NEURAL NETWORK TUTORIAL | Toronto 2017-11-09T12:19:37+00:00

Neural Network tutorial

Neural Network Tutorial | Neural Networks Fundamentals using TensorFlow

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Neural Network tutorial

Neural Network tutorial

The objective of this neural network tutorial

The objective of this neural network tutorial is to provide an introduction to artificial neural networks (ANN) theory and practical applications using TensorFlow. The neural network tutorial course focuses on building artificial neural network models in TesorFlow for typical problems. In this neural network tutorial, you will receive a full training for using TensorFlow, which is the most popular library for building your own neural network. This neural network tutorial guarantees you that you will receive tools end theory to build your own neural network models from experts in the field.

Neural network tutorial is for anyone that wants to make a career in artificial neural networks. It doesn’t matter if you are a computer scientist or just a creative coder without machine learning background. In this neural network tutorial, it is covered basic fundaments of the state-of-the-art of artificial neural networks, and the basic of tensorflow and python.

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Neural Network Tutorial | Fundamentals using TensorFlow

Instructor: John Doe, Lamar George

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NEURAL NETWORK TUTORIAL

NEURAL NETWORK TUTORIAL | DEEP NEURAL NETWORK

Artificial Neural Networks are algorithms to perform certain specific tasks like clustering, classification, pattern recognition, and so on.

Artificial Neural Networks are inspired by brain neurons.  Artificial neurons are configured to perform specific tasks.

Artificial Neural Networks resemble the human brain in the way the knowledge is acquired through learning and in the way knowledge is stored within inter-neuron connection strengths (which is known as synaptic weights).

Artificial Neural Networks can be viewed as weighted directed graphs in which artificial neurons are nodes, and directed edges with weights are connections between neuron outputs and neuron inputs.

The Artificial Neural Network receives information from the external world in the form of pattern and image in vector form. Each input is multiplied by its corresponding weights (which is a linear equation). Weights are the information used by the neural network to solve a problem (or to weight the relationship between the input variable with the output variable). Typically weight represents the strength of the interconnection between neurons inside the artificial neural network.

The weighted inputs are all summed up inside the artificial neuron. In case the weighted sum is zero, the bias variable is added to make the output not- zero( Bias has the weight and input always equal to “1”). It is used a threshold value to limit the sum from 0 to the threshold value. Also, it is used an activation function to get the desired output. There are linear as well as the nonlinear activation function.

There are different types of layers in Artificial Neural Network:

  • Input layer– It contains Artificial Neurons (which receive input from the outside world on which network will learn)
  • Output layer – It contains units that represent the output information.
  • Hidden layer – These units are in between input and output layers. These layers transform the input into something that output unit could use in some way.

There are different types of artificial neural networks:

  • Perceptron – is an artificial neural network with only one artificial neuron, some input units, and one output unit.
  • Radial Basis Function Network – These networks are similar to forward Neural Network, but in this artificial neural network, it is used a radial basis function as activation function of these neurons.
  • Multilayer Perceptron-In these networks, it is used more than one hidden layer of neurons, unlike single layer perceptron.
  • Recurrent Neural Network– This type of Artificial Neural Network, the hidden layer neurons has self-connections.
  • Long /Short Term Memory Network (LSTM)– This type of Artificial Neural Network in which memory cell is incorporated inside hidden layer neurons is called LSTM network.
  • Hopfield Network– This type of artificial neural networks are fully interconnected in which each neuron is connected to every other neuron.
  • Boltzmann Machine Network – These networks are similar to Hopfield network, but (in this artificial neural network) some neurons are input, while others are hidden in nature.
  • Convolutional Neural Network– This artificial neural network is used for image recognition.
  • Modular Neural Network– In this artificial neural network, it is the combined structure of different types of the neural network like multilayer perceptron, Hopfield NetworkRecurrent Neural Network, and so on.
  • Physical Neural Network– In this type of Artificial Neural Network, electrically adjustable resistance material is used to emulate the function of synapse instead of software simulations performed in the neural network.

The “information bottleneck” is a new idea that explains the success of today’s artificial-intelligence algorithms. Indeed, this idea might also explain how human brains learn.

Developers with neural network tutorial can develop artificial neural network (ANN) systems to converse, drive cars, beat video games and Go champions, dream, paint pictures and help make scientific discoveries, and so on. Neural networks have a huge success but developers with neural networks tutorial don’t know how it is possible.

A developer with neural network tutorial knows that a deep neural network (like a brain) has layers of neurons. In this case, the artificial neurons are figments of computer memory.

A developer with neural network tutorial knows the ANN process starts when a neuron fires, then it sends signals to connected neurons in the layer above. During neural network tutorial (or learning process), connections in the network are strengthened or weakened as needed to make the system better at sending signals from input data. In the case of image recognition, the pixels of a photo (of a dog) go through the layers and neurons to learn that this picture represents a dog.

