Deep Learning in Cosmetic Industry

Deep Learning has been in the limelight since a while now, and while we are constantly exploring this field, a lot of its potential is still left to be unlocked and discovered. Deep Learning is a term that is best recognised by those who have some experience working on it or researching it. For everybody else, this article will give you a basic insight about Deep Learning, followed by a deeper understanding of one of its most bleeding-edge areas of application. So let’s dive in.

The most uncomplicated way to introduce Deep Learning is to define it as a field that uses algorithms which are inspired by the functioning of the human brain. Sounds cool, doesn’t it? Though this might be a vague definition, it is true in its essence. Deep Learning is technically a sub-field of the larger, more-encompassing machine learning, and it incorporates smart and efficient algorithms to make your task more personalised and efficient. These algorithms are called artificial neural networks.

Artificial Neural Networks(ANN’s) comprises of input layer, hidden layers and output layer. Each Layer is made of one or more nodes. These nodes are interconnected such that flow starts from input layer and ends at output layer. Input layer consists of feature nodes specified in the problem and output node consists of target variable node. Weights of nodes, transfer functions and activation functions are used to control the flow from input layer to output layer. Hidden layers are responsible for exploring complex patterns in the given data.

Deep Learning

Deep Learning makes use of brain simulations to make algorithms efficient and more wide-ranging to use. Today we have huge amounts of data stored everywhere- individual databases maintained by organisations, government databases, and so on. Deep Learning uses this data to make your interaction with a product or a service more memorable.

What differentiates Deep learning from Machine Learning?

Machine learning algorithms can only solve problems that we know how to solve i.e., mathematical modeling of features and improvising model based on performance measure. For problems where mathematical modeling seems impossible, deep learning comes into picture. As mentioned above, deep learning uses artificial neural networks to identify hidden patterns in the combination of features which is manually handled by conventional algorithms in machine learning.

Deep learning can automate feature engineering for poorly defined problems in which user cannot understand the significance of features and their combinations.

The idea of imitating human brain and identification of unexpected combinations using hidden layers leads to amazing insights which is what makes them stand out from machine learning algorithms.

How can Deep Learning be used in the cosmetic industry?

The key features that allow Deep Learning to gain a foothold in the cosmetic industry are IoT and facial recognition. The cosmetic and skincare industry is quickly becoming oversaturated with products, making it one of the most confusing and least satisfying niches in personal care. Since each product claims to be better than the other in only one or two aspects, most customers end up feeling limited and dissatisfied with their choices in skincare.

Facial recognition when combined with big data and deep learning can bring back this customer satisfaction by giving you a more detailed and personalized analysis of your skin, followed by recommending the best treatments and products for you. Deep Learning has also been moulded to create an application that can analyze your selfies and tell you what you need to make your skin better. Infact, selfies are a vast untapped market that is up to its brim with potential. Once you have significant knowledge on the mobile and cloud industries, you can capture and validate data from any source. Then you can use Deep Learning algorithms along with a few user-specific data (obtained from specialized questions) to understand the skin color, type, whether or not there is any pigmentation on the skin, and more such features. With such an arsenal of knowledge in your hand, you can easily advise the huge target audience that is waiting for the best product for their skin type.

There are many brands that have already been using Augmented Reality (AR )for the cosmetic industry since a long time. The applications made by these brands allow you to try on different makeup products, such as lipsticks, blushes, eye shadows, eye liner, and even hair color, in order to pick the best combination of products for you, all without trying on a single product! Deep Learning algorithms finally came through with data-driven solutions to enhance AR technology. Integrating AR technology with deep learning made user experience much more personalized.

Using Deep Learning in the cosmetics industry has really only just begun, with some of its possible benefits including saving time, reducing related costs, and reaching more customers with your products. At this point, we can definitely say that the future of the cosmetic industry in creating more personalised services and allowing more people access to their products lies with Deep Learning. Is your organisation ready to utilize such an immense opportunity?

How to better manage SCRUM – The Prologue

As technological innovations are rapidly increasing in the IT industry, it is becoming paramount to acquaint yourself with the increasing constraints and complexity in software. The perspectives adopted by people toward software development need to undergo rapid changes to fit in seamlessly with the progressing complexity.

Agile software development is a variety of development methods that are based on dividing a larger task into smaller fragments called ‘iterations’. Each iteration handles the changing requirements of the customers and provides solutions for them in the same iteration.

Why adopt Agile practices?

