Sailesh Chandivale

However, the location of the stomach is a clue. The stomach's natural position is on the left side, where it can digest food more effectively. Gravity helps the waste travel from the small intestine to the large intestine.

A feedforward neural network (FNN) is an artificial neural network wherein connections between the nodes do not form a cycle. As such, it is different from its descendant: recurrent neural networks.

The feedforward neural network was the first and simplest type of artificial neural network devised. In this network, the information moves in only one direction—forward—from the input nodes, through the hidden nodes (if any) and to the output nodes. There are no cycles or loops in the network.

The simplest kind of feedforward neural network is a linear network, which consists of a single layer of output nodes; the inputs are fed directly to the outputs via a series of weights. The sum of the products of the weights and the inputs is calculated in each node. The mean squared errors between these calculated outputs and a given target values are minimized by creating an adjustment to the weights. This technique has been known for over two centuries as the method of least squares or linear regression. It was used as a means of finding a good rough linear fit to a set of points by Legendre (1805) and Gauss (1795) for the prediction of planetary movement.

The single-layer perceptron combines a linear neural network with a threshold function. If the output value is above some threshold (typically 0) the neuron fires and takes the activated value (typically 1); otherwise it takes the deactivated value (typically −1). Neurons with this kind of activation function are often called linear threshold units. In the literature the term perceptron often refers to networks consisting of just one of these units. Similar "neurons" were described in physics by Ernst Ising and Wilhelm Lenz for the Ising model in the 1920s, and by Warren McCulloch and Walter Pitts in the 1940s.

A perceptron can be created using any values for the activated and deactivated states as long as the threshold value lies between the two.

Perceptrons can be trained by a simple learning algorithm that is usually called the delta rule. It calculates the errors between calculated output and sample output data, and uses this to create an adjustment to the weights, thus implementing a form of gradient descent.

Single-layer perceptrons are only capable of learning linearly separable patterns; in 1969 in a famous monograph titled Perceptrons, Marvin Minsky and Seymour Papert showed that it was impossible for a single-layer perceptron network to learn an XOR function. Nonetheless, it was known that multi-layer perceptrons (MLPs) are capable of producing any possible boolean function. For example, already in 1967, Shun'ichi Amari trained an MLP by stochastic gradient descent.

Although a single threshold unit is quite limited in its computational power, it has been shown that networks of parallel threshold units can approximate any continuous function from a compact interval of the real numbers into the interval . This result can be found in Peter Auer, Harald Burgsteiner and Wolfgang Maass "A learning rule for very simple universal approximators consisting of a single layer of perceptrons".

A single-layer neural network can compute a continuous output instead of a step function. A common choice is the so-called logistic function:

With this choice, the single-layer network is identical to the logistic regression model, widely used in statistical modeling. The logistic function is one of the family of functions called sigmoid functions because their S-shaped graphs resemble the final-letter lower case of the Greek letter Sigma. It has a continuous derivative, which allows it to be used in backpropagation. This function is also preferred because its derivative is easily calculated:

(The fact that f {\displaystyle f} satisfies the differential equation above can easily be shown by applying the chain rule.)

If single-layer neural network activation function is modulo 1, then this network can solve XOR problem with a single neuron.

This class of networks consists of multiple layers of computational units, usually interconnected in a feed-forward way. Each neuron in one layer has directed connections to the neurons of the subsequent layer. In many applications the units of these networks apply a sigmoid function as an activation function. However sigmoidal activation functions have very small derivative values outside a small range and do not work well in deep neural networks due to the vanishing gradient problem.

The universal approximation theorem for neural networks states that every continuous function that maps intervals of real numbers to some output interval of real numbers can be approximated arbitrarily closely by a multi-layer perceptron with just one hidden layer. This result holds for a wide range of activation functions, e.g. for the sigmoidal functions.

Multi-layer networks use a variety of learning techniques. The first deep learning MLP was published by Alexey Grigorevich Ivakhnenko and Valentin Lapa in 1965. They trained their MLP layer by layer, incrementally adding layers until the remaining error was acceptable, continually pruning superfluous hidden units with the help of a separate validation set.

The first deep learning MLP trained by stochastic gradient descent was published in 1967 by Shun'ichi Amari. In computer experiments conducted by Amari's student Saito, a five layer MLP with two modifiable layers learned internal representations required to classify non-linearily separable pattern classes.

