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Make sure you check your mixes when the track is loud and when the track is quiet. When the track is quiet you should be able to hear the most important parts of your track clearly. Back up your music.
Losing your work is awful. Producing on a laptop is less enjoyable than having a bigger screen. Get the iMac instead of the Macbook. Or, buy a display for your laptop. Set it and come back later. Each piece serves the whole.
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Eventually it comes out. Put care and value into your releases. People respond to this. Find someone to do visual design for you. Album covers, web design, logos, etc. Always pay for visual art. If the person declines compensation buy them lunch. Whenever you struggle or doubt yourself remember why you got into this in the first place. Making music is fun. In the comments below, share what you wish you knew when you started producing.
Furthermore, if this kind of material excites or interests you, click here to check out my book: Electronic Music Arrangement: How to Arrange Electronic Music. Missing our best stuff. Sign up to be the first to learn about new tutorials, sales, giveaways and more.
He also drinks way too much tea. Download a FREE 40-minute tutorial from Matthew Weiss on mixing low end. If anyone is interested in getting some real work done in music, feel free to contact me via fb or email.
People constantly tell me my music is better than everything on the radio, that I should be signed to a major label, etc.
My music can be heard at YouTube. This definitely has opened my eyes or should i say ears. Some very good pointers as well as confirmations of what i am doing right and wrong. I will definitely keep this close. Thank you so much!!.
Get a Lenovo Z50.As a Realtor, I recommend that Buyers begin. Either way, today's rates are pretty darn close to yesterday. Rates Only Slightly Higher Despite Bond Market Weakness Comments (0) Read More by Matthew Graham on December 07 2017, 4:17 PMMortgage rates were best described as " unchanged " today, although that may not be the case tomorrow. The afternoon hours saw bond markets (which dictate rate movement) come under some pressure.
In the grand scheme of things, that pressure reinforces. Mortgage Rates Very Close to 1-Month Lows Comments (0) Read More by Matthew Graham on December 06 2017, 4:48 PMMortgage rates moved noticeably lower today as bond market improved for the 2nd day in a row--the first time that's happened since early November (when it comes to the bonds that relate to mortgage rates) his was the first time since early November.
Mortgage Rates Steady to Lower Comments (0) Read More by Matthew Graham on December 05 2017, 4:58 PMMortgage rates were more intuitive today with most lenders keeping things unchanged at first. This matched the movement in underlying bond markets, where today's trading levels in the morning (when most lenders put out the first rate sheet) were roughly.
Mortgage Rates Steady to Higher, Depending on Lender Comments (0) Read More by Matthew Graham on December 04 2017, 4:17 PMMortgage rates were distinctly mixed today, with some lenders clearly moving higher while others were effectively unchanged. The deciding factor is both simple and obvious. It has to do with Friday's wild action in the bond market (following the Flynn. Mortgage Rates Recover After Flynn Headlines Comments (0) Read More by Matthew Graham on December 01 2017, 3:33 PMMortgage rates were at their highest levels in roughly 1 month as of yesterday afternoon.
That ran counter to many of the mortgage rate news stories that came out throughout the day due to said stories using Freddie Mac's Primary Mortgage Market Survey. Mortgage Rates Hit 1-Month Highs Comments (0) Read More by Matthew Graham on November 30 2017, 4:12 PMThere are several news stories out today that reference LOWER rates this week.
These all rely on stale survey data that failed to account for changes over the past 2 days. Mortgage rates actually continued higher today at the same quicker pace seen yesterday. Mortgage Rates Bounce to 2-Week Highs Comments (0) Read More by Matthew Graham on November 29 2017, 3:14 PMMortgage rates moved higher today, with most lenders quoting their highest rates in at least 2 weeks.
But before you let that worry you too much, know that the range of available rates has been so narrow over the past 2 weeks that it didn't take much.
Bring your visual storytelling to the next level
Mortgage Rates Improve Slightly Despite Market Volatility Comments (0) Read More by Matthew Graham on November 28 2017, 4:47 PMMortgage rates improved modestly today as markets digested tax bill headlines and the confirmation hearing for new Fed Chair Jerome Powell.
As the head of the institution that has the biggest impact on short-term rate momentum, Powell is an important. Mortgage Rates Flat as Markets Get Back to Business Comments (0) Read More by Matthew Graham on November 27 2017, 4:58 PMMortgage rates were almost perfectly unchanged today as markets returned to full force following the extended Thanksgiving break. Bond markets (which dictate mortgage rate momentum) had been consolidating in a narrower and narrower range in the weeks.
Mortgage Rate Volatility Will Increase From Here Comments (0) Read More by Matthew Graham on November 24 2017, 1:53 PMMortgage rates were roughly unchanged today. That's not too surprising considering lenders don't tend to make big moves on the Friday after Thanksgiving, regardless of market conditions.
Moreover, they're working with a shorter-than-normal trading day. Mortgage Rates Move Modestly Lower Ahead of Holiday Comments (0) Read More by Matthew Graham on November 22 2017, 2:55 PMMortgage rates fell modestly today, with bond market strength both before and after the release of the Fed Minutes (a more detailed account of the Fed meeting that took place 3 weeks ago).
Stronger bond markets correlate with lower rates.The categorized scatterplot (in the graph below) shows the differences between patterns of correlations between dependent variables across the groups. Additionally, if the software has a brushing facility which supports animated brushing, you can select (i. Frequency or one-way tables represent the simplest method for analyzing categorical (nominal) data (refer to Elementary Concepts).
They are often used as one of the exploratory procedures to review how different categories of values are distributed in the sample. In practically every research project, a first "look" at the data usually includes frequency tables.