To be honest, businesses care about achieving better business results, and Agile as a tool is very capable of giving them the above results. The idea of better business results comprises of a few key points, which Agile can resolve/make more efficient. Agile helps you get your product to the market faster thanks to frequent delivery cycles. Agile also takes into consideration the feedback given by real customers. This helps the development teams bring to the market a product that perfectly understands the customer’s needs, leading to higher customer satisfaction. Agile lets your team work more efficiently as individuals and as a team, giving the end customer better quality products.

Why is Scrum popular?

Scrum methodology is far more popular than its counterparts due to one reason- simplicity. Scrum also utilizes a variety of certifications- Scrum Masters, product owners, developers, and more. Scrum is responsible for the streamlining of software development, a fact that most companies have not overlooked. Many Scrum Masters have also voiced that the simplicity of scrum lies in its structure- a prescriptive, structured format that gives you very clear guidelines about what you have to do. Scrum breaks down a particular task into several “iterations”, bite-sized cycles of work that need to be completed by every member on the team before the deadline of the iteration. Thanks to the constant deadlines, a feeling of constant urgency is maintained, making each member perform to their very best so the work is completed before the deadline.

Forming a good SCRUM team:

Here are a few guidelines that can help you get started while forming a scrum team

  • Having the right mix of people with individual and unique skills that can complement each other is a basic prerequisite.
  • Cross-skilling, or knowing the skills of your team members, is also a very important factor in a good scrum team. For ex- the person working on UX should be able to debug codes and work on backend, if required. Similarly, backend developers can lend their expertise to DevOps.
  • Any team member can commence testing the part of the end product that is developed by someone else. When team members test each other’s work, there is lesser probability that the customers will find any flaws in the end product.

Parts of a Scrum:

A scrum meeting addresses several items in a single meeting, but the main functions of a scrum are-

Part 1- Managing Product Backlogs

When applying scrum to your project, you should start with creating a prioritized list of tasks given to the development team. The tasks are derived from the project requirements and the roadmap taken to complete the task. Such a list of tasks is called a product backlog. Creating and managing the product backlog according to different iterations is an essential segment to have a successful scrum.  

Part 2- Capacity Planning

When you are planning the next sprint in your scrum process, you need to effectively gauge the capacity of your team, based on the number of members you have in your team for that particular sprint. Capacity planning can be expressed best as an equation- (number of team members) * (number of productive hours in a day) * (number of days in the sprint). Unforeseen parameters are also taken into account, like any leave submitted by an employee would have to be balanced by the other members in the team.

Part 3- Managing Sprint Backlogs

A sprint backlog is a series of tasks that need to be completed in a sprint. They are listed down by the team at the beginning of a sprint, with modifications being made  to it as the sprint progresses. Time estimation for each task is also performed for a sprint backlog. A scrum team should effectively distribute the tasks in a product backlog because the tasks selected have to be completed in that sprint itself.

At first, it may seem intimidating because you might see many more tasks than what you are used to. However the idea behind scrum is not taking on more work instead, it is to work smarter so that you can accomplish more work in a shorter period of time.

Topic Modeling using NMF and LDA

Topic modeling is a statistical model to discover hidden semantic patterns in unstructured collection of documents. Large collection of documents are represented in terms of topics and topics are represented in terms of words. This Top-Down approach will help in exposing hidden insights from the corpus. In this approach, every document is a distribution of topics and every topic is a distribution of words. The topics extracted using Topic modeling are collection of similar words. The intuition behind Topic modeling is built on top of mathematical framework, which is based on probability and statistics of words in each topic.

Out of all the existing algorithms for topic modeling, Latent Dirichlet association (LDA) and Non-negative matrix factorization (NMF) are extensively used by Data modelers and widely accepted in scientific community for topic extraction. LDA is a probabilistic model and NMF is a matrix factorization and multivariate analysis technique.

The basic idea in topic modeling is to vectorize the given corpus by term frequency or term frequency-inverse document frequency and split that document term matrix into document – topic and topic – word subsets and thereby optimizing subsets either by using probabilistic or factorization techniques.

The challenge and ambiguity involved in Topic modeling is validation. The very approach of extracting topics from large collection of documents itself is unsupervised i.e., documents are not labelled prior modeling. Therefore, validating topics obtained from unsupervised approach is a tedious task. One has come out with their own validation technique depending upon their application. Due to the advent of dimensionality reduction techniques and advanced computational packages, one can visualize the similarity between topics extracted from corpus.

There are numerous applications of Topic modeling. The idea of searching for keywords in corpus can be tremendously enhanced by embedding topic modeling with search engines as topic models can pinpoint relevant words and documents by using a threshold probability distribution. Topic modeling is widely used in advanced research labs in the domain of healthcare, journalism, politics and Law enforcement. Modeling topics helps users in doing targeted research which undoubtedly leads to efficient results.