It’s learning from examples. That’s pretty much the deal.
At a very basic level, Deep Learning is a Machine Learning technique. It teaches a computer to filter inputs through layers to learn how to predict and classify information. Observations can be in the form of images, text, or sound.
The inspiration for Deep Learning is the way that the human brain filters information. Its purpose is to mimic how the human brain works to create some real magic.
It’s literally an artificial neural network.
In the human brain, there are about 100 billion neurons. Each neuron connects to about 100,000 of its neighbors. We’re kind of recreating that, but in a way and at a level that works for machines.
In our brains, a neuron has a body, dendrites, and an axon. The signal from one neuron travels down the axon and transfers to the dendrites of the next neuron. That connection where the signal passes is called a synapse.
Neurons by themselves are kind of useless. But when you have lots of them, they work together to create some serious magic. That’s the idea behind a deep learning algorithm! You get input from observation and you put your input into one layer. That layer creates an output which in turn becomes the input for the next layer, and so on. This happens over and over until your final output signal!
The neuron (node) gets a signal or signals ( input values), which pass through the neuron. That neuron delivers the output signal.
Think of the input layer as your senses: the things you see, smell, and feel, for example. These are independent variables for one single observation. This information is broken down into numbers and the bits of binary data that a computer can use. You’ll need to either standardize or normalize these variables so that they’re within the same range.
They use many layers of nonlinear processing units for feature extraction and transformation. Each successive layer uses the output of the previous layer for its input. What they learn forms a hierarchy of concepts. In this hierarchy, each level learns to transform its input data into a more and more abstract and composite representation.
Image by ahmedgad on Pixabay
That means that for an image, for example, the input might be a matrix of pixels. The first layer might encode the edges and compose the pixels. The next layer might compose an arrangement of edges. The next layer might encode a nose and eyes. The next layer might recognize that the image contains a face, and so on.
The input node takes in information in a numerical form. The information is presented as an activation value where each node is given a number. The higher the number, the greater the activation.
Based on the connection strength (weights) and transfer function, the activation value passes to the next node. Each of the nodes sums the activation values that it receives (it calculates the weighted sum) and modifies that sum based on its transfer function. Next, it applies an activation function. An activation function is a function that’s applied to this particular neuron. From that, the neuron understands if it needs to pass along a signal or not.
Each of the synapses gets assigned weights, which are crucial to Artificial Neural Networks (ANNs). Weights are how ANNs learn. By adjusting the weights, the ANN decides to what extent signals get passed along. When you’re training your network, you’re deciding how the weights are adjusted.
The activation runs through the network until it reaches the output nodes. The output nodes then give us the information in a way that we can understand. Your network will use a cost function to compare the output and the actual expected output. The model performance is evaluated by the cost function. It’s expressed as the difference between the actual value and the predicted value. There are many different cost functions you can use, you’re looking at what the error you have in your network is. You’re working to minimize loss function. (In essence, the lower the loss function, the closer it is to your desired output). The information goes back, and the neural network begins to learn with the goal of minimizing the cost function by tweaking the weights. This process is called backpropagation.
In forward propagation, information is entered into the input layer and propagates forward through the network to get our output values. We compare the values to our expected results. Next, we calculate the errors and propagate the info backward. This allows us to train the network and update the weights. (Backpropagation allows us to adjust all the weights simultaneously.) During this process, because of the way the algorithm is structured, you’re able to adjust all of the weights simultaneously. This allows you to see which part of the error each of your weights in the neural network is responsible for.
When you’ve adjusted the weights to the optimal level, you’re ready to proceed to the testing phase!
There are two different approaches to get a program to do what you want. First, there’s the specifically guided and hard-programmed approach. You tell the program exactly what you want it to do. Then there are neural networks. In neural networks, you tell your network the inputs and what you want for the outputs, and then you let it learn on its own.
