Deep Learning in Microsoft Azure

What is Microsoft’s approach to Deep Learning, and how does it differ from Open Source alternatives? In this session, we will look at Deep Learning, and how it can be implemented in Microsoft and Azure technologies with the Cognitive Toolkit, Tensorflow in Azure and CaffeOnSpark on AzureHDInsight. Join this session in order to understand deep learning better, and how we can use it to provide business and technical benefits in our organizations

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Further reading about Deep Learning

Deep Learning A-Z™: Hands-On Artificial Neural Networks

Deep Learning Using TensorFlow

How to get started with Python for Deep Learning and Data Science

Cheat Sheets for AI, Neural Networks, Machine Learning, Deep Learning & Big Data

Deep Learning With TensorFlow 2.0

#deep-learning #machine-learning #data-science #azure

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Deep Learning in Microsoft Azure
Marget D

Marget D


Top Deep Learning Development Services | Hire Deep Learning Developer

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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

Mikel  Okuneva

Mikel Okuneva


Top 10 Deep Learning Sessions To Look Forward To At DVDC 2020

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:

Also Read: Why Deep Learning DevCon Comes At The Right Time

Adversarial Robustness in Deep Learning

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.

Read an interview with Dipanjan Sarkar.

Imbalance Handling with Combination of Deep Variational Autoencoder and NEATER

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.

Default Rate Prediction Models for Self-Employment in Korea using Ridge, Random Forest & Deep Neural Network

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

Rusty  Shanahan

Rusty Shanahan


Microsoft Azure Machine Learning x Udacity — Lesson 2 Notes

Detailed Notes for Machine Learning Foundation Course by Microsoft Azure & Udacity, 2020 on Lesson 2 —_ Introduction to Machine Learning_

What is Machine Learning?

Machine learning_ is a data science technique used to extract patterns from data, allowing computers to identify related data, and forecast future outcomes, behaviors, and trends._

One important component of machine learning is that we are taking some data and using it to make predictions or identify important relationships. But looking for patterns

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  • Health
  • Finance/Banking
  • Manufacturing
  • Retail
  • Government
  • Education

Brief History of Machine Learning

Artificial Intelligence:

A broad term that refers to computers thinking more like humans.

Machine Learning:

A subcategory of artificial intelligence that involves learning from data without being explicitly programmed.

Deep Learning:

A subcategory of machine learning that uses a layered neural-network architecture originally inspired by the human brain.

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Data Science Process

Raw data, however, is often noisy and unreliable and may contain missing values and outliers. Using such data for modeling can produce misleading results. For the data scientist, the ability to combine large, disparate data sets into a format more appropriate for analysis is an increasingly crucial skill.

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**Collect Data: **Query databases, call Web Services, APIs, scraping web pages.

**Prepare Data: **Clean data and create features needed for the model.

**Train Model: **Select the algorithm, and prepare training, testing, and validation data sets. Set-up training pipelines including feature vectorization, feature scaling, tuning parameters, model performance on validation data using evaluation metrics or graphs.

**Evaluate Model: **Test & compare the performance of models with evaluation metrics/graphs on the validation data set.

**Deploy Model: **Package the model and dependencies. Part of DevOps, integrate training, evaluation, and deployment scripts in respective build & release pipeline.

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Artificial Intelligence Jobs

Common Types of Data

  • Numeric
  • Time-series
  • Categorical
  • Text
  • Images

Tabular Data

Data that is arranged in a data _table _and is the most common type of data in Machine Learning. is arranged in rows and columns. In tabular data, typically each row describes a single item, while each column describes different properties of the item. Each row describes a single product (e.g., a shirt), while each column describes a property the products can have (e.g., the color of the product)

Row: An item or entity.

Column: A property that the items or entities in the table can have.

Cell: A single value.


It is important to know that in machine learning we ultimately always work with numbers or specifically vectors.

vector is simply an array of numbers, such as (1, 2, 3)—or a nested array that contains other arrays of numbers, such as (1, 2, (1, 2, 3)).

For now, the main points you need to be aware of are that:

  • All non-numerical data types (such as images, text, and categories) must eventually be represented as numbers
  • In machine learning, the numerical representation will be in the form of an array of numbers — that is, a vector

Scaling Data

Scaling data means transforming it so that the values fit within some range or scale, such as 0–100 or 0–1. This scaling process will not affect the algorithm output since every value is scaled in the same way. But it can speed up the training process.

Two common approaches to scaling data:

Standardization rescales data so that it has a mean of 0 and a standard deviation of 1. The formula for this is:

(𝑥 − 𝜇)/𝜎

Normalization rescales the data into the range [0, 1].

The formula for this is:

(𝑥 −𝑥𝑚𝑖𝑛)/(𝑥𝑚𝑎𝑥 −𝑥𝑚𝑖𝑛)

Encoding Data

when we have categorical data, we need to encode it in some way so that it is represented numerically.

There are two common approaches for encoding categorical data:

  1. **Ordinal encoding: **convert the categorical data into integer codes ranging from 0 to (number of categories – 1). One of the potential drawbacks of this approach is that it implicitly assumes an order across the categories.
  2. **One-hot encoding: **transform each categorical value into a column. One drawback of one-hot encoding is that it can potentially generate a very large number of columns.

