prediction problems. But this book focuses on deep
learning. And we believe you’ll be much more inclined to learn more
mathematics once you’ve gotten your hands dirty deploying useful machine
learning models on real datasets. So while we reserve the right to
introduce more math much later on, we’ll wrap up this chapter here.

If you’re eager to learn more about linear algebra, here are some of our favorite resources on the topic * For a solid primer on basics, check out Gilbert Strang’s book Introduction to Linear Algebra * Zico Kolter’s Linear Algebra Reivew and Reference