By allowing the network to learn on its own, you can avoid the necessity of entering in all of the rules. You can create the architecture and then let it go and learn. Once it’s trained up, you can give it a new image and it will be able to distinguish output.
A feedforward network is a network that contains inputs, outputs, and hidden layers. The signals can only travel in one direction (forward). Input data passes into a layer where calculations are performed. Each processing element computes based upon the weighted sum of its inputs. The new values become the new input values that feed the next layer (feed-forward). This continues through all the layers and determines the output. Feedforward networks are often used in, for example, data mining.
A feedback network (for example, a recurrent neural network) has feedback paths. This means that they can have signals traveling in both directions using loops. All possible connections between neurons are allowed. Since loops are present in this type of network, it becomes a non-linear dynamic system which changes continuously until it reaches a state of equilibrium. Feedback networks are often used in optimization problems where the network looks for the best arrangement of interconnected factors.
Inputs to a neuron can either be features from a training set or outputs from the neurons of a previous layer. Each connection between two neurons has a unique synapse with a unique weight attached. If you want to get from one neuron to the next, you have to travel along the synapse and pay the “toll” (weight). The neuron then applies an activation function to the sum of the weighted inputs from each incoming synapse. It passes the result on to all the neurons in the next layer. When we talk about updating weights in a network, we’re talking about adjusting the weights on these synapses.
A neuron’s input is the sum of weighted outputs from all the neurons in the previous layer. Each input is multiplied by the weight associated with the synapse connecting the input to the current neuron. If there are 3 inputs or neurons in the previous layer, each neuron in the current layer will have 3 distinct weights: one for each synapse.
In a nutshell, the activation function of a node defines the output of that node.
The activation function (or transfer function) translates the input signals to output signals. It maps the output values on a range like 0 to 1 or -1 to 1. It’s an abstraction that represents the rate of action potential firing in the cell. It’s a number that represents the likelihood that the cell will fire. At it’s simplest, the function is binary: yes (the neuron fires) or no (the neuron doesn’t fire). The output can be either 0 or 1 (on/off or yes/no), or it can be anywhere in a range. If you were using a function that maps a range between 0 and 1 to determine the likelihood that an image is a cat, for example, an output of 0.9 would show a 90% probability that your image is, in fact, a cat.
In a nutshell, the activation function of a node defines the output of that node.
The activation function (or transfer function) translates the input signals to output signals. It maps the output values on a range like 0 to 1 or -1 to 1. It’s an abstraction that represents the rate of action potential firing in the cell. It’s a number that represents the likelihood that the cell will fire. At it’s simplest, the function is binary: yes (the neuron fires) or no (the neuron doesn’t fire). The output can be either 0 or 1 (on/off or yes/no), or it can be anywhere in a range.
What options do we have? There are many activation functions, but these are the four very common ones:
This is a step function. If the summed value of the input reaches a certain threshold the function passes on 0. If it’s equal to or more than zero, then it would pass on 1. It’s a very rigid, straightforward, yes or no function.
Example threshold function
This function is used in logistic regression. Unlike the threshold function, it’s a smooth, gradual progression from 0 to 1. It’s useful in the output layer and is used heavily for linear regression.
Example sigmoid function
This function is very similar to the sigmoid function. But unlike the sigmoid function which goes from 0 to 1, the value goes below zero, from -1 to 1. Even though this isn’t a lot like what happens in a brain, this function gives better results when it comes to training neural networks. Neural networks sometimes get “stuck” during training with the sigmoid function. This happens when there’s a lot of strongly negative input that keeps the output near zero, which messes with the learning process.
Example hyperbolic tangent function (tanh)
This might be the most popular activation function in the universe of neural networks. It’s the most efficient and biologically plausible. Even though it has a kink, it’s smooth and gradual after the kink at 0. This means, for example, that your output would be either “no” or a percentage of “yes.” This function doesn’t require normalization or other complicated calculations.