Image Data

An image consists of small tiles called _pixels. _The color of each pixel is represented with a set of values:

  • In grayscale images, each pixel can be represented by a single number, which typically ranges from 0 to 255. This value determines how dark the pixel appears (e.g., 0 is black while 255 is bright white).
  • In colored images, each pixel can be represented by a vector of three numbers (each ranging from 0 to 255) for the three primary color channels: red, green, and blue. These three red, green, and blue (RGB) values are used together to decide the color of that pixel. For example, purple might be represented as 128, 0, 128 (a mix of moderately intense red and blue, with no green).

Color Depth or Depth:

The number of channels required to represent a color in an image.

  • RGB depth = 3 (i.e each pixel has 3 channels)
  • Grayscale depth= 1

Encoding an Image:

We need to know the following three things about an image to in order to encode it:

  • Horizontal position of each pixel
  • Vertical position of each pixel
  • Color of each pixel

We can fully encode an image numerically by using a vector with three dimensions. The size of the vector required for any given image would be:

Size of Vector = height * weight * depth

Image Data is normalized to subtract per channel mean pixel values

Trending AI Articles:

1. Natural Language Generation:

The Commercial State of the Art in 20202. This Entire Article Was Written by Open AI’s GPT23. Learning To Classify Images Without Labels4. Becoming a Data Scientist, Data Analyst, Financial Analyst and Research Analyst

Other Preprocessing Steps:

In addition to encoding an image numerically, we may also need to do some other preprocessing steps. Generally, we would want to ensure the input images have:

  • Uniform aspect ratio
  • Normalized
  • Rotation
  • Cropping
  • Resizing
  • Denoising
  • Centering

Text Data


Text normalization is the process of transforming a piece of text into its canonical (official) form.

  • Lemmatization: example of Normalization; the process of reducing multiple inflections to a single dictionary form
  • Lemma: dictionary form of a word e.g. is -> be
  • Stop-words: high-frequency unwanted words during the analysis
  • **Tokenize: **split each string of text into a list of smaller parts or tokens


The next step after Normalization is to actually encode the text in a numerical form called vectorization. There are many different ways that we can vectorize a word or a sentence, depending on how we want to use it. Common approaches include:

  • Term Frequency-Inverse Document Frequency (TF-IDF) Vectorization: gives less importance to words that contain less information and are common in documents, e.g. _the, _and to give higher importance to words that contain relevant information and appear less frequently. It assigns weights to words that signify their relevance in the documents.
  • Word Embedding: Word2Vec, GloVe

#deep-learning #ai #azure #deep learning #deep learning

Layla  Gerhold

Layla Gerhold


Azure Machine Learning Service

In a series of blog posts, I am planning to write down my experiences of training, deploying and managing models and running pipelines with Azure Machine Learning Service. This is part-1 where I will be walking you through the creation of workspace in Azure ML service

About Azure Machine Learning Service

Azure Machine Learning Service is a cloud based platform from Microsoft to train, deploy, automate, manage and track ML models. It has a facility to build models by using drag-drop components in Designer along with traditional code based model building. Azure ML service makes our job very ease in maintaining developed models and also helps in hassle free deployment of models in lower(QA, Unit) and higher(Prod) environments as APIs. It is integrated with various components in Azure like Azure Kubernetes Services, **Azure Databricks, Azure Monitor, Azure Storage accounts, Azure Pipelines, MLFlow, Kubeflow **to carry out various activities which will be discussed in upcoming posts.

Why Azure Machine Learning Service

In the process of building models, one need to play around with various hyperparameters and use various techniques. Also one need to scale out the resources for training the model if the dataset is huge. Bringing your model development and deployment to cloud makes your job easy. In particular Azure Machine Learning Service has below advantages.

  1. Simplifies model management
  2. Automated machine learning simplifies model building
  3. Scales out training to GPU cluster or CPU cluster or Azure Databricks whenever needed with inbuilt integration
  4. Deployment of models to production with Azure Kubernetes Service or Azure IOT edge is very simple.

#microsoft-azure #cloud-machine-learning #deep-learning #machine-learning #azure-machine-learning

Few Shot Learning — A Case Study (2)

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:

  1. Effectiveness of different architectures such as Residual and Inception Networks
  2. Effects of transfer learning via using pre-trained classifier on ImageNet dataset

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.

Introduction to Few-Shot Classification

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.

  1. N way: It means that there will be total N classes which we will be using for training/testing, like 5 classes in above example.
  2. K shot: Here, K means we have only K example images available for each classes during training/testing.
  3. Support set: It represents a collection of all available K examples images from each classes. Therefore, in support set we have total N*K images.
  4. Query set: This set will have all the images for which we want to predict the respective classes.

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.

About Relation Network

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:

  1. Embedding module: This module will extract the required underlying representations from each input images irrespective of the their classes.
  2. Relation Module: This module will score the relation of embedding of query image with each class embedding.

Training/Testing procedure:

We can define the whole procedure in just 5 steps.

  1. Use the support set and get underlying representations of each images using embedding module.
  2. Take the average of between each class images and get the single underlying representation for each class.
  3. Then get the embedding for each query images and concatenate them with each class’ embedding.
  4. Use the relation module to get the scores. And class with highest score will be the label of respective query image.
  5. [Only during training] Use MSE loss functions to train both (embedding + relation) modules.

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