ML Math Skills - Data Science Central2017-11-20T17:01:21Zhttps://www.datasciencecentral.com/forum/topics/ml-math-skills?feed=yes&xn_auth=noMatrix product is basic to un…tag:www.datasciencecentral.com,2017-10-25:6448529:Comment:6413102017-10-25T13:35:14.864ZAbenezer Girmahttps://www.datasciencecentral.com/profile/AbenezerGirma
<p>Matrix product is basic to understand ML. I think this may help you to visualize what is happening. In addition, follow the suggestion given in above comments.…</p>
<p><a href="https://api.ning.com/files/SML9nsi*A-O2AKwAaW7pp8YCScGb2IYNjgC9DacRFlefgaM*YJhKkOXvqJnmgn8wCgolgn39YjhYxPQQKUBN7eODsHGtUrfw/CLMatrixMultExplanation.png" target="_self"><img class="align-full" height="126" src="https://api.ning.com/files/SML9nsi*A-O2AKwAaW7pp8YCScGb2IYNjgC9DacRFlefgaM*YJhKkOXvqJnmgn8wCgolgn39YjhYxPQQKUBN7eODsHGtUrfw/CLMatrixMultExplanation.png" width="305"></img></a></p>
<p>Matrix product is basic to understand ML. I think this may help you to visualize what is happening. In addition, follow the suggestion given in above comments.</p>
<p><a href="https://api.ning.com/files/SML9nsi*A-O2AKwAaW7pp8YCScGb2IYNjgC9DacRFlefgaM*YJhKkOXvqJnmgn8wCgolgn39YjhYxPQQKUBN7eODsHGtUrfw/CLMatrixMultExplanation.png" target="_self"><img src="https://api.ning.com/files/SML9nsi*A-O2AKwAaW7pp8YCScGb2IYNjgC9DacRFlefgaM*YJhKkOXvqJnmgn8wCgolgn39YjhYxPQQKUBN7eODsHGtUrfw/CLMatrixMultExplanation.png" class="align-full" height="126" width="305"/></a></p>
<p></p> I'd suggest some background i…tag:www.datasciencecentral.com,2017-09-11:6448529:Comment:6193712017-09-11T06:37:37.431ZNaveen Mathew Nathan Shttps://www.datasciencecentral.com/profile/NaveenMathewNathanS
I'd suggest some background in machine learning and neural networks before you start reading the book.<br />
<br />
1) Linear algebra is a must have!<br />
2) Look into the history of neural networks. Start with perceptron and feed forward network with 1 hidden layer before you move onto other architectures - they are fancy, but learning the limitations of perceptron, feed forward networks will truly inspire you to read more.<br />
3) Learn how to interpret weights of neural networks. Hidden layer weights may seem…
I'd suggest some background in machine learning and neural networks before you start reading the book.<br />
<br />
1) Linear algebra is a must have!<br />
2) Look into the history of neural networks. Start with perceptron and feed forward network with 1 hidden layer before you move onto other architectures - they are fancy, but learning the limitations of perceptron, feed forward networks will truly inspire you to read more.<br />
3) Learn how to interpret weights of neural networks. Hidden layer weights may seem insignificant, but they tell you exactly what/how the network learns.<br />
<br />
IMHO these 3 are necessary to understand why other architectures are required and the type of problems that each architecture can solve. I admit that deep learning is a beast, but it can be tamed by using a systematic approach. Ideally, go through a course on deep learning (there are many in YouTube) and use the book as primary reference material. Greetings Frederick,
What th…tag:www.datasciencecentral.com,2017-09-10:6448529:Comment:6192912017-09-10T17:31:06.409ZAvi Silterrahttps://www.datasciencecentral.com/profile/AviSilterra
<p>Greetings Frederick,</p>
<p></p>
<p>What the product operation line is saying (equation 2.5) is the <strong>cell C_{i,j}</strong> is defined as the <strong>dot product of A's row i</strong> and <strong>B's column j</strong>.</p>
<p>A How To video is <a href="https://www.khanacademy.org/math/precalculus/precalc-matrices/multiplying-matrices-by-matrices/v/matrix-multiplication-intro">Intro to matrix multiplication (video) | Khan Academy</a>. This is just the tip of the iceberg for linear…</p>
<p>Greetings Frederick,</p>
<p></p>
