Manual Networking In A Week: How To Network In Seven Simple Steps (TYW)

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Module 2: Connecting to the Internet
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  2. 9 Things You Need To Know About Mesh Networks
  3. How to Develop Convolutional Neural Networks for Multi-Step Time Series Forecasting
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Implement the best practices for securing wireless network access, including methods for secure authentication and encryption. Describe common types of network security threats and attacks. Explain how software tools can mitigate network security threats. In this module, you will compare different types of network connections, particularly those in a home network. Today's home networks consists of many different components that connect wirelessly through a router to the Internet.

At the end of this module, you will be able to explain how Wi-Fi functions and how the home network manages all of those wireless conversations over the network. In this module, you will setup a wireless home network and connect various wireless devices. You will also compare and contrast the various ways in which a home network can connect to an ISP for Internet services. A lab to change the wireless settings for a smartphone or other mobile device is included as well.

In this module, you will learn about best practices in security to implement in a home network. Emphasis is placed explaining how the various security tools and operating system features are used to mitigate attacks. You are also exposed to how a simple firewall can be configured to filter or allow different types of traffic. Fabulous contents and guidance to learn and practice about the Networking Protocols. Encouraged me to dig a little deeper and g through resources outside Coursera to complete this course. Learned more about my home network than I thought was really going on.

It's nice to see that this is going to help me with my job and secure my house as well. Peer review assignments can only be submitted and reviewed once your session has begun. If you choose to explore the course without purchasing, you may not be able to access certain assignments. When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile.

If you only want to read and view the course content, you can audit the course for free. More questions? Visit the Learner Help Center. Browse Chevron Right. Information Technology Chevron Right. Home Networking Basics. Offered By. Cisco Networking Basics Specialization Cisco.

About this Course 37, recent views. Course 4 of 5 in the Cisco Networking Basics Specialization. Flexible deadlines. Flexible deadlines Reset deadlines in accordance to your schedule. Beginner Level. Hours to complete. Each week is approximately 2. Available languages. Chevron Left. Syllabus - What you will learn from this course. Reading 12 readings. About this Course 10m. Cisco Packet Tracer 10m. Connecting Home Devices 10m.

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Components of a Home Network 10m. Typical Home Network Routers 10m. The Electromagnetic Spectrum 10m. LAN Wireless Frequencies 10m. Wired Network Technologies 10m. What is Wi-Fi? Wireless Settings 10m.

9 Things You Need To Know About Mesh Networks

Wireless Channels 10m. Managing Multiple Conversations 10m. Quiz 1 practice exercise. Week 1 Quiz 30m. Video 1 video. First Time Setup 2m. Reading 13 readings. First Time Setup 5m. Asking the Right Questions 10m. Who Can Use My Network? What is an ISP? How Do I Connect to the Internet? Cable and DSL Connections 5m. Additional Connectivity Options 5m. Mobile Devices and Wi-Fi 5m. Wi-Fi Settings 10m.

Manually Configuring Wi-Fi Settings 10m. Configuring Data Settings 10m. This is a general benefit of feed-forward neural networks. An important secondary benefit of using CNNs is that they can support multiple 1D inputs in order to make a prediction. This is useful if the multi-step output sequence is a function of more than one input sequence. This can be achieved using two different model configurations.

In this tutorial, we will explore how to develop three different types of CNN models for multi-step time series forecasting; they are:. The models will be developed and demonstrated on the household power prediction problem. A model is considered skillful if it achieves performance better than a naive model, which is an overall RMSE of about kilowatts across a seven day forecast. We will not focus on the tuning of these models to achieve optimal performance; instead we will sill stop short at skillful models as compared to a naive forecast.

The chosen structures and hyperparameters are chosen with a little trial and error. In this section, we will develop a convolutional neural network for multi-step time series forecasting using only the univariate sequence of daily power consumption. Given some number of prior days of total daily power consumption, predict the next standard week of daily power consumption. The number of prior days used as input defines the one-dimensional 1D subsequence of data that the CNN will read and learn to extract features.

Some ideas on the size and nature of this input include:. There is no right answer; instead, each approach and more can be tested and the performance of the model can be used to choose the nature of the input that results in the best model performance. One sample will be comprised of seven time steps with one feature for the seven days of total daily power consumed. This is a good start. The data in this format would use the prior standard week to predict the next standard week. A problem is that instances is not a lot for a neural network. A way to create a lot more training data is to change the problem during training to predict the next seven days given the prior seven days, regardless of the standard week.