After the deep neural network tutorial, it has “learned” from thousands of sample dog photos, the ANN model can identify dogs in new photos as accurately as people can. Developers with neural network tutorial wonder what it is about deep learning that enables generalization (like a human brain).

Naftali Tishby, a computer scientist and neuroscientist from the Hebrew University of Jerusalem, presented a new theory explaining how deep learning works. Tishby presented the “information bottleneck” idea. This idea talks about how a network rids noisy input data (of extraneous details) as if by squeezing the information through a bottleneck, and the ANN only retains the most relevant features to general concepts.

Tishby explains how this squeezing procedure happens during deep learning.

This bottleneck idea could be very important in the future of deep neural network research for developers with neural network tutorial. The bottleneck idea not only gives an understanding why neural networks work as well as they do currently but also this bottleneck idea could be used as a tool for constructing new objectives and architectures of networks.

According to Tishby, this bottleneck idea is that the most important part of learning is actually forgetting.

Tishby got his bottleneck idea when he was thinking about how good humans are at speech recognition, which is a major challenge for AI at the time. Tishby realized that the most important was to know the relevant features of a spoken word. In general, when there is big data, we must know which signals are important.

Tishby said that this notion of relevant information was mentioned many times in history but never formulated correctly, which is very important for knowing how ANN works.

For example, imagine a complex data set (called X), which represents the pixels of a dog photo, and Y is a simpler variable represented by those data (like the word “dog”). All the “relevant” information is captured (such as X and Y).

Physicists David Schwab and Pankaj Mehta inspired Tishby. Tishby saw the potential of information bottleneck principle in 2014.

In 2015, Tishby and his student Noga Zaslavsky hypothesized that ANN is an information bottleneck procedure that compresses noisy data as much as possible while preserving information about what the data represent. Tishby and Shwartz-Ziv’s new experiments with ANN reveal how the bottleneck procedure actually happens in their study cases.

The basic algorithm used in deep-learning to tweak neural connections in response to data is called “stochastic gradient descent”. Developers with neural network tutorial use this algorithm to train the ANN, and each time the training data are fed into the network, a cascade of firing activity sweeps upward through the layers of artificial neurons. Then, when the signal reaches the top layer, the final firing pattern can be compared to the correct label (ground truth) for the image (which in this case is 0 for “no dog” and 1 for “dog”). The difference between the results of this step and the correct pattern is “back-propagated” down the layers (like a teacher correcting an exam), the algorithm strengthens or weakens each connection to make the network layer better at producing the correct output signal. After the neural network tutorial, common patterns in the training data become reflected in the strengths of the connections, and the network becomes expert at correctly labeling the data, such as by recognizing a dog, a word, or a 1.

Actually, Tishby and Shwartz-Ziv tracked how much information each layer of a deep neural network retained about the input data and how much information each one retained about the output label. The developers with neural network tutorial found that layer by layer, the networks converged to the information bottleneck theoretical bound: a theoretical limit derived in Tishby, Pereira and Bialek’s original paper that represents the absolute best the system can do at extracting relevant information. After the neural network tutorial, the ANN has compressed the input as much as possible without losing the ability to accurately predict its label (in case of a classification problem) or predict a numeric value (in case of a regression problem).

Tishby and Shwartz-Ziv found that ANN proceeds in two phases: a short “fitting” phase (where the ANN learns to label its training data), and a much longer “compression” phase (where it becomes good at generalization and it is measured the ANN’s performance of labeling new test data).

Then, ANN’s learning switches to the compression phase. The ANN starts to lose information (input data), and then ANN keeps track of the strongest features (where those correlations are most relevant to the output label). Using stochastic-gradient-descent algorithm, ANN has more or less accidental correlations in the training data.

This randomization of data and noise in the data are effectively the same as compressing the system’s representation of the input data. For example, some photos of dogs might have houses (which is noise) in the background, while others don’t (have noise). As an ANN’s cycles through these training photos, it might “forget” the correlation between houses (noise) and dogs (label) in some photos as other photos counteract it. This noise helps the ANN learning to form general concepts (features). Indeed, their experiments revealed that neural network tutorial has a generalization performance during the compression phase, which means that the ANN’s model is becoming better at labeling test data. It means that neural network tutorial is used to recognize dogs in photos might be tested on new photos of dogs or new photos of other things.

Some AI experts (and developers with neural network tutorial ) see bottleneck idea as one of many important theoretical insights about deep learning to have emerged recently.

Developers with neural network tutorial and AI practitioners hope that these explorations (such as bottleneck idea and neural network tutorial ) will uncover general insights about learning and intelligence.

