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Gaming on macOS has never been a huge selling point. And considering how small the Mac market is in comparison to the PC market, few developers have provided native support for macOS. But macOS has more power as a gaming platform than its reputation would suggest. There are five major ways you can game on your Mac.

1. GeForce Now Cloud Gaming

GeForce Now is the newest offering on the list, and it’s incredibly cool. It uses high-end video streaming and virtualization tools to let users play games on Nvidia’s hardware. This hardware, camped in data centers around the country, processes your input and sends back high-end graphics. This means your computer only needs to be capable of rendering YouTube videos to support high-end gaming on brand-new titles.

You might think you’d experience a ton of lag, but that’s far from the case. The only downside is that very slight input lag can make precision aiming in competitive shooters slightly more challenging.

But you get to play games like Overwatch and PUBG on a Mac. Better yet, the service is currently in a free open beta. You provide the games, and Nvidia provides the hardware. Learn more and install GeForce Now from Nvidia’s website.

2. Emulators

If you want to pay the newest games, GeForce Now is your best option. But if you have fond memories of older-generation console games, you can use emulators to play a ton of games for free. While you do technically need to pirate the games, it’s often considered a moral grey area by users that have purchased the game previously.

Emulators are available for just about every console up to the Playstation 3, but not every emulator works with macOS. Emulation requires such tremendous CPU power, and emulators must be written for the base OS’s code.

The process of using an emulator tends to vary for each platform, but the basic concept is the same for all. Run the emulator to create a virtualized environment that matches the console’s specs, then load the games from a separate file. Keep in mind that even with a newer system, you might not get awesome frame rates for newer games.

OpenEmu is a macOS-compatible combined emulator for many classic gaming platforms. Emulators for individual platforms can be downloaded at Emulator Zone.

3. Wineskin

Wineskin is a tool for making macOS ports of Windows software. It’s primarily used for games, but it can be used for non-gaming software, too. It works by creating “wrappers” that run concurrently with Windows programs, spoofing the operating environment the program is expecting. When you use Wineskin Winery to create a wrapper for each application you want to run, each game will have its own .app file in your Applications directory.

Wineskin is a free software project, and it’s regularly updated, but it doesn’t necessarily work for every game. As the Wineskin manual says, “It’s not always easy, and the same methods might not work for different programs.” It does tend to work best with older, less complicated games that use well-known software libraries. Right now, the platform only officially works on older versions of macOS, making support sketchy.

4. Steam

There’s a surprising number of games that are natively available for the Mac or ported to the platform. You can install Steam on any Mac, or take a look at their Mac games directory online. The depth might surprise you! Natively-supported games tend to lean towards strategy games and Valve’s own games, but there’s a wide variety of games available.

5. Boot Camp

Finally, you can always install Windows on your Mac with Boot Camp. It’s the most expensive option and one of the most complicated to set up. But once you get it running, it’s basically trouble-free.

Of course, You might find your a rig a little underpowered for the newest games at the highest quality settings: Macs are optimized for high-end gaming. However, you won’t have to worry about compatibility, emulation or support by a third party.

Conclusion

The most reliable tool for gaming on a Mac is Boot Camp, but it’s also resource- and time-intensive. GeForce Now is an amazing secondary option for Mac gamers who don’t want to split their hard drive in half for Windows. Retro gamers can emulate most classic games, and Steam offers native Mac support for a variety of games. Wineskin is something of a last resort but can often get older games and those no longer supported running on macOS. If you really want a Mac that can run games like the best Windows machines, build a high-end Hackintosh and dual-boot Windows.

Alexander Fox

Alexander Fox is a tech and science writer based in Philadelphia, PA with one cat, three Macs and more USB cables than he could ever use.

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Litecoin (Ltc): A Beginner’s Guide To The Peer

Litecoin is a decentralized, peer-to-peer cryptocurrency that has become increasingly popular in recent years. 

Litecoin

Designed to be a more accessible alternative to Bitcoin, Litecoin offers many of the same benefits but with some key differences. Like Bitcoin, Litecoin is a decentralized cryptocurrency that relies on blockchain technology to facilitate secure transactions. However, one of the key differences between Litecoin and Bitcoin is that Litecoin offers faster transaction confirmation times and improved storage efficiency.

Whether you’re new to the world of cryptocurrency or are already familiar with Litecoin and other digital currencies, this guide will help you understand what Litecoin is, how it works, and why it’s a valuable investment option.

What is Litecoin (LTC), and how does it work?