Example rectifier function
So let’s say, for example, your desired value is binary. You’re looking for a “yes” or a “no.” Which activation function do you want to use?
From the above examples, you could use the threshold function or you could go with the sigmoid activation function. The threshold function would give you a “yes” or “no” (1 or 0). The sigmoid function would be able to give you the probability of a yes.
If you were using a sigmoid function to determine how likely it is that an image is a cat, for example, an output of 0.9 would show a 90% probability that your image is, in fact, a cat.
Photo by minanafotos on Pixabay
You could use a brute force approach to adjust the weights and test thousands of different combinations. But even with the most simple neural network that has only five input values and a single hidden layer, you’ll wind up with 10⁷⁵ possible combinations.
Running this on the world’s fastest supercomputer would take longer than the universe has existed so far.
But if you go with gradient descent, you can look at the angle of the slope of the weights and find out if it’s positive or negative in order to continue to slope downhill to find the best weights on your quest to reach the global minimum.
If you go with gradient descent, you can look at the angle of the slope of the weights and find out if it’s positive or negative. This allows you to continue to slope downhill to find the best weights on your quest to reach the global minimum.
Gradient descent is an algorithm for finding the minimum of a function. The analogy you’ll see over and over is that of someone stuck on top of a mountain and trying to get down (find the minima). There’s heavy fog making it impossible to see the path, so she uses gradient descent to get down to the bottom of the mountain. She looks at the steepness of the hill where she is and proceeds down in the direction of the steepest descent. You should assume that the steepness isn’t immediately obvious. Luckily, she has a tool that can measure steepness!
Unfortunately, this tool takes forever.
She wants to use it as infrequently as she can to get down the mountain before dark. The real difficulty is choosing how often she wants to use her tool so she doesn’t go off track.
In this analogy, the person is the algorithm. The steepness of the hill is the slope of the error surface at that point. The direction she goes is the gradient of the error surface at that point. The tool she’s using is differentiation (the slope of the error surface can be calculated by taking the derivative of the squared error function at that point). The rate at which she travels before taking another measurement is the learning rate of the algorithm. It’s not a perfect analogy, but it gives you a good sense of what gradient descent is all about. The machine is learning the gradient, or direction, that the model should take to reduce errors.
gradient descent (simplified!)
Gradient descent requires the cost function to be convex, but what if it isn’t?
Normal gradient descent will get stuck at a local minimum rather than a global minimum, resulting in a subpar network. In normal gradient descent, we take all our rows and plug them into the same neural network, take a look at the weights, and then adjust them. This is called batch gradient descent. In stochastic gradient descent, we take the rows one by one, run the neural network, look at the cost functions, adjust the weights, and then move to the next row. Essentially, you’re adjusting the weights for each row.
Stochastic gradient descent has much higher fluctuations, which allows you to find the global minimum. It’s called “stochastic” because samples are shuffled randomly, instead of as a single group or as they appear in the training set. It looks like it might be slower, but it’s actually faster because it doesn’t have to load all the data into memory and wait while the data is all run together. The main pro for batch gradient descent is that it’s a deterministic algorithm. This means that if you have the same starting weights, every time you run the network you will get the same results. Stochastic gradient descent is always working at random. (You can also run mini-batch gradient descent where you set a number of rows, run that many rows at a time, and then update your weights.)
Many improvements on the basic stochastic gradient descent algorithm have been proposed and used, including implicit updates (ISGD), momentum method, averaged stochastic gradient descent, adaptive gradient algorithm (AdaGrad), root mean square propagation (RMSProp), adaptive moment estimation (Adam), and more.
So here’s a quick walkthrough of training an artificial neural network with stochastic gradient descent:
#deep-learning #machine-learning #artificial-intelligence #data-science
We at Inexture, strategically work on every project we are associated with. We propose a robust set of AI, ML, and DL consulting services. Our virtuoso team of data scientists and developers meticulously work on every project and add a personalized touch to it. Because we keep our clientele aware of everything being done associated with their project so there’s a sense of transparency being maintained. Leverage our services for your next AI project for end-to-end optimum services.