Is that the brain may deconstruct the new letter into a series of strokes (which allows the conception of the letter as knowledge). Lake explained that the human brain builds a simple causal model of the letter, which is a shorter path to generalization.

Tishby’s idea is a more general form of human learning than in AI. Tishby’s idea gives a complete characterization of the problems that can be learned.

Actually, both real and artificial neural networks tutorial ideas are used to resolve real problems in which every detail matters and minute differences can throw off the whole result.

Generalizing is the traversing process of information through the bottleneck.

Artificial Neural Networks (ANNs) is used in many areas such as image processing and understanding, language modeling, language translation, speech processing, game playing, and many others.

ANN training with half precision while maintaining the network accuracy achieved with single precision is a new technique. Actually, this technique is called mixed-precision training since it uses both single- and half-precision representations.

This technique uses half-precision floating-point format, which consists of 1 sign bit, 5 bits of exponent, and 10 fractional bits.  Furthermore, supported exponent values fall into the [-24, 15] range, which means the format supports non-zero value magnitudes in the [2-24, 65,504] range. Since this is narrower than the [2-149, ~3.4×1038] range supported by single-precision format, training some networks requires extra consideration.

Using these techniques, NVIDIA and Baidu Research were able to match single-precision result accuracy for all networks that were trained (Mixed-Precision Training).

Furthermore, the NVIDIA Volta GPU architecture uses Tensor Core instructions, which multiply half precision matrices, accumulating the result into either single- or half-precision output. It was found that accumulation into single precision is critical to achieving good training results.  Then, the accumulated values are converted to half precision before writing to memory. Furthermore, the cuDNN and CUBLAS libraries provide a variety of functions that rely on Tensor Cores for arithmetic.

Actually, there are 4 types of tensors encountered when training ANNs: activations, activation gradients, weights, and weight gradients.

Actually, most of the half-precision range is not used by activation gradients (which tend to be small values with magnitudes below 1).

Indeed, a way to ensure that gradients fall into the range representable by half precision is to multiply the training loss with the scale factor (which is used to scale the).

Each epoch (or iteration) of neural network training updates the network weights by adding corresponding weight gradients.  These weight gradient magnitudes are often very smaller than corresponding weights, especially after multiplication with the learning rate (which is a number (or factor) that makes the weight bigger).

A simple remedy for the ANN that loses updates is to maintain and update a master copy of weights in single precision. In each epoch (iteration), a half-precision copy of the master weights is made and used in both the forward- and back-propagation, reaping the performance benefits. Indeed, the weight updates are converted to single-precision and used to update the master copy and the process is repeated in the next iteration. This technique is mixing half-precision storage with single-precision storage (only where it’s needed).

Furthermore, empirical results (of these techniques) suggest that while the half-precision range is narrower than that of single precision, it is sufficient for training ANNs for various application tasks as results match those of purely single-precision training.

In the case of image recognition, neural network tutorial is used to analyze pixels (for example, ANNs are used to recognize hand written numbers). Actually, Artificial Neural networks are made up of layers. The first layer of neurons represents each pixel in an image (where each pixel have a numeric value that represents the color of that pixel). Then, the data is passed through several more layers where a final layer determines which number between zero and nine the image best represents.

In the middle of the ANN, there are “weights”. These weights are given to each connection between layers help the network understand the inputs it’s receiving, and these functions set the value to one and zero (which is called threshold and the image is in black and white). When it is fully designed, the network can be really complex.

Microsoft and Amazon are teaming up.  Amazon Web Services (AWS) and  Microsoft’s AI and Research Group developed a new open-source deep learning interface called Gluon. Developers with neural network tutorial developed Gluon, which is an interface to prototype, build, train and deploy sophisticated machine learning models for the cloud, devices at the edge and mobile apps.

Developers with neural network tutorial use deep learning to train a computer to recognize patterns or unlock insights based on a set of rules for parsing a massive pool of data. Cloud companies and developers with neural network tutorial offer ways to speed up the process, but a fair amount of skill is required to get meaningful results such as neural network tutorial.

And most developers with neural network tutorial are interested in incorporating deep learning technology into their applications but they don’t have AWS or Microsoft expertise. Gluon will give those developers with neural network tutorial a way to tap into that expertise without having to invest nearly as much time and effort in understanding how to use machine learning techniques.

 Gluon allows developers with neural network tutorial to write deep-learning systems in the popular Python language and take advantage of deep learning templates developed by Microsoft and AWS. Gluon makes it possible and easier. Developers with neural network tutorial are very interested in learning more about Gluon.

Imagination Technologies will dramatically speed up artificial intelligence processing with the chip design.

Imagination is introducing a chip design to speed up artificial neural networks (ANNs). The chip component will be useful for neural network processing in applications such as mobile devices, automotive tech, and so on. Actually, ANNs have big advances in areas like pattern recognition, resulting in a huge explosion for artificial intelligence applications.