This only impacts the training data, the test problem remains the same: predict the daily power consumption for the next standard week given the prior standard week. The training data is provided in standard weeks with eight variables, specifically in the shape [, 7, 8]. The first step is to flatten the data so that we have eight time series sequences. We then need to iterate over the time steps and divide the data into overlapping windows; each iteration moves along one time step and predicts the subsequent seven days.

We can do this by keeping track of start and end indexes for the inputs and outputs as we iterate across the length of the flattened data in terms of time steps. We can also do this in a way where the number of inputs and outputs are parameterized e. This multi-step time series forecasting problem is an autoregression. That means it is likely best modeled where that the next seven days is some function of observations at prior time steps.

This and the relatively small amount of data means that a small model is required. We will use a model with one convolution layer with 16 filters and a kernel size of 3. This means that the input sequence of seven days will be read with a convolutional operation three time steps at a time and this operation will be performed 16 times. This is then interpreted by a fully connected layer before the output layer predicts the next seven days in the sequence.

We will use the mean squared error loss function as it is a good match for our chosen error metric of RMSE. We will use the efficient Adam implementation of stochastic gradient descent and fit the model for 20 epochs with a batch size of 4.

Building a Home Network

The small batch size and the stochastic nature of the algorithm means that the same model will learn a slightly different mapping of inputs to outputs each time it is trained. This means results may vary when the model is evaluated. You can try running the model multiple times and calculating an average of model performance. Now that we know how to fit the model, we can look at how the model can be used to make a prediction. Generally, the model expects data to have the same three dimensional shape when making a prediction. In this case, the expected shape of an input pattern is one sample, seven days of one feature for the daily power consumed:.

Data must have this shape when making predictions for the test set and when a final model is being used to make predictions in the future. If you change the number of input days to 14, then the shape of the training data and the shape of new samples when making predictions must be changed accordingly to have 14 time steps.

It is a modeling choice that you must carry forward when using the model. This means that we have the observations available for the prior week in order to predict the coming week. These are collected into an array of standard weeks, called history. In order to predict the next standard week, we need to retrieve the last days of observations. As with the training data, we must first flatten the history data to remove the weekly structure so that we end up with eight parallel time series.

Next, we need to retrieve the last seven days of daily total power consumed feature number 0. We will parameterize as we did for the training data so that the number of prior days used as input by the model can be modified in the future. We then make a prediction using the fit model and the input data and retrieve the vector of seven days of output. The forecast function below implements this and takes as arguments the model fit on the training dataset, the history of data observed so far, and the number of inputs time steps expected by the model. Your specific results may vary given the stochastic nature of the algorithm.

You may want to try running the example a few times. We can see that in this case, the model was skillful as compared to a naive forecast, achieving an overall RMSE of about kilowatts, less than kilowatts achieved by a naive model. A plot of the daily RMSE is also created.

The plot shows that perhaps Tuesdays and Fridays are easier days to forecast than the other days and that perhaps Saturday at the end of the standard week is the hardest day to forecast. In this case, we can see a further drop in the overall RMSE, suggesting that further tuning of the input size and perhaps the kernel size of the model may result in better performance. Comparing the per-day RMSE scores, we see some are better and some are worse than using seventh inputs. This may suggest a benefit in using the two different sized inputs in some way, such as an ensemble of the two approaches or perhaps a single model e.

In this section, we will update the CNN developed in the previous section to use each of the eight time series variables to predict the next standard week of daily total power consumption. We will do this by providing each one-dimensional time series to the model as a separate channel of input. The CNN will then use a separate kernel and read each input sequence onto a separate set of filter maps, essentially learning features from each input time series variable.

This is helpful for those problems where the output sequence is some function of the observations at prior time steps from multiple different features, not just or including the feature being forecasted. It is unclear whether this is the case in the power consumption problem, but we can explore it nonetheless. First, we must update the preparation of the training data to include all of the eight features, not just the one total daily power consumed.

It requires a single line:. We also must update the function used to make forecasts with the fit model to use all eight features from the prior time steps.

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Again, another small change:. We will use 14 days of prior observations across eight of the input variables as we did in the final section of the prior section that resulted in slightly better performance.

How to Develop Convolutional Neural Networks for Multi-Step Time Series Forecasting

Finally, the model used in the previous section does not perform well on this new framing of the problem. The increase in the amount of data requires a larger and more sophisticated model that is trained for longer. With a little trial and error, one model that performs well uses two convolutional layers with 32 filter maps followed by pooling, then another convolutional layer with 16 feature maps and pooling.

The fully connected layer that interprets the features is increased to nodes and the model is fit for 70 epochs with a batch size of 16 samples. We now have all of the elements required to develop a multi-channel CNN for multivariate input data to make multi-step time series forecasts. We can see that in this case, the use of all eight input variables does result in another small drop in the overall RMSE score. The final day, Saturday, remains a challenging day to forecast, and Friday an easy day to forecast.

There may be some benefit in designing models to focus specifically on reducing the error of the harder to forecast days. It may be interesting to see if the variance across daily scores could be further reduced with a tuned model or perhaps an ensemble of multiple different models.

It may also be interesting to compare the performance for a model that uses seven or even 21 days of input data to see if further gains can be made. This requires a modification to the preparation of the model, and in turn, modification to the preparation of the training and test datasets. Starting with the model, we must define a separate CNN model for each of the eight input variables.

The configuration of the model, including the number of layers and their hyperparameters, were also modified to better suit the new approach. The new configuration is not optimal and was found with a little trial and error. The multi-headed model is specified using the more flexible functional API for defining Keras models. We can loop over each variable and create a sub-model that takes a one-dimensional sequence of 14 days of data and outputs a flat vector containing a summary of the learned features from the sequence.

Each of these vectors can be merged via concatenation to make one very long vector that is then interpreted by some fully connected layers before a prediction is made. As we build up the submodels, we keep track of the input layers and flatten layers in lists. This is so that we can specify the inputs in the definition of the model object and use the list of flatten layers in the merge layer. This is required when training the model, when evaluating the model, and when making predictions with a final model. We can achieve this by creating a list of 3D arrays, where each 3D array contains [ samples, timesteps, 1 ], with one feature.

If this is a problem, you can comment out this line. Next, we can update the preparation of input samples when making a prediction for the test dataset. We must perform the same change, where an input array of [1, 14, 8] must be transformed into a list of eight 3D arrays each with [1, 14, 1]. We can see that in this case, the overall RMSE is skillful compared to a naive forecast, but with the chosen configuration may not perform better than the multi-channel model in the previous section. We can also see a different, more pronounced profile for the daily RMSE scores where perhaps Mon-Tue and Thu-Fri are easier for the model to predict than the other forecast days.

It may be interesting to explore alternate methods in the architecture for merging the output of each sub-model. In this tutorial, you discovered how to develop 1D convolutional neural networks for multi-step time series forecasting. Do you have any questions? Ask your questions in the comments below and I will do my best to answer. Hi Jason, thanks for the tutorial. Great article — thank you for the code examples. Question — what if I wanted to forecast out all of the variables separately rather than one total daily power consumption?

I would recommend treating it like a seq2seq problem and forecast n variables for each step in the output sequence. When you increase the number of training example by overlapping your data, do you not run the risk of overfitting your model? You are essentially giving the model the same data multiple times. Do you have any idea? Thanks Jason, i believe that the feature map is too small for the layers, which resulted in the error.

Also, one point to note is that my multi channel model is not doing as well as your example. Hi Jason, First of all congratulations and many thanks for the tutorials! As my understanding, song classification is a case of Time Series data right? Can you write a topic about that? Hi Jason, as always great tutorial!

7-year-old shows how to hack Wi-Fi in less than 11 MINUTES

I had a question concerning the multi-head CNN, would it be a good idea to use different CNN architecture instead of using the same one? Hi Jason, I noticed that loss was very high during training. Loss must approach 0 in some task. If normalizing features,how to invert scaling for actual and forecast in Walk-Forward Validation. Hi Jason, thanks for these examples. Do you see a difference between the results in using multi-channel vs multi-head CNN for multi-variate data? What is your recommendation on using these 2 different approaches?

Try both, see work works for you. I like multi-head with multi-channel so that I can use different kernel sizes on the same data — much like a resnet type design. Hi Jason, do you have any recommendations on when multi-channel and when multi-head approach would be better? I recommend using multi-channel and compare it to multichannel on multi-heads to allow the use of different kernel sizes.

Hi Jason Can you provide link of code how time series data can be converted to image form for input to CNN?

Anf how to convert in 2D? Thank you for this tutorial and for the book version as well. Could you please help with that. Thank you! Thank you for your reply.

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  • Hi Jason, thank you so much for your tutorial. I have tried this code on my data speed data during a sequence of time and it works very well. Please, have you idea what should i change in your code to have residual neural network?? Thanks a lot for your support. Thanks for the great tutorial! I wonder if the multivariate channel approach is applicable for high-dimensional data, e. To me it seems like you append sample i of the test set to history line in the first 2 examples and then you predict on the last 7 days of history, which is sample i of the test set.

    Please tell me where the error in reasoning is.. I am using walk-forward validation, which is a preferred approach for evaluating time series forecasting models. How can I predict day ahead or hour ahead prediction.

    subnetting is simple

    Should I use walk forward validation? My data are very few at the moment, 30 days of timesteps each day of a sensor measurement. Then basically i followed your instructions on how to structure the rest of the network to see how it works but i get an error when i run it with any number of batch size. Thank you in advance for your time and your extremely helpful tutorials i have used before.

    Apologies if it is something obsolete or something out of your knowledge. This is the error in case that aids your understanding : ————————————————————————— ValueError Traceback most recent call last in 16 fit network 17 model. The error suggests a mismatch between the shape of your data and the expected shape of the model.

    Do you mind explaining how I could use the reframed data from that example to apply to apply to this example here for daily prediction? I have a question it might be silly. What about if you only have data corresponding to several months of each year instead of the 12 months. The first approach that comes to my mind is to treat each year as a different problem and just create a model for each one them or use the same model and compare results.

    Do you have any suggestions? Sorry if you have addressed this issue on another post, if so please let me know which one. Model with what you have and compare results to a naive method to see if you can develop a model that has skill.

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    If I have data collected for x number of months from y different years lets say 3 months , I am assuming I have 3 time series. Each time-series being the data collected from those x number of months corresponding to each year. Is there a way to combine those time series into a single dataset? Or it doesnt make sense at all to combine them? What about if you are only interested on the power consumption during summer months and you want to use the data from multiple years?

    Or what about if you are only given data from certain period of the years instead of the 12 months? Yes, if the 3 years are contiguous and observe the same feature, then it is one time series that spans 3 years. Test a few methods and discover what works best for your needs. Great article on time series thanks. I was wondering how you would deal with multiple step time series in the case where the steps are not contiguous time.

    For example one series could be value value1, 7 days ago, value2 25 days ago, value3 33 days ago, for predicting value 4 90 days ago. Then value1 2 days ago, value2 5 days ago, value3 7 days ago for predicting value 4 20 days ago, etc. Should the time be a features read in parallel then a bit like a 2D image , or should there b2 2 parallel CONV1D network being pooled at later step?

    Yes, you can try modeling as-is as a first step, then try using zero padding to make the intervals uniform and a masking layer to skip the padding for LSTM models. I managed to apply your method to my project now, thanks for the tutorial. I believe that the Y here is the The total active power consumed by the household kilowatts , am I right? There would be only one feature.

    Yes, you can use the fit model to make a prediction, e. HI Jason: I have a quick questions why do you use in the split data, data[] for train and [] for test? We have many rows of data, but in this tutorial we want to work with consistent weeks that start on one day and end on another sun-sat, or mon-sun or something. The data does not have this structure, so we clip off a row that start and some rows off the end to ensure we have this structure — so that we only have full weeks. Thank you very much for your great effort and tutorials.

    I have learned very much from your tutorials. I want to predict the price of electricity and I have only time series of electric price and time series of electric load. For example, I have days of electric prices and loads. For example how can I forecast the price of electricity for the day, day, day, ….

    Focus on the framing of the problem, what are the inputs to the model you will have at prediction time, and what do you need from the model for one prediction. You can frame the problem any way you wish. I recommend experimenting with a few different approaches to see what works best for your specific dataset.

    I have data in seconds means it changes every seconds what window size should I use???? As you restructure the data in weekly timeframe…. How to restructure my data which is in seconds… Plz help. Name required.