#deep learning development #deep learning framework #deep learning expert #deep learning ai #deep learning services
The Deep Learning DevCon 2020, DLDC 2020, has exciting talks and sessions around the latest developments in the field of deep learning, that will not only be interesting for professionals of this field but also for the enthusiasts who are willing to make a career in the field of deep learning. The two-day conference scheduled for 29th and 30th October will host paper presentations, tech talks, workshops that will uncover some interesting developments as well as the latest research and advancement of this area. Further to this, with deep learning gaining massive traction, this conference will highlight some fascinating use cases across the world.
Here are ten interesting talks and sessions of DLDC 2020 that one should definitely attend:
By Dipanjan Sarkar
**About: **Adversarial Robustness in Deep Learning is a session presented by Dipanjan Sarkar, a Data Science Lead at Applied Materials, as well as a Google Developer Expert in Machine Learning. In this session, he will focus on the adversarial robustness in the field of deep learning, where he talks about its importance, different types of adversarial attacks, and will showcase some ways to train the neural networks with adversarial realisation. Considering abstract deep learning has brought us tremendous achievements in the fields of computer vision and natural language processing, this talk will be really interesting for people working in this area. With this session, the attendees will have a comprehensive understanding of adversarial perturbations in the field of deep learning and ways to deal with them with common recipes.
By Divye Singh
**About: **Imbalance Handling with Combination of Deep Variational Autoencoder and NEATER is a paper presentation by Divye Singh, who has a masters in technology degree in Mathematical Modeling and Simulation and has the interest to research in the field of artificial intelligence, learning-based systems, machine learning, etc. In this paper presentation, he will talk about the common problem of class imbalance in medical diagnosis and anomaly detection, and how the problem can be solved with a deep learning framework. The talk focuses on the paper, where he has proposed a synergistic over-sampling method generating informative synthetic minority class data by filtering the noise from the over-sampled examples. Further, he will also showcase the experimental results on several real-life imbalanced datasets to prove the effectiveness of the proposed method for binary classification problems.
By Dongsuk Hong
About: This is a paper presentation given by Dongsuk Hong, who is a PhD in Computer Science, and works in the big data centre of Korea Credit Information Services. This talk will introduce the attendees with machine learning and deep learning models for predicting self-employment default rates using credit information. He will talk about the study, where the DNN model is implemented for two purposes — a sub-model for the selection of credit information variables; and works for cascading to the final model that predicts default rates. Hong’s main research area is data analysis of credit information, where she is particularly interested in evaluating the performance of prediction models based on machine learning and deep learning. This talk will be interesting for the deep learning practitioners who are willing to make a career in this field.
#opinions #attend dldc 2020 #deep learning #deep learning sessions #deep learning talks #dldc 2020 #top deep learning sessions at dldc 2020 #top deep learning talks at dldc 2020
In the previous blog, we looked into the fact why Few Shot Learning is essential and what are the applications of it. In this article, I will be explaining the Relation Network for Few-Shot Classification (especially for image classification) in the simplest way possible. Moreover, I will be analyzing the Relation Network in terms of:
Moreover, effectiveness will be evaluated on the accuracy, time required for training, and the number of required training parameters.
Please watch the GitHub repository to check out the implementations and keep updated with further experiments.
In few shot classification, our objective is to design a method which can identify any object images by analyzing few sample images of the same class. Let’s the take one example to understand this. Suppose Bob has a client project to design a 5 class classifier, where 5 classes can be anything and these 5 classes can even change with time. As discussed in previous blog, collecting the huge amount of data is very tedious task. Hence, in such cases, Bob will rely upon few shot classification methods where his client can give few set of example images for each classes and after that his system can perform classification young these examples with or without the need of additional training.