Actually, PowerVR Neural Network Accelerator has 2X the performance and uses half the bandwidth of the nearest competitor. Indeed, this chip is 8X more powerful than rival chips (digital signal processors).

AI processing is very important for developers with neural network tutorial because of power, bandwidth, performance, reliability, security, and latency.

Indeed, the accelerator will support multiple operating systems such as Linux and Android. Furthermore, the accelerator can be used in system-on-chip designs and deliver high performance and low power consumption.

Developers with neural network tutorial use neural network accelerators (NNAs), which could be a new class of processors and are likely to be as significant as central processing units and graphics processing units. Developers with neural network tutorial use both CPUs and GPUs, and now developers with neural network tutorial will use NNAs too.

Developers with neural network tutorial use ANNs for photography enhancement, predictive text enhancement in mobile devices, feature detection, eye tracking (in augmented reality and virtual reality headsets), pedestrian detection, driver alertness monitoring (in automotive safety systems), facial recognition, crowd behavior analysis in smart surveillance, online fraud detection, content advice, and predictive UX, speech recognition, response in virtual assistants, collision avoidance and subject tracking in drones.

Actually, nearly 79% of chip vendors said they were already using (or planning to use it) for neural networks (to perform computer vision functions in their products or services).

Actually, many developers with neural network tutorial are adopting ANNs algorithms to bring new perceptual capabilities to their products. Indeed, it is a challenge the processing performance for these ANNs algorithms. Furthermore, specialized processors such as PowerVR 2NX NNA, which are designed for ANNs algorithms, will increase the development of these ANNs algorithms in many new applications.

Imagination has also introduced two new PowerVR GPUs: the cost-sensitive PowerVR Series9XE and 9XM GPUs. Indeed, Apple used Imagination’s graphics technology in its smartphones in the past, but Apple is now creating its own graphics components.

It is expected that Artificial Intelligence (AI) will generate 15.7 trillion by 2030. The growth of the global GDP is 14%.

Neural network tutorial of Bigdataguys offers courses of neural networks. You can acquire a job as a neural network engineer in industries Google, Facebook, Uber, Microsoft, or any other company. The best way to learn about artificial neural networks (ANNs) is to take a course with us. Neural network tutorial covers the basic theory and practical examples to create your own artificial neural network.

Many companies need people with neural network tutorial to work with them in different types of problems. Bigdataguys gives you tools to artificial neural network.

The average salary for a neural network engineer is $149,465 per year

 

CURRICULUM

Lecture1.1 Creation, Initializing, Saving, and Restoring TensorFlow variables
Lecture1.2 Feeding, Reading and Preloading TensorFlow Data
Lecture1.3 How to use TensorFlow infrastructure to train models at scale
Lecture1.4 Visualizing and Evaluating models with TensorBoard

Lecture2.1 1.Inputs and Placeholders
Lecture2.2 2.Build the GraphS
Lecture2.3 Inference
Lecture2.4 Loss
Lecture2.5 Training
Lecture2.6 3.Train the Model
Lecture2.7 The Graph
Lecture2.8 The Session
Lecture2.9 Train Loop
Lecture2.10 4.Evaluate the Model
Lecture2.11 Build the Eval Graph
Lecture2.12 Eval Output

Lecture3.1 Activation functions
Lecture3.2 The perceptron learning algorithm
Lecture3.3 Binary classification with the perceptron
Lecture3.4 Document classification with the perceptron
Lecture3.5 Limitations of the perceptron
Lecture3.6 Minimizing the cost function
Lecture3.7 Forward propagation
Lecture3.8 Back propagation

Neural Network Tutorial

Lecture4.1 Kernels and the kernel trick
Lecture4.2 Maximum margin classification and support vectors
Lecture4.3 Nonlinear decision boundaries

Lecture5.1 Nonlinear decision boundaries
Lecture5.2 Feedforward and feedback artificial neural networks
Lecture5.3 Multilayer perceptrons
Lecture5.4 Improving the way neural networks learn

Lecture6.1 Goals
Lecture6.2 Model Architecture
Lecture6.3 Principles
Lecture6.4 Code Organization
Lecture6.5 Launching and Training the Model
Lecture6.6 Evaluating a Model

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Skill level: Intermediate
Language: English
Certificate: No
Assessments: Self
Prerequisites: Basic Python programming

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John Doe
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John Doe has been a professional educator
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and coached over 1000 students, and he
holds degrees in Physics and Literature
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spent the last 4 years studying how
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Learning Scientist & Master Trainer
He has been a professional educator for
the past 20 years. He’s taught, tutored,
and coached over 1000 students, and
he holds degrees in Physics and Literature
from Northwestern University. He has
spentthe last 4 years studying how
people learn to code and develop applications.

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