<p>What the product operation line is saying (equation 2.5) is the <strong>cell C_{i,j}</strong> is defined as the <strong>dot product of A's row i</strong> and <strong>B's column j</strong>.</p>
<p>A How To video is <a href="https://www.khanacademy.org/math/precalculus/precalc-matrices/multiplying-matrices-by-matrices/v/matrix-multiplication-intro">Intro to matrix multiplication (video) | Khan Academy</a>. This is just the tip of the iceberg for linear algebra. As other commentators noted, a refreshing class on linear algebra might be helpful.</p>
<p>For a mathematical reason why the definition of matrix multiplication is the way it is, see <a href="https://math.stackexchange.com/questions/271927/why-historically-do-we-multiply-matrices-as-we-do" target="_blank">https://math.stackexchange.com/questions/271927/why-historically-do-we-multiply-matrices-as-we-do.</a></p> There are really some great b…tag:www.datasciencecentral.com,2017-09-08:6448529:Comment:6187612017-09-08T17:53:02.864ZAdrian Thompsonhttps://www.datasciencecentral.com/profile/AdrianThompson
<p>There are really some great books I love Mathematics - From the Birth of Numbers which is brilliant in that you get great scope & history. Also I highly value Schaum's outlines series : Linear Algebra (Fully solved problems) walks you thru certain problems and how to solve, then gives you problems to solve, you give it a spin and the answers are provided so you can verify accordingly. I think we also have to give a shout out to the for Dummies series which are are written be great…</p>
<p>There are really some great books I love Mathematics - From the Birth of Numbers which is brilliant in that you get great scope & history. Also I highly value Schaum's outlines series : Linear Algebra (Fully solved problems) walks you thru certain problems and how to solve, then gives you problems to solve, you give it a spin and the answers are provided so you can verify accordingly. I think we also have to give a shout out to the for Dummies series which are are written be great people with great imaginations to convey a thought process in a fun manner.</p>
<p>Get the the library and have some fun ! </p>
<p></p> A good starting point would b…tag:www.datasciencecentral.com,2017-09-06:6448529:Comment:6178322017-09-06T07:50:44.154ZAndreas Wöhrlhttps://www.datasciencecentral.com/profile/AndreasWoehrl
<p>A good starting point would be <a href="https://www.coursera.org/learn/datasciencemathskills" target="_blank">https://www.coursera.org/learn/datasciencemathskills</a> also this one doesn't cover matrices as far as I remember. Andrew NG's Machine Learning course (<a href="https://www.coursera.org/learn/machine-learning/home/week/1" target="_blank">https://www.coursera.org/learn/machine-learning/home/week/1</a>) also has a linear algebra section in week 1 to refresh your knowledge.</p>
<p>A good starting point would be <a href="https://www.coursera.org/learn/datasciencemathskills" target="_blank">https://www.coursera.org/learn/datasciencemathskills</a> also this one doesn't cover matrices as far as I remember. Andrew NG's Machine Learning course (<a href="https://www.coursera.org/learn/machine-learning/home/week/1" target="_blank">https://www.coursera.org/learn/machine-learning/home/week/1</a>) also has a linear algebra section in week 1 to refresh your knowledge.</p> You may want to take a course…tag:www.datasciencecentral.com,2017-09-03:6448529:Comment:6167652017-09-03T11:43:00.169ZNicholas Halehttps://www.datasciencecentral.com/profile/NicholasHale
<p>You may want to take a course in Linear Algebra before getting into ML. It won't make a lot of sense unless you have a basic understanding of matrices. </p>
<p>You may want to take a course in Linear Algebra before getting into ML. It won't make a lot of sense unless you have a basic understanding of matrices. </p>