Different Optimization Algorithm for Deep Neural Networks: Complete Guide

In a world where Deep Learning is dominating everywhere, from Agriculture to Medical Science, Automobile, Education, Defense, Security, and other fields. The algorithm has to be efficient for Neural Networks to get better results. Optimization techniques become the centerpiece of deep learning algorithms when one expects better and faster results from the neural networks, and the choice between these optimization algorithms techniques can make a huge difference between waiting for hours or days for excellent accuracy. There is some main point about Optimization in Neural Network.

  1. Better Optimization Algorithm
  2. Better Activation Function
  3. Better Initialization Method
  4. Better Regularization

In this article, we will only focus on the Better Optimizing algorithm for Deep Neural Network (DNN). We will call this optimizing algorithm as a Learning algorithm for this article. There are several well-known Learning algorithms out there. let’s have a look at them.

Momentum-Based Learning Algorithm

  1. Vanilla Gradient Descent (GD)
  2. Momentum Based Gradient Descent
  3. Nesterov Accelerated Gradient Descent (NAG)

Batch-Learning Based Learning Algorithm

  1. Stochastic Update
  2. Mini-Batch Update

Adaptive Learning rate Based Learning Algorithm

  1. AdaGrad
  2. RMS Prop
  3. Adam (a mixture of RMS Prop and Momentum based GD)

#machine-learning #optimization-algorithms #learning-algorithms #deep-learning #neural-networks

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Different Optimization Algorithm for Deep Neural Networks: Complete Guide

Different Optimization Algorithm for Deep Neural Networks: Complete Guide

In a world where Deep Learning is dominating everywhere, from Agriculture to Medical Science, Automobile, Education, Defense, Security, and other fields. The algorithm has to be efficient for Neural Networks to get better results. Optimization techniques become the centerpiece of deep learning algorithms when one expects better and faster results from the neural networks, and the choice between these optimization algorithms techniques can make a huge difference between waiting for hours or days for excellent accuracy. There is some main point about Optimization in Neural Network.

  1. Better Optimization Algorithm
  2. Better Activation Function
  3. Better Initialization Method
  4. Better Regularization

In this article, we will only focus on the Better Optimizing algorithm for Deep Neural Network (DNN). We will call this optimizing algorithm as a Learning algorithm for this article. There are several well-known Learning algorithms out there. let’s have a look at them.

Momentum-Based Learning Algorithm

  1. Vanilla Gradient Descent (GD)
  2. Momentum Based Gradient Descent
  3. Nesterov Accelerated Gradient Descent (NAG)

Batch-Learning Based Learning Algorithm

  1. Stochastic Update
  2. Mini-Batch Update

Adaptive Learning rate Based Learning Algorithm

  1. AdaGrad
  2. RMS Prop
  3. Adam (a mixture of RMS Prop and Momentum based GD)

#machine-learning #optimization-algorithms #learning-algorithms #deep-learning #neural-networks

Vaughn  Sauer

Vaughn Sauer

1621440840

Ultimate Guide for Deep Learning with Neural Network in 2021

In deep learning with Keras, you don’t have to code a lot, but there are a few steps on which you need to step over slowly so that in the near future, you can create your models. The flow of modelling is to load data, define the Keras model, compile the Keras model, fit the Keras model, evaluate it, tie everything together, and make the predictions out of it.

But at times, you might find it confusing because of not having a good hold on the fundamentals of deep learning. Before starting your new deep learning with Keras project, make sure to go through this ultimate guide which will help you in revising the fundamentals of deep learning with Keras.

In the field of Artificial Intelligence, deep learning has become a buzzword which always finds its way in various conversations. When it comes to imparting intelligence to the machines, it has been since many years that we used Machine Learning (ML).

But, considering the current period, due to its supremacy in predictions, deep learning with Keras has become more liked and famous as compared to the old and traditional ML techniques.

Deep Learning

Machine learning has a subset in which the Artificial Neural Networks (ANN) is trained with a large amount of data. This subset is nothing but deep learning. Since a deep learning algorithm learns from experience, it performs the task repeatedly; every time it tweaks it a little intending to improve the outcome.

It is termed as ‘deep learning’ because the neural networks have many deep layers which enables learning. Deep learning can solve any problem in which thinking is required to figure out the problem.

**Keras **

There are many APIs, frameworks, and libraries available to get started with deep learning. But here’s why deep learning with Keras is beneficial. Keras is a high-level neural network application programming interface (API) which runs on the top of TensorFlow – which is an end-to-end machine learning platform and is an open-source. Not just Tensorflow, but also CNTK, Theano, PlaidML, etc.

It helps in commoditizing artificial intelligence (AI) and deep learning. The coding in Keras is portable, it means that using Keras you can implement a neural network while using Theano as a backend and then subsequently run it on Tensorflow by specifying the backend. Also further, it is not mandatory rather, not needed at all to change the code.

If you are wondering why deep learning is an important term in Artificial Intelligence or if you are lagging motivation to start learning deep learning with Keras, this google trends snap shows how people’s interest in deep learning has been growing steadily worldwide for the last few years.

#deep learning #deep learning with neural network #neural network

Various Optimization Algorithms For Training Neural Network

Many people may be using optimizers while training the neural network without knowing that the method is known as optimization. Optimizers are algorithms or methods used to change the attributes of your neural network such as weights and learning rate in order to reduce the losses.

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Optimizers help to get results faster

How you should change your weights or learning rates of your neural network to reduce the losses is defined by the optimizers you use. Optimization algorithms or strategies are responsible for reducing the losses and to provide the most accurate results possible.

We’ll learn about different types of optimizers and their advantages:

Gradient Descent

Gradient Descent is the most basic but most used optimization algorithm. It’s used heavily in linear regression and classification algorithms. Backpropagation in neural networks also uses a gradient descent algorithm.

Gradient descent is a first-order optimization algorithm which is dependent on the first order derivative of a loss function. It calculates that which way the weights should be altered so that the function can reach a minima. Through backpropagation, the loss is transferred from one layer to another and the model’s parameters also known as weights are modified depending on the losses so that the loss can be minimized.

algorithm: θ=θ−α⋅∇J(θ)

Advantages:

  1. Easy computation.
  2. Easy to implement.
  3. Easy to understand.

Disadvantages:

  1. May trap at local minima.
  2. Weights are changed after calculating gradient on the whole dataset. So, if the dataset is too large than this may take years to converge to the minima.
  3. Requires large memory to calculate gradient on the whole dataset.

Stochastic Gradient Descent

It’s a variant of Gradient Descent. It tries to update the model’s parameters more frequently. In this, the model parameters are altered after computation of loss on each training example. So, if the dataset contains 1000 rows SGD will update the model parameters 1000 times in one cycle of dataset instead of one time as in Gradient Descent.

θ=θ−α⋅∇J(θ;x(i);y(i)) , where {x(i) ,y(i)} are the training examples.

As the model parameters are frequently updated parameters have high variance and fluctuations in loss functions at different intensities.

Advantages:

  1. Frequent updates of model parameters hence, converges in less time.
  2. Requires less memory as no need to store values of loss functions.
  3. May get new minima’s.

Disadvantages:

  1. High variance in model parameters.
  2. May shoot even after achieving global minima.
  3. To get the same convergence as gradient descent needs to slowly reduce the value of learning rate.

#adam #neural-networks #optimization #machine-learning #deep-learning #deep learning

Sofia  Maggio

Sofia Maggio

1626106680

Neural networks forward propagation deep dive 102

Forward propagation is an important part of neural networks. Its not as hard as it sounds ;-)

This is part 2 in my series on neural networks. You are welcome to start at part 1 or skip to part 5 if you just want the code.

So, to perform gradient descent or cost optimisation, we need to write a cost function which performs:

  1. Forward propagation
  2. Backward propagation
  3. Calculate cost & gradient

In this article, we are dealing with (1) forward propagation.

In figure 1, we can see our network diagram with much of the details removed. We will focus on one unit in level 2 and one unit in level 3. This understanding can then be copied to all units. (ps. one unit is one of the circles below)

Our goal in forward prop is to calculate A1, Z2, A2, Z3 & A3

Just so we can visualise the X features, see figure 2 and for some more info on the data, see part 1.

Initial weights (thetas)

As it turns out, this is quite an important topic for gradient descent. If you have not dealt with gradient descent, then check this article first. We can see above that we need 2 sets of weights. (signified by ø). We often still calls these weights theta and they mean the same thing.

We need one set of thetas for level 2 and a 2nd set for level 3. Each theta is a matrix and is size(L) * size(L-1). Thus for above:

  • Theta1 = 6x4 matrix

  • Theta2 = 7x7 matrix

We have to now guess at which initial thetas should be our starting point. Here, epsilon comes to the rescue and below is the matlab code to easily generate some random small numbers for our initial weights.

function weights = initializeWeights(inSize, outSize)
  epsilon = 0.12;
  weights = rand(outSize, 1 + inSize) * 2 * epsilon - epsilon;
end

After running above function with our sizes for each theta as mentioned above, we will get some good small random initial values as in figure 3

. For figure 1 above, the weights we mention would refer to rows 1 in below matrix’s.

Now, that we have our initial weights, we can go ahead and run gradient descent. However, this needs a cost function to help calculate the cost and gradients as it goes along. Before we can calculate the costs, we need to perform forward propagation to calculate our A1, Z2, A2, Z3 and A3 as per figure 1.

#machine-learning #machine-intelligence #neural-network-algorithm #neural-networks #networks

Mckenzie  Osiki

Mckenzie Osiki

1623135499

No Code introduction to Neural Networks

The simple architecture explained

Neural networks have been around for a long time, being developed in the 1960s as a way to simulate neural activity for the development of artificial intelligence systems. However, since then they have developed into a useful analytical tool often used in replace of, or in conjunction with, standard statistical models such as regression or classification as they can be used to predict or more a specific output. The main difference, and advantage, in this regard is that neural networks make no initial assumptions as to the form of the relationship or distribution that underlies the data, meaning they can be more flexible and capture non-standard and non-linear relationships between input and output variables, making them incredibly valuable in todays data rich environment.

In this sense, their use has took over the past decade or so, with the fall in costs and increase in ability of general computing power, the rise of large datasets allowing these models to be trained, and the development of frameworks such as TensforFlow and Keras that have allowed people with sufficient hardware (in some cases this is no longer even an requirement through cloud computing), the correct data and an understanding of a given coding language to implement them. This article therefore seeks to be provide a no code introduction to their architecture and how they work so that their implementation and benefits can be better understood.

Firstly, the way these models work is that there is an input layer, one or more hidden layers and an output layer, each of which are connected by layers of synaptic weights¹. The input layer (X) is used to take in scaled values of the input, usually within a standardised range of 0–1. The hidden layers (Z) are then used to define the relationship between the input and output using weights and activation functions. The output layer (Y) then transforms the results from the hidden layers into the predicted values, often also scaled to be within 0–1. The synaptic weights (W) connecting these layers are used in model training to determine the weights assigned to each input and prediction in order to get the best model fit. Visually, this is represented as:

#machine-learning #python #neural-networks #tensorflow #neural-network-algorithm #no code introduction to neural networks