In general, in few shot classification four terminologies (N way, K shot, support set, and query set) are used.
At this point, someone new to this concept will have doubt regarding the need of support and query set. So, let’s understand it intuitively. Whenever humans sees any object for the first time, we get the rough idea about that object. Now, in future if we see the same object second time then we will compare it with the image stored in memory from the when we see it for the first time. This applied to all of our surroundings things whether we see, read, or hear. Similarly, to recognise new images from query set, we will provide our model a set of examples i.e., support set to compare.
And this is the basic concept behind Relation Network as well. In next sections, I will be giving the rough idea behind Relation Network and I will be performing different experiments on 102-flower dataset.
The Core idea behind Relation Network is to learn the generalized image representations for each classes using support set such that we can compare lower dimensional representation of query images with each of the class representations. And based on this comparison decide the class of each query images. Relation Network has two modules which allows us to perform above two tasks:
We can define the whole procedure in just 5 steps.
Few things to know during the training is that we will use only images from the set of selective class, and during the testing, we will be using images from unseen classes. For example, from the 102-flower dataset, we will use 50% classes for training, and rest will be used for validation and testing. Moreover, in each episode, we will randomly select 5 classes to create the support and query set and follow the above 5 steps.
That is all need to know about the implementation point of view. Although the whole process is simple and easy to understand, I’ll recommend reading the published research paper, Learning to Compare: Relation Network for Few-Shot Learning, for better understanding.
#deep-learning #few-shot-learning #computer-vision #machine-learning #deep learning #deep learning
How Deep Learning Works with Different Neuron Layers
Artificial Intelligence, Machine Learning, and Deep Learning come under Data Science. These terms are small but have changed technology. They have given a new direction to technology. The first step to understanding how deep learning works is to grasp the differences between AI, ML, and Deep Learning
#deep learning working #how deep learning works #machine learning
In this post, we will investigate how easily we can train a Deep Q-Network (DQN) agent (Mnih et al., 2015) for Atari 2600 games using the Google reinforcement learning library Dopamine. While many RL libraries exist, this library is specifically designed with four essential features in mind:
_We believe these principles makes __Dopamine _one of the best RL learning environment available today. Additionally, we even got the library to work on Windows, which we think is quite a feat!
In my view, the visualization of any trained RL agent is an absolute must in reinforcement learning! Therefore, we will (of course) include this for our own trained agent at the very end!
We will go through all the pieces of code required (which is** minimal compared to other libraries**), but you can also find all scripts needed in the following Github repo.
The general premise of deep reinforcement learning is to
“derive efficient representations of the environment from high-dimensional sensory inputs, and use these to generalize past experience to new situations.”
- Mnih et al. (2015)
As stated earlier, we will implement the DQN model by Deepmind, which only uses raw pixels and game score as input. The raw pixels are processed using convolutional neural networks similar to image classification. The primary difference lies in the objective function, which for the DQN agent is called the optimal action-value function
where_ rₜ is the maximum sum of rewards at time t discounted by γ, obtained using a behavior policy π = P(a_∣_s)_ for each observation-action pair.
There are relatively many details to Deep Q-Learning, such as Experience Replay (Lin, 1993) and an _iterative update rule. _Thus, we refer the reader to the original paper for an excellent walk-through of the mathematical details.
One key benefit of DQN compared to previous approaches at the time (2015) was the ability to outperform existing methods for Atari 2600 games using the same set of hyperparameters and only pixel values and game score as input, clearly a tremendous achievement.
This post does not include instructions for installing Tensorflow, but we do want to stress that you can use both the CPU and GPU versions.
Nevertheless, assuming you are using
Python 3.7.x, these are the libraries you need to install (which can all be installed via
tensorflow-gpu=1.15 (or tensorflow==1.15 for CPU version) cmake dopamine-rl atari-py matplotlib pygame seaborn pandas
#reinforcement-learning #q-learning #games #machine-learning #deep-learning #deep learning