The first alternative cryptocurrency is generally regarded as Litecoin (LTC). It was introduced on October 13, 2011, as “the silver to Bitcoin’s gold,” and it is still one of the most valuable peer-to-peer (P2P) cryptocurrencies. Litecoin is a decentralized, open-source currency that relies on blockchain technology to facilitate fast and secure transactions.

Similarly to Bitcoin, Litecoin is a decentralized P2P cryptocurrency that relies on blockchain technology to facilitate fast and secure transactions. However, thanks to its Scrypt proof-of-work algorithm, Litecoin features faster transaction confirmation times and improved storage efficiency.

Additionally, Litecoin uses Segregated Witness (SegWit) technology to increase its transaction capacity, making it a more attractive option for merchants and businesses looking to accept Litecoin payments.

Litecoin: Is it really decentralized?

Litecoin

So Litecoin is a great option for those who want to be in control of their own finances. With Litecoin, you are your own bank, which gives you a greater level of freedom and control over your money. Additionally, Litecoin is a more secure and efficient payment option than traditional fiat currencies, so it’s perfect for online transactions.

However, despite Litecoin’s popularity and increased adoption by businesses, some critics question the true decentralization of Litecoin. Like Bitcoin, Litecoin is highly dependent on a small group of key stakeholders for its security and continued development. As such, whether Litecoin is truly decentralized remains a topic of debate among crypto enthusiasts.

Despite these concerns, Litecoin remains one of the top digital currencies in terms of popularity and market capitalization. If you’re interested in learning more about Litecoin and how it works, there are plenty of comprehensive resources available online. Ultimately, whether Litecoin is a truly decentralized cryptocurrency will depend on ongoing development efforts and community feedback moving forward.

Is Litecoin a proof-of-stake or proof-of-work cryptocurrency?

Cryptocurrency

Litecoin can be considered both a proof-of-stake and a proof-of-work cryptocurrency, as it uses both mechanisms to secure its network. Litecoin’s Scrypt proof-of-work algorithm provides security against attacks by miners, while Litecoin’s Segregated Witness technology makes Litecoin more efficient than traditional cryptocurrencies in terms of transaction capacity and processing times. 

Litecoin incorporates new features

The implementation of various features that were also suggested and eventually implemented on the Bitcoin network has supported the cryptocurrency’s first efforts. Litecoin is more sustainable than Bitcoin, as it processes transactions five times faster and consumes fewer resources.

SegWit

Segregated Witness technology is another Litecoin innovation that allows the cryptocurrency to be more efficient and scalable for mainstream adoption. Like Litecoin, many other cryptocurrencies are incorporating this innovative technology in order to increase transaction speeds, improve security, and reduce costs.

Lightning Network

A scaling solution called the Lightning Network essentially adds a new layer to the blockchain of a cryptocurrency, where transactions happen quickly, and fees are extremely low. The additional layer consists of user-generated payment channels. It was intended to be used with the Bitcoin blockchain at first. However, Litecoin has also adopted the Lightning Network and is expected to be one of the first cryptocurrencies to fully implement this new scaling solution.

MimbleWimble

Litecoin has also been exploring the potential integration of MimbleWimble, a scalable privacy protocol that is reportedly more efficient than other solutions. Litecoin’s developers believe that Litecoin can become a global payment system with this innovative technology and have even begun testing its capabilities on Litecoin’s test network.

How to buy and store Litecoin?

Create an account with the exchange of your choice if you intend to purchase Litecoin. You can assess a variety of factors before selecting an exchange, including security, costs, and convenience of usage.

Once you’ve purchased Litecoin, you can store it in a dedicated wallet or use an online wallet for day-to-day transactions. Litecoin wallets are available both on desktop and mobile platforms, so you can easily manage your Litecoin holdings wherever you go. Options include Litecoin Core, LoafWallet, Atomic Wallet, LiteVault, chúng tôi Litecoin QT, LitePal, and more. However, it’s important to keep in mind that any digital currency is only as secure as the wallet it’s stored in. So be sure to choose a reputable crypto wallet that offers robust security features and peace of mind.

Should you buy Litecoin?

Although Litecoin may not currently be the most popular cryptocurrency, it has consistently demonstrated substantial technological breakthroughs, making it a sound investment. Litecoin’s development team is dedicated to improving scalability and transaction speeds, and other cryptocurrencies are exploring similar innovations. 

Additionally, Litecoin offers the potential for higher returns on investment compared to other cryptocurrencies due to Litecoin’s relatively low price per coin and wide adoption.