For example, in survey research, frequency tables can show the number of males and females who participated in the survey, the number of respondents from particular ethnic and racial backgrounds, and so on. Responses on some labeled attitude measurement scales (e. Customarily, if a data set includes any categorical data, then one of the first steps in the data analysis is to compute a frequency table for those categorical variables.
Purpose and Arrangement of Table. Crosstabulation is a combination of two (or more) frequency tables arranged such that each cell in the resulting table represents a unique combination of specific values of crosstabulated variables. Thus, crosstabulation allows us to examine frequencies of observations that belong to specific categories on more than one variable. By examining these frequencies, we can identify relations between crosstabulated variables. Only categorical (nominal) variables or variables with a relatively small number of different meaningful values should be crosstabulated.
Note that in the cases where we do want to include a continuous variable in a crosstabulation (e. The simplest form of crosstabulation is the 2 by 2 table where two variables are "crossed," and each variable has only two distinct values.
MALE FEMALE FEMALE FEMALE MALE. A B B A B. The resulting crosstabulation could look as follows. In general, this table shows us that more females than males chose the soda pop brand A, and that more males than females chose soda B. Thus, gender and preference for a particular brand of soda may be related (later we will see how this relationship can be measured). The values in the margins of the table are simply one-way (frequency) tables for all values in the table.
They are important in that they help us to evaluate the arrangement of frequencies in individual columns or rows.
Thus, the differences between the distributions of frequencies in individual rows (or columns) and in the respective margins informs us about the relationship between the crosstabulated variables.
Column, Row, and Total Percentages. The example in the previous paragraph demonstrates that in order to evaluate relationships between crosstabulated variables, we need to compare the proportions of marginal and individual column or row frequencies.
Such comparisons are easiest to perform when the frequencies are presented as percentages. Graphical Representations of Crosstabulations. For analytic purposes, the individual rows or columns of a table can be represented as column graphs.
However, often it is useful to visualize the entire table in a single graph. Stub-and-Banner tables, or Banners for short, are a way to display several two-way tables in a compressed form. This type of table is most easily explained with an example. Let us return to the survey of sports spectators example. In the table above, we see the two-way tables of expressed interest in Football by expressed interest in Baseball, Tennis, and Boxing.Topic Model Status Creating a topic model is a process that can take just a few seconds or a few days depending on the size of the dataset used as input and on the workload of BigML's systems.
The topic model goes through a number of states until its fully completed. Through the status field in the topic model you can determine when the topic model has been fully processed and ready to be used to create predictions. Thus when retrieving a topicmodel, it's possible to specify that only a subset of fields be retrieved, by using any combination of the following parameters in the query string (unrecognized parameters are ignored): Fields Filter Parameters Parameter TypeDescription fields optional Comma-separated list A comma-separated list of field IDs to retrieve.
To update a topic model, you need to PUT an object containing the fields that you want to update to the topic model' s base URL.
Once you delete a topic model, it is permanently deleted. If you try to delete a topic model a second time, or a topic model that does not exist, you will receive a "404 not found" response.
However, if you try to delete a topic model that is being used at the moment, then BigML. To list all the topic models, you can use the topicmodel base URL.
By default, only the 20 most recent topic models will be returned. You can get your list of topic models directly in your browser using your own username and API key with the following links. You can also paginate, filter, and order your topic models. Time Series Last Updated: Friday, 2017-10-27 12:23 A time series model is a supervised learning method to forecast the future values of a field based on its previously observed values.
It is used to analyze time based data when historical patterns can explain the future behavior such as stock prices, sales forecasting, website traffic, production and inventory analysis, weather forecasting, etc. A time series model needs to be trained with time series data, i. BigML implements exponential smoothing to train time series models. Time series data is modeled as a level component and it can optionally include a trend (damped or not damped) and a seasonality components as explained below:Forecast equation Level equation Forecast equation Level equation Trend equation Forecast equation Level equation Damped trend equation Forecast equation Level equation Trend equation Seasonality equation The different components can have variations, e.
As a result of combining the different variations for each component, several models can be trained for a given objective field. Note that BigML excludes certain combinations for numerical stability reasons such as additive errors with multiplicative trends or multiplicative error and trend with additive seasonality. BigML computes four different performance measures to select the best model for a given objective field.
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You can create a time series model selecting one or several fields from your dataset to use as objective fields to forecast their future values. You can also list all of your time series. This can be used to change the names of the fields in the time series with respect to the original names in the dataset or to tell BigML that certain fields should be preferred. Example: 100 name optional String,default is dataset's name The name you want to give to the new time series. The type of the field must be numerical.
Non-numeric fields will be ignored, and if not present, the right-most valid field in the dataset will be used. The period needs to be set taking into account the time interval of your instances and the seasonal frequency.
For example, for monthly data and annual seasonality, the period should be 12, for daily data and weekly seasonality, the period should be 7.
It can take values from 0 to 60. If the period is set to 1, there is no seasonality. If the period is 0, or not given, BigML will automatically learn the period in your data.
The range of successive instances to build the time series. Multiplicative seasonality models are only available when the objective field has strictly positive values (greater than 0). Example: 2 tags optional Array of Strings A list of strings that help classify and index your time series. If absent, the first datetime field in the dataset whose values are continuously either decreasing or increasing.
If not given, the server will auto-detect the first sequential datetime field in the dataset. All fields are optional: giving any two among start, end, and interval are enough for a full specification, since the remaining one can always be computed.They were readily available while we were on the road too - they provided us with a local cell phone which was very useful.
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