Welcome to the Deep Learning Introductory Course.
I’ve worked with AS for four years and am now in charge of machine learning business development management.
As a member of the AI Team, I’ve helped clients create machine learning solutions from conception to production.
This blog will teach you about deep learning (DL) and the resources that provides for creating DL_based applications.
We’ll also go through a case study of how one of our clients is using Deep Learning to innovate.
Machine learning is a subset of Deep Learning, which is a subset of Artificial Intelligence.
learning research began in the 1950s, with scientists devoting substantial resources to wait for the following 70 years.
Yann LeCun’s study on Convolutional Neural Networks, as well as Sepp Hochreiter and Juergen Schmidhuber’s work on short_term memory or LSTM, lay the groundwork for the present error in the 1980s and 1990s.
The rediscovery of the backpropagation training algorithm in 1986 was a watershed moment in Deep Learning research.
The backpropagation method uses the chain rule of derivatives to help the model learn from its mistakes.
However, research into Deep Learning fell out of favour in the decades that followed.
This was partially due to data and computer restrictions.
With the advent of the Internet, cellphones, smart TVs, and the affordable availability of digital cameras, more and more data became available.
The amount of computing power available was increasing.
GPUs became a general_purpose computing tool as CPUs got faster.
Both of these phenomena aided the advancement of neural networks.
Yann LeCun released a study on image recognition using convolutional neural networks in 1998.
However, it wasn’t until 2007 that studies began to pick up again.
The introduction of GPUs and the resulting reduction in training time ushered in Neural Networks and Deep Learning as a mainstay.
The problem that neural networks handled became more and more fascinating as data and computer capacity increased.
In 2008, when GPS became more popular, Neural Networks reappeared.
Artificial Neural Networks are used in deep learning to process and evaluate reference data and come to a conclusion.
Traditional Compute Processing Architectures are not the same as Artificial Neural Networks (ANN).
They’re made to work more like the human brain in this regard.
The more adaptable and capable of dealing with unforeseen abnormalities, as well as novelty, the better.
Later, we’ll go over Artificial Neural Networks in greater detail.
A subset of machine learning algorithms is known as deep learning.
For feature extraction and manipulation, deep learning employs layers of non_linear Processing Units.
The output from the preceding layer is used as an input for each subsequent layer.
Pattern analysis, which is unsupervised, and classification, which can be supervised or unsupervised, are examples of supervised or unsupervised algorithms.
These methods rely on the unsupervised learning of several layers of data characteristics or representations.
To build a hierarchical representation, higher_level characteristics are generated from lower_level features.
Deep learning algorithms are a subset of the larger Machine Learning discipline of learning data representations, and they learn many layers of representations that correspond to various degrees of abstraction.
Deep Learning focuses on end_to_end learning based on raw features, whereas standard Machine Learning focuses on feature engineering.
Each layer is in charge of evaluating the data’s more complicated characteristics.
A neural network is a group of simple trainable mathematical units that learn complicated functions collectively.
A Neural Network can do a good job of translating input data and features to output judgments if given enough training data.
It is made up of several layers.
An input layer, certain hidden layers, and an output layer are all present.
An Artificial Neural Network’s basic unit is the Artificial Neuron, which is also known as a node.
Artificial neurons, like the real neurons for which they are named, contain many input channels.
Inside the processing step, a neuron adds together various inputs and creates a single output that may be sent to numerous artificial neurons.
To acquire their weighted value, the input values are multiplied by the weight in this simple example.
The node then modifies the total to create more accurate predictions, based on the success or failure of the product forecasts, by adding an offset vector termed the bias to the sum.
The neuron activates if the final value generated by the previous stages reaches or above the set activation threshold after the inputs have been weighted then summed, with the bias applied if necessary.
The activation stage is what it’s called.
It’s the last stage before delivering an output.
Any neural network that does not establish a cycle between neurons is referred to be a feedforward neural network.
This implies that data is sent from one input to the next without looping backward.
Recurrent Neural Network is another Neural Network that looks backwards.
When processing sequential input like text, voice, or handwriting, a recurrent neural network is most useful.
When you consider in the words or letters that come before it, your ability to anticipate the next word or later is significantly increased.
After Long Short Term Memory or LSTM methods changed voice recognition systems in 2007, recurrent neural networks became considerably more prominent.
Many of today’s most successful applications in the voice recognition, text-to-speech, and handwriting recognition domains are based on LSTM.
Deep Learning applications may be found in a variety of sectors.
Let’s take a look at each one separately.
Text analysis applications may be found in a variety of industries, including banking, social media, CRM, and insurance, to mention a few.
It’s a technique for detecting insider training.
Make sure you’re in conformity with the law.
Affinity towards a certain brand.
Analyzing people’s feelings.
By examining blobs of text, you can do things like Intent Analysis and more.
Deep Learning may also be used to tackle time_series and predictive analytic challenges.
Data centres are used for Log Analysis and risk fraud detection in the supply chain sector, as well as predictive analysis utilising sensor data in the IoT field.
It’s also utilised to construct recommendation algorithms in social media and e_commerce.
It also employs sound analysis.
Deep Learning is utilised in the security sector for voice identification, voice analysis, and sentiment analysis in the CRM area.
Deep learning is also utilised in the automobile and aviation sectors, where it is used to identify engine and instrument floor.
Deep learning is being used in the banking industry to detect credit card fraud, among other things.
Finally, it’s utilised to analyse images.
Deep Learning is utilised in the security area for things like facial recognition.
It’s used for tagging and identifying persons in photos on social media.
Of course, the issue is one of size.
AlexNet won the ImageNet competition for convolutional neural networks in 2012.
There were eight layers, 650,000 linked neurons, and over 60 million parameters in the system.
The complexity of Neural Networks has greatly risen in recent years.
Resnet 152, a Deep Residual Neural Network with 152 layers and millions more linked neurons in parameters, is a recent network.
Three sophisticated Deep Learning_enabled managed API services are available on the Platform.
Amazon Lex, Amazon Polly, and Amazon Rekognition are three of Amazon’s artificial intelligence services.
Amazon Lex is a service for integrating speech and text_based conversational interfaces into any application.
It has powerful Deep Learning capabilities for automatic voice recognition, speech_to_text conversion, and natural language comprehension to determine the input’s purpose.
As a result, you’ll be able to create apps with extremely engaging user interfaces and lifelike conversational interactions.
Polly on Amazon translates tags into natural-sounding speech.
Allowing you to create speech-enabled applications and new types of speech-enabled goods.
Amazon Rekognition makes it simple to include image analysis into your apps, allowing them to detect objects, sceneries, and faces, as well as pictures.
You may also look up and compare people’s faces, identify celebrities, and block undesirable information.
Deep Learning can be difficult to implement on a technical level.
You’ll need to know how to skate, train, and infer across big distributed networks, as well as comprehend the math behind the models.
Several Deep Learning frameworks have evolved as a result, allowing you to design models and then train them at scale.
The Amazon deep_learning AMIs may be used to create bespoke models.
Amazon Linux and Ubuntu are supported.
The Apache MXnet TensorFlow, Microsoft Cognitive Toolkit Caffe, Caffe2, theano, torch, Pytorch, and Keras are all pre_installed on the AWS Deep Learning AMIs.
The Deep Learning AMIs make it possible to easily deploy and scale any of these frameworks.
The Deep Learning AMIs can assist you in getting up and running rapidly.
Many deep learning frameworks are included, as well as tutorials demonstrating appropriate installation, configuration, and model accuracy.
The Deep Learning AMIs manage dependencies, keep track of library versions, and check for code compatibility.
With monthly AMI updates, you’ll always have the most recent versions of the engines in your data science libraries.
Whether you’re looking for Amazon EC2 GPU or CPU Instances, we’ve got you covered.
The deep learning AMIs are available at no additional cost.
You just have to pay for the AWS resources you use to store and execute your application.
You can get started with AWS Deep Learning AMIs in one of two ways: you can deploy a deep-learning Compute Instance in one click or you can instal a deep_learning Compute Instance in two clicks.
The AWS Deep Learning AMIs are available in the AWS marketplace and may be deployed rapidly.
GPUs are used for large_scale training, whereas CPUs are used to perform predictions or inferences.
With a pay_as_you_go pricing mechanism, they both provide a reliable, secure, and high_performance execution environment in which to execute your applications.
Launching an AWS Cloud Formation Deep Learning template is another option to get started with the Deep Learning AMIs.
To train across many instances, you may use the Deep Learning CloudFormation template, which provides a fast method to start all of your resources using deep learning AMIs.
Let’s have a look at a use case now.
C_Span is a non_profit organisation dedicated to broadcasting and documenting government sessions in the United States.
To assist human indexers, C_span has created an automatic facial recognition system, although it will be sluggish.
Users’ capacity to search archive content was limited since they could only index half of the incoming content by speaker.
So, how did they use Amazon Rekognition as a Deep Learning Service to solve problems and reap benefits?
They used Amazon recognition to match submitted screenshots of a database of 97,000 recognised faces automatically.
C_span will be able to more than double the video index from $3,500 to $7,500 a year as a result of this.
It reduced the time it took to index an hour of video footage from 60 to 20 minutes.
In less than three weeks, they had everything up and running, and in less than two hours, they had indexed 97,000 picture collections.
I hope you gained some insight, and we’ll continue to look into additional options.
Thank you for listening. My name is Kunal Katke, and I’m with AI.