Today, the most popular method for training MLPs is back-propagation. The terminology "back-propagating errors" was introduced in 1962 by Frank Rosenblatt, but he did not know how to implement this, although Henry J. Kelley had a continuous precursor of backpropagation already in 1960 in the context of control theory. Modern backpropagation is actually Seppo Linnainmaa's general reverse mode of automatic differentiation (1970) for discrete connected networks of nested differentiable functions. It is an efficient application of the chain rule (derived by Gottfried Wilhelm Leibniz in 1673) to networks of differentiable nodes. In 1982, Paul Werbos applied backpropagation to MLPs in the way that has become standard. In 1985, David E. Rumelhart et al. published an experimental analysis of the technique. Many improvements have been implemented in subsequent decades.

During backpropagation, the output values are compared with the correct answer to compute the value of some predefined error-function. The error is then fed back through the network. Using this information, the algorithm adjusts the weights of each connection in order to reduce the value of the error function by some small amount. After repeating this process for a sufficiently large number of training cycles, the network will usually converge to some state where the error of the calculations is small. In this case, one would say that the network has learned a certain target function. To adjust weights properly, one applies a general method for non-linear optimization that is called gradient descent, due to Augustin-Louis Cauchy, who first suggested it in 1847. For this, the network calculates the derivative of the error function with respect to the network weights, and changes the weights such that the error decreases (thus going downhill on the surface of the error function). For this reason, back-propagation can only be applied on networks with differentiable activation functions.

In general, the problem of teaching a network to perform well, even on samples that were not used as training samples, is a quite subtle issue that requires additional techniques. This is especially important for cases where only very limited numbers of training samples are available. The danger is that the network overfits the training data and fails to capture the true statistical process generating the data. Computational learning theory is concerned with training classifiers on a limited amount of data. In the context of neural networks a simple heuristic, called early stopping, often ensures that the network will generalize well to examples not in the training set.

Other typical problems of the back-propagation algorithm are the speed of convergence and the possibility of ending up in a local minimum of the error function. Today, there are practical methods that make back-propagation in multi-layer perceptrons the tool of choice for many machine learning tasks.

One also can use a series of independent neural networks moderated by some intermediary, a similar behavior that happens in brain. These neurons can perform separably and handle a large task, and the results can be finally combined.

More generally, any directed acyclic graph may be used for a feedforward network, with some nodes (with no parents) designated as inputs, and some nodes (with no children) designated as outputs. These can be viewed as multilayer networks where some edges skip layers, either counting layers backwards from the outputs or forwards from the inputs. Various activation functions can be used, and there can be relations between weights, as in convolutional neural networks.

Examples of other feedforward networks include radial basis function networks, which use a different activation function.

Sometimes multi-layer perceptron is used loosely to refer to any feedforward neural network, while in other cases it is restricted to specific ones (e.g., with specific activation functions, or with fully connected layers, or trained by the perceptron algorithm).

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Introduction · Power on the server with CD inserted and make sure you have selected CD/DVD as your first boot device and you should see the

Searching for jobs is extremely stressful, but you know what’s even more stressful? Having to do your interview in another language.

But it doesn’t have to be if you prepare yourself for this opportunity that could really change your life, forever!

Here are 8 questions that they will likely ask, along with answers to help you rock (do well on) your interview.

Download: This blog post is available as a convenient and portable PDF that you can take anywhere. Click here to get a copy. (Download)

There is some great news when it comes to job interviews. It’s not all doom and gloom (bad). Most recruiters these days ask the interviewees (you) the same basic questions. So with a little preparation, you can speak very well at your interview, even if you don’t have a very advanced level of English.

After greeting, shaking hands and introducing yourself, the next thing that interviewers are probably going to ask you to do is to talk about yourself.

Now, this might seem easy for you – you’ve practicewd it in your English class so much, but they don’t want to hear every single detail. Avoid saying something like: I was born in Beijing. I love playing the computer and surfing the net. or I have two sisters. They don’t want to know everything about you. They want to know about you and your career growth; they want to know about you related to the job you’re applying for.

Also, make sure you don’t use any informal slang or make any basic grammar mistakes.

For example:

I’ve been working as a junior chef at a small Italian restaurant for 2 years and my duties included assisting the head chef and preparing salads. I have always been interested in food and cooking which was why I chose to follow this career path. I studied at ******* college, where I gained my first level cooking diploma.

When your interviewer asks you this question, they want to know all your positive qualities. These positive qualities need to relate to what they want and are looking for.

So before you head into your interview, make sure you do your research as to what kind of person suits this job, especially if you’re a newbie (new) and entering the workforce for the first time. Treat this question as a chance to advertise yourself – you are the product, now market yourself. The thing to remember here is not to just list a number of adjectives (anyone can do this). Instead, use examples to support your point.

For example, you could answer with any of the following:

To be punctual – to be on time.