Does Litecoin have a future?

Litecoin has been around the cryptocurrency scene since 2011, making it one of the oldest digital currencies on the market. Litecoin is a peer-to-peer cryptocurrency that was created to be an alternative to Bitcoin, with faster transaction speeds and lower fees. Litecoin has become increasingly popular over time and is now one of the top five largest cryptocurrencies in terms of market capitalization.

Despite Litecoin’s track record, it does have a future ahead of it. Litecoin is constantly evolving and adopting new technology to improve its performance, such as SegWit which improves transaction speed and security. Litecoin has also begun to attract more institutional investors in recent years, providing the project with additional capital for further development. Litecoin’s lightning network is also being implemented, which will allow Litecoin to be used for instant and low-cost payments.

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Beginner’s Guide To Machine Learning Explainability

Algorithms such as linear/logistic regression are easy to interpret based on model coefficients and Tree-based algorithms which can help us understand how it’s making decisions, with built-in support for feature importance and visualization.

Permutation Feature Importance

One simple method is Permutation Feature Importance, It is a model inspection technique that can be used for any fitted estimator when the data is tabular. The permutation feature importance is defined to be the decrease in a model score when a single feature value is randomly shuffled. This procedure breaks the relationship between the feature and the target, thus the drop in the model score is indicative of how much the model depends on the feature.

Algorithm

Train a model on a dataset

Calculate the error metrics or score(s) for the trained model for reference

Shuffled the validation data columns values one column(features) at a time and can be repeated K times

Using the shuffled data to evaluate the trained model using the same error or score metrics for each iteration

The features that affect the error metrics most are the important ones as it indicates a model dependency on that features

We can calculate the importance score for each feature as

              source

Where,

ij is the feature importance of feature j

s is the reference score calculated from the trained model

K is the number of iterations(shuffling operations) performed for a feature

sk,j is the score for kth iteration on feature j

We can use the python sklearn package for build-in permutation_importance function

from sklearn.inspection import permutation_importance

We will be using the Xgboost algorithm with default parameters on the Boston dataset

xg = xgboost.XGBRegressor() xg.fit(Xtrain, ytrain)

Now we will use the permutation_importance function on the test set to calculate the feature importance, we need to provide a trained model and number of shuffling iteration to perform (n_repeats parameter)

r = permutation_importance(xg, Xtest, ytest, n_repeats=30, random_state=0)

Here, we have set n_repeats=30

plt.figure(figsize=(10,4)) plt.bar(boston.feature_names,r.importances_mean) plt.xlabel('Features') plt.ylabel('Mean Importance') plt.title('Feature importance using Feature Permutation Importance'); Caveats

If two features are correlated and one of them is permuted then

The model still has the other correlated feature and in that case, both features will have lower importance value even if those are actually important

It can be biased by unrealistic data instances formed by permutation

Computationally expensive for a large number of features

A good practice is to drop one of the correlated features based on domain understanding and try to apply the Permutation Feature Importance algorithm which will provide better feature understanding.

Let’s discuss another method to interpret the black box models.

Global surrogate models

An interpretation model trained to approximate the predictions of the black-box models

It’s like solving black box Interpretability task using simpler and explainable models (such as linear regression, decision tree,..) i.e explaining machine learning using more machine learning

How does it work?

Get the predictions(yhat) from the black-box model

Select any simple and explainable model(linear reg., decision tree..) as per the use case

Train the selected model on the same dataset used for training the black-box model, using predictions(yhat) as the target

Measure the performance, as to how well the surrogate model approximates the behavior of the black-box model

Finally, we can interpret the global surrogate model

Advantages:

Flexible, as a selection of surrogate model, does not depend on the black-box model. If at some time we have a better performing black-box model in place of an existing black box, we do not have to change the method of interpretation

▪️ Interpretation becomes irrelevant if the black-box model is not performing well

LIME (Local Interpretation Model agnostic Explanation)

We have seen few model interpretation techniques for Global interpretation, what about Local Interpretation i.e when we want to understand how a model prediction was made for a particular observation.

Consider a loan approval model, what if a user request is declined, then the user has the right to question WHY? and authorities should know why the model has declined the user request and communicate the same to the user as they just can’t say that their system has rejected it instead they need to explain on what factors(features) the request is rejected.

▪️LIME can explain the predictions of any classifier or regressor in a faithful way, by approximating it locally with an interpretable model(linear reg., decision tree..)

▪️It tests what happens to the predictions when we feed variations of the data into the machine learning model

▪️Can be used on tabular, text, and image data

Source: LIME Paper

We can use python lime library to interpret models

import lime import lime.lime_tabular

We will be using the Xgboost algorithm with default parameters on the Boston dataset

xg = xgboost.XGBRegressor() xg.fit(Xtrain, ytrain)

Now we will create a lime explainer object, for which we have to specify the target column, features names, categorical features, and mode of the algorithm(regression or classification)

explainer = lime.lime_tabular.LimeTabularExplainer(Xtrain, feature_names=boston.feature_names, class_names=['price'], categorical_features=categorical_features, verbose=True, mode='regression')

We can use the explain_instance method of the explainer object to interpret a particular instance of data

exp = explainer.explain_instance(Xtest[i], xg.predict, num_features=5)

i is the index in test data that we need to interpret

we can visualize the interpretation output using the show_in_notebook method

exp.show_in_notebook(show_table=True)

We will get similar to below output

The Beginner’s Guide To Creating Your Own Streaming Service

Streaming is slowly killing cable TV and broadcast. According to a survey from CNBC, almost 60% of Americans are streaming and nearly all with Netflix. But most popular streaming services such as Youtube Premium and Netflix are not free. To enjoy their tailored content, you’ll need a credit card. 

But what about creating your own streaming media service? How magical would it be to create your own Netflix? 

Maybe your own personal media collection is starting to grow. From personal videos, movies, music, pictures, etc. — how can you get the most out of your media? How do you create your personal streaming service? 

In this Beginner’s Guide to creating your own streaming service, such as Netflix, we will get to know Plex, a free media software that lets you organize all your content, presents them on a device with beautiful dashboards and layouts, and enables you to navigate through it.

You also learn about hosting Plex on the cloud, how a Virtual Private Server (VPS) with managed Plex Server can be the key to storing all your content online and streaming it anywhere from any device.

In this Beginner’s Guide to creating your own streaming service

Starting with Plex.

Plex vs. Other Hosted Streaming Services like Netflix.

What is Plex Hosting?

Final Words.

Starting with Plex

Plex is a client and server media player platform that lets you organize your entire media collection, and allows you to stream it anywhere and from any device. With Plex, you can find and access all your media from a centralized platform. See your personal media, movie collection, streaming music, podcasts, web shows, and a lot more from your favorite device.  

Plex is based on three main components, the Plex Media Server, the Plex Client, and the Plex Central Server. 

The Plex Media Server is the core element— it lets you organize, track, and gives you access to your media. Although you can run the server at home and use clients at home, when you install it on a cloud hosting service, you can connect to it from anywhere. 

The Plex client is the place where the media files are accessible, displayed, and played. The client is installed on the playing device and connects to the server. Plex clients can run on smart TVs, smartphones, video game consoles, and more.

Finally, the Plex Central Server is the Plex’s remote server that stores and maintains your Plex account and learn our Plex complete guide. The Central Server is the place that allows clients to have access to media anywhere and anytime from any device.

Installing and running Plex is not so complicated. If you are running Plex on-premises, you’ll need an always-on server with high bandwidth and storage (depending on the amount of media).  

Plex vs Other Hosted Streaming Services like Netflix

Netflix is among the top streaming services for the millennials. Although it is the go-to-streaming platform for over 150 million users around the world, the service is not free. In order to watch its long list of movies and TV shows collection, you’ll need to subscribe. 

So what’s the catch? 

The main difference between Netflix and Plex is that Plex does not provide paid on-demand streaming or live TV streams as Netflix does. Plex is like Netflix for your own content. In other words, it lets you add your own media to your personal Plex Media Server. 

Advantages of Plex over Netflix?

Plex provides free and ad-supported streaming of thousands of movies and series. 

Access your own media using multiple Plex player apps from mobiles, smart TVs, web browsers, etc. 

Stream all your media by connecting to your Plex Media Server from anywhere and any device. 

Plex is fantastic, but to see your content anywhere, at any time, from any device, you need a dedicated server (or at least a powerful on-premises server). Not everybody can afford or even set up a dedicated server at home with high-speed bandwidth and large storage.  

What happens if you want to have full control of your Plex media server and still access all your content anytime, from anywhere in the world, and share your media library with your friends? 

You need Plex Hosting. 

What is Plex Hosting? 

A managed Plex Service does not only allows you to have full control of your online server, it also helps you with: 

Media transcodes with applications like Handbrake.

Some services may also come with media libraries (so you don’t need to create one!).

They allow you to create your own libraries online.

Ok, so a Plex Hosting service deals with all your media online, now how do you create your own media libraries? 

Services like a seedbox which are remote servers dedicated to torrenting and P2P. A seedbox is a remote client or dedicated Virtual Private Server (VPS) that is designed especially for downloading and uploading torrent files safely, anonymously, and at very high speeds.

When using a seedbox, you can download media from a P2P site, and transfer it to your personal computer through safe methods like FTP, or leave it in the VPS to stream it with Plex. A seedbox lets you store all your media content on the cloud, and host Plex Media Server. 

Also read: Top 10 Best Artificial Intelligence Software

Final Words. 

To create your own streaming service, if you are only going to stream your media at home, an on-premises server running Plex is sufficient. 

But if you are planning to take streaming to a whole new level, maybe watch your movies anywhere you go, with your own mobile, and even share your media libraries with friends, you’ll need a VPS service that hosts Plex. 

Diego Asturias

Writes about anything business and design related. With a decade of experience working in the tech industry

A Beginner’s Guide To Deep Learning Algorithms

This article was published as a part of the Data Science Blogathon.

Introduction to Deep Learning Algorithms

The goal of deep learning is to create models that have abstract features. This is accomplished by building models composed of many layers in which higher layers interpret the input while lower layers abstract the details.

As we train these deep learning networks, the high-level information from the input image produces weights that determine how information is interpreted.

These weights are generated by stochastic gradient descent algorithms based on backpropagation for updating the network parameters.

Training large neural networks on big data can take days or weeks, and it may require adjustments for optimal performance, such as adding more memory or computing power.

Sometimes it’s necessary to experiment with multiple architectures such as nonlinear activation functions or different regularization techniques like dropout or batch normalization.

Nearest Neighbor

Clustering algorithms divide a larger set of input into smaller sets so that those sets can be more easily visualized -Nearest Neighbor is one such algorithm because it breaks the input up based on the distance between data points.

For example, if we had an input set containing pictures of animals and cars, the nearest neighbor would break the inputs into two clusters. The nearest cluster would contain images with similar shapes (i.e., animals or cars), and the furthest cluster would contain images with different shapes.

Convolutional Neural Networks (CNN)

Convolutional neural networks are a class of artificial neural networks that employ convolutional layers to extract features from the input. CNNs are frequently used in computer vision because they can process visual data with fewer moving parts, i.e., they’re efficient and run well on computers. In this sense, they fit the problem better than traditional deep learning models. The basic idea is that at each layer, one-dimensionality is dropped out of the input; so for a given pixel, there is a pooling layer for just spatial information, then another for just color channels, then one more for channel-independent filters or higher-level activation functions.

Long Short Term Memory Neural Network (LSTMNN)

Several deep learning algorithms can be combined in many different ways to produce models that satisfy certain properties. Today, we will discuss the Long Short-Term Memory Neural Network (LSTMNN). LSTM networks are great for detecting patterns and have been found to work well in NLP tasks, image recognition, classification, etc. The LSTMNN is a neural network that consists of LSTM cells.

Recurrent Neural Network ( RNN )

An RNN is an artificial neural network that processes data sequentially. In comparison to other neural networks, RNNs can understand arbitrary sequential data better and are better at predicting sequential patterns. The main issue with RNNs is that they require very large amounts of memory, so many are specialized for a single sequence length. They cannot process input sequences in parallel because the hidden state must be saved across time steps. This is because each time step depends on the previous time step, and future time steps cannot be predicted by looking at only one past time step.

Generative Adversarial Networks (GANs) Support Vector Machines (SVM)

One deep learning algorithm is Support Vector Machines (SVM). One of the most famous classification algorithms, SVM, is a numerical technique that uses a set of hyperplanes to separate two or more data classes. In binary classification problems, hyperplanes are generally represented by lines in a two-dimensional plane. Generally, an SVM is trained and used for a particular problem by tuning parameters that govern how much data each support vector will contribute to partitioning the space. The kernel function determines how one feature vector maps into an SVM; it could be linear or nonlinear depending on what is being modeled.

Artificial Neural Networks (ANN)

ANNs are networks that are composed of artificial neurons. The ANN is modeled after the human brain, but there are variations. The type of neuron being used and the type of layers in the network determine the behavior.

ANNs typically involve an input layer, one or more hidden layers, and an output layer. These layers can be stacked on top of each other and side by side. When a new piece of data comes into the input layer, it travels through the next layer, which might be a hidden layer where it does computations before going on to another layer until it reaches the output layer.

The decision-making process involves training an ANN with some set parameters to learn what outputs should come from inputs with various conditions.

Autoencoders Section: Compositional Pattern Producing Networks (CPPN)

Compositional Pattern Producing Networks (CPPN) is a kind of autoencoder, meaning they’re neural networks designed for dimensionality reduction. As their name suggests, CPPNs create patterns from an input set. The patterns created are not just geometric shapes but very creative and organic-looking forms. CPPN Autoencoders can be used in all fields, including image processing, image analysis, and prediction markets.

Conclusion

To summarize, deep learning algorithms are a powerful and complex technology capable of identifying data patterns. They enable us to parse information and recognize trends more efficiently than ever.

Furthermore, they help businesses make more informed decisions with their data. I hope this guide has given you a better understanding of deep learning and why it is important for the future.

There are many deep learning algorithms, but the most popular ones used today are Recurrent Neural Networks (RNN) and Convolutional Neural Networks (CNN). 

I would recommend taking some time to learn about these two approaches on your own to decide which one might be best for your situation.

The media shown in this article is not owned by Analytics Vidhya and is used at the Author’s discretion. 

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Mac Wireless Problems? Guide To Troubleshooting Airport & Wireless Problems On Your Mac

Mac Wireless & Airport Connection Problem Troubleshooting: The Basics

Mac’s are amazingly reliable and have few problems, but it’s not incredibly unusual to run into problems connecting to a wireless network. If you’re having problems connecting your Mac wirelessly to an Airport or other WiFi router, check out this guide and try out these troubleshooting tips to fix your wireless internet connection.

* Turn Airport on & off – You can do this via the Airport menu bar or from the Network Preferences. This is the first thing you should try when troubleshooting Mac wireless problems.

* Reset your router – This is the second thing you should try doing. You can fix a surprising amount of wireless problems just by resetting the airport/router. All you need to do is turn the thing off for a few seconds and turn it back on.

* Reset your Cable/DSL modem – You’ll usually want to reset this in combination with your wireless router. Reset this first so the DHCP information will be pulled to the wireless router properly.

* Change Wireless Channels – sometimes your router’s wireless broadcast channel will interfere with a neighbors, be sure you have your router set to a unique channel. Even if it’s a weak signal there can still be interference.

* Make sure Wireless/Airport card software & firmware is up to date – This is usually done just by going to the Software Update menu, if there are any updates available for your Mac or Airport, install them.

Mac Wireless Troubleshooting: Intermediate

* Change wireless security protocol – You shouldn’t be using WEP anyway for security reasons, but sometimes changing from WEP to WPA/WPA2 or WPA to WPA2 can resolve wireless connection difficulties.

* Make sure router firmware is up to date – Check your router manufacturers website for firmware updates, if there are any available, install them.

* Delete and recreate connection – Try deleting and recreating/reestablishing the wireless connection, sometimes a setting can be corrupted and this may fix it.

* Create a new Network Location – Similar to the above suggestion, try creating a new and different wireless network location to see if it resolves the connection problems.

* Change DHCP auto settings to manual – sometimes there is a problem with the DHCP server, and if you manually set an IP address on the network you can be fine. Remember to set the IP to a high number so it wouldn’t interfere with other DHCP machines. As long as you have the subnet mask, router, and DNS settings configured manually as well, this shouldn’t be a problem.

* Disable “Wireless G/N/B only” mode – Sometimes a setting is selected that only broadcasts your wireless signal in Wireless B, G, or N mode (depending on the routers abilities). If this is set, try disabling it.

dscacheutil -flushcache

Mac Wireless Connection Problem Troubleshooting: Advanced

* Zap the PRAM – Reboot your Mac and hold Command+Option+P+R during restart until you hear another chime, let the Mac boot as usual.

* Delete Wireless Config files – Delete com.apple.internetconfigpriv.plist and com.apple.internetconfig.plist files from ~/Library/Preferences and reboot

* Trash your home directories SystemConfiguration – Remove all files within ~/Library/Preferences/SystemConfiguration/ and then reboot your Mac.

* Reset your Mac’s System Management Controller (SMC) – For MacBook and MacBook Pro’s: Shutdown the MacBook/Pro, remove the battery, disconnect the power, hold the Power Key for 15 seconds. Replace the battery, reconnect power, and zap the PRAM and wait for 2 chimes before letting the keys go. Let boot as usual.

Many of these tips are from our fixing dropped wireless airport connection problems in Snow Leopard article.

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