I’m a punctual person. I always arrive early and complete my work on time. My previous job had a lot of deadlines (time when you must finish something by) and I made sure that I was organized and adhered to (respected) all my jobs.

To be a team-player – to work well with others. I consider myself to be a team-player. I like to work with other people and I find that it’s much easier to achieve something when everyone works together and communicates well.

To be ambitious – to have goals. I’m ambitious. I have always set myself goals and it motivates me to work hard. I have achieved my goals so far with my training, education and work experience and now I am looking for ways to improve myself and grow.

To take initiative – to do something without having to be told to do it. When I work, I always take initiative. If I see something that needs doing, I don’t wait for instruction, I do it. I believe that to be get anywhere in life, you need this quality.

To be proactive – To do things and make them happen. I’m proactive. When I think about things, I do them. I like to see results and it’s important in this industry to be proactive and responsible for your own actions.

To keep your cool – To stay calm in all kinds of situations. I think it’s really important to be able to stay calm when you’re working as a reporter. It can get really stressful, but one of my greatest qualities is that I can keep my cool and I don’t allow the pressure to get to me, which helps me achieve all my goals and remain focused. Here are a number of other words that can help you answer this question:

REMEMBER: It’s really important that you give good, solid answers and back them up with evidence otherwise it’s just going to sound like you’ve memorized what you’re saying. Some companies won’t directly ask you what your strengths are, they could ask the same thing, but using different words, such as:

Watching sample interview videos is a great way to get comfortable with the process and get an idea of the type of questions to expect. The large job postings company Indeed has a video that shares some tips for how to do an interview in 2022. You can watch it on FluentU with accurate, interactive subtitles.

FluentU has a few more videos about interviews that you might find useful (just search for “interview” to find all videos on the topic as well as any video that uses the word).

You can also practice your English skills with the program’s native English content, which includes good topics for small talk and current events, like popular music videos, movie clips and trailers and news segments. Get the iOS / Android app or try FluentU in your browser and prepare for your interview by listening to natural English.

What? I don’t have any weaknesses! Of course you do – no one’s perfect. Everyone has weaknesses, but what they’re checking for here is how you try to fix your weaknesses and they also want to know how self-aware (how much you know about yourself) you are.

Another trick here is to turn those weaker qualities into positive qualities. For example, your weakness is that you spend too much time on projects which makes you work slower. Turn that into a positive by saying: I sometimes am slower in completing my tasks compared to others because I really want to get things right. I will double or sometimes triple-check documents and files to make sure everything is accurate (correct).

Another great trick is to talk about a weakness (like being disorganized) and mention some methods that you are using to help overcome this: e.g. I have created a time-management system, which allows me to list all my duties and organize my deadlines so I have a clearer idea of what I need to do.

If you’re applying for your first job, this question is not for you.

However, if you’ve worked before, the interviewer wants to find out why you left your old job. Did you leave because you were fired? (Your old boss asked you to leave for doing something wrong). Did you quit? (Resign – Did you choose to stop working?) Or were you laid off? (Made redundant – no longer needed because the job is no longer available?)

If you chose to leave your old job, avoid saying anything negative about your old workplace or boss (even if this is true). The person or people interviewing you will just look at you in a negative way. You can say the following:

Here they want to know everything you’ve studied related to the job. For example your training and further education (e.g. university, polytechnic, college). You don’t need to tell them everything you’ve done since elementary school, just the important things.

Your: Degrees – 3-4 year qualification from university/college.

Diploma – A short-term qualification (e.g. 1 year) from college/university/polytechnic.

Certificate – A piece of paper showing your participation in a course. NB: Make sure you take all the necessary documents with you, as they may need proof!

If they ask you the question: tell us about your scholastic record, they want to know what kind of grades you received.

Here, they are asking about your goals. Again, it’s related to your career, not your personal life. So if having a family is on the list, don’t mention it. Be careful what you say here, you need to be ambitious, but NOT too ambitious as those interviewing you may see you as a threat (competition). You can mention: By then I will have…I would have liked to…

Here, they are asking you about how much money you would expect to earn from the job. Be reasonable. Make sure you do your research on the internet about what the average salary is. Do not say I don’t know, it makes you sound unsure. Be confident and name your price without selling yourself too short (going for less) or going too high. The truth of the matter is, they already have a salary in mind, but this is their way of checking if you know the industry and if you’re aware of your own skills.

Yes, you do! This is how an interviewer will usually finish the interview. They are not just being polite—they want you to speak.

Remember, they’re still judging you as you answer this question. So don’t ask anything that will make you sound silly, such as what kind of work does your company do? Or how much vacation time do I get each year? You want to find out more, and if you don’t ask any questions, then they may view this as you being not very interested in the job. Ask questions like: