Starting a startup is a one-shot celebration of registering a company, but running a startup is all about disentangling misunderstandings of startup stakeholders. It’s all about conversations that are rewarding and serendipity Prof. Suresh of NSRCEL at Indian Institute of Management, Bangalore (IIMB). As a co-founder, issues can be a fundamental problem of most startups, many ecosystem observers talk about the need for a “we story”- a collaboration between partners about values and goals. If co-founders are going to collaborate, they have to figure out how to have a productive conversation. A conversation, as opposed to parallel monologues, involves two or more people making an effort to understand each other. In the grip of strong emotion, a productive conversation can be surprisingly hard. In our conflict-averse startup culture, it is very important for co-founders to agree to disagree with each other. Everyone will have their own way of striking a balance between empathy and problem-solving. Many of the books and mentors suggest that before one plays with numbers and presents them to investors, it is important to plan for conflict resolution through conversation. If possible, have an arbitrator who all co-founders will look up to. His/her decision will be final. This will help you to agree to disagree with your fellow co-founders. At times, it appears that co-founder conflict resolution is a low priority, but it is worth investing in, considering the long-term emotional strain. During my work with the Co-Founders at Minjar, I asked my reporting manager and co-founder Anand about conflict resolution and arbitration. A bit about Anand, he never discusses problems over an empty stomach. He listens very patiently. He may be a slow decision maker, but he’s very good at avoiding conflicts. He doesn’t shy away from anger, but at the same time, he doesn’t indulge it. He tackles hard issues without shutting down. Though it is very rare, he doesn’t mind saying “Sorry” if he is wrong. These attributes have been a stepping stone in shaping Anand as a serial entrepreneur, and has allowed him to become the co-founder of Kuliza & Minjar. Anand says the following about arbitration and conflict resolution: "The startup is all about conversations that are rewarding and serendipity". This ideology is a very powerful one & isn't well understood. Often co-founders associate ups & downs, and differences in opinions as their personal/ collective failings. Ability to engage in a conversation under all circumstances, is a pretty powerful skill. Strenuous situations tend to reveal a person's true personality. Often it is much more efficient to let the other co-founder make the decisions than involving an arbitrator. There is no guarantee that arbitrators won’t accentuate the set of problems that co-founders are facing. If co-founders are not enjoying the conversation between themselves, why should it become palatable by involving a mediator? A perfect decision is an illusion, regardless of where it comes from. When it comes to day to day decision making, people simply need to stop focusing on perfection, and rather appreciate the opportunities being presented to them. Hence making amicable conversations and serendipity becomes far more important than anything else. Winning an argument might boost your confidence, but in no way ensures the longevity of your startup, nor does it guarantee the prevention of common errors in judgement. People learn when they see things happen (or see reality when they make mistakes). We are prone to making decisions by imagining the future (visions of which could be driven by fear, greed, hope or expectations). Often the source of conflicts lies in the latter or misaligned value systems. Therefore, real time experiences help improve decision making skills, while, envisioning the future could lead to errors and conflicts. Finally, remember that an argument is about the argument and never the person. Dwelling on its memory for too long makes silly things appear larger than life, and becomes a continuous source of stress. Just as it's tough to stand inside an oven, its very difficult to live in a stressful environment. A ‘we story’ is a fairytale. And there are no fairy tales without conversations and serendipity. Simply put, emotions are often inconvenient in the startup world. As Prof. Saras of NSRCEL @ IIMB says, “What matters most in startups is what’s possible on the other side of co-creation”. It’s not that co-founders come together in electric recognition and pure passion, then fall away from each other through conflict. Rather, co-founders come together with a passion and hence succeed through continuing conversation. Source - https://www.linkedin.com/pulse/agreeably-disagreeing-how-get-along-your-co-founder-kesava-reddy/
Agreeably Disagreeing: How Not To Get Along With Your Co-Founder
A Complete Guide To Customer Acquisition For Startups
Any business is enlivened by its customers. Therefore, a strategy to constantly bring in new clients is an ongoing requirement. In this regard, having a proper customer acquisition strategy can be of great importance.
So, if you are just starting your business, or planning to expand it, read on to learn more about this concept.
The problem with customer acquisition
As an organization, when working in a diverse and competitive market like India, you need to have a well-defined customer acquisition strategy to attain success. However, this is where most startups struggle. Now, you may have a great product or service, but if you are not in the right place targeting the right demographic, you are not likely to get the results you want.
To resolve this, typically, companies invest, but if that is not channelized properly, it will be futile.
So, the best way out of this dilemma is to have a clear customer acquisition strategy in place.
How can you create the ideal customer acquisition strategy for your business?
- Define what your goals are
You need to define your goals so that you can meet the revenue expectations you have for the current fiscal year. You need to find a value for the metrics –
- MRR – Monthly recurring revenue, which tells you all the income that can be generated from all your income channels.
- CLV – Customer lifetime value tells you how much a customer is willing to spend on your business during your mutual relationship duration.
- CAC – Customer acquisition costs, which tells how much your organization needs to spend to acquire customers constantly.
- Churn rate – It tells you the rate at which customers stop doing business.
All these metrics tell you how well you will be able to grow your business and revenue.
- Identify your ideal customers
You need to understand who your current customers are and who your target customers are. Once you are aware of your customer base, you can focus your energies in that direction and get the maximum sale of your products or services. You can also understand what your customers require through various analytics and markers and address them to leverage your products/services towards them.
- Choose your channels for customer acquisition
How will you acquire customers who will eventually tell at what scale and at what rate you need to expand your business? You could market and sell your products on social media channels like Instagram, Facebook and YouTube, or invest in paid marketing like Google Ads. You need to develop a unique strategy for each of these channels.
- Communicate with your customers
If you know exactly what your customers have in mind, then you will be able to develop your customer strategy with a clear perspective in mind. You can do it through surveys or customer opinion forms, email contact forms, blog posts and social media posts. After that, you just need to measure the analytics, clearly understand the insights, and improve your strategy accordingly.
Combining these strategies with your long-term business plan will bring results. However, there will be challenges on the way, where you need to adapt as per the requirements to make the most of it. At the same time, introducing new technologies like AI and ML can also solve such issues easily. To learn more about the use of AI and ML and how they are transforming businesses, keep referring to the blog section of E2E Networks.
Image-based 3D Object Reconstruction State-of-the-Art and trends in the Deep Learning Era
3D reconstruction is one of the most complex issues of deep learning systems. There have been multiple types of research in this field, and almost everything has been tried on it — computer vision, computer graphics and machine learning, but to no avail. However, that has resulted in CNN or convolutional neural networks foraying into this field, which has yielded some success.
The Main Objective of the 3D Object Reconstruction
Developing this deep learning technology aims to infer the shape of 3D objects from 2D images. So, to conduct the experiment, you need the following:
- Highly calibrated cameras that take a photograph of the image from various angles.
- Large training datasets can predict the geometry of the object whose 3D image reconstruction needs to be done. These datasets can be collected from a database of images, or they can be collected and sampled from a video.
By using the apparatus and datasets, you will be able to proceed with the 3D reconstruction from 2D datasets.
State-of-the-art Technology Used by the Datasets for the Reconstruction of 3D Objects
The technology used for this purpose needs to stick to the following parameters:
Training with the help of one or multiple RGB images, where the segmentation of the 3D ground truth needs to be done. It could be one image, multiple images or even a video stream.
The testing will also be done on the same parameters, which will also help to create a uniform, cluttered background, or both.
The volumetric output will be done in both high and low resolution, and the surface output will be generated through parameterisation, template deformation and point cloud. Moreover, the direct and intermediate outputs will be calculated this way.
- Network architecture used
The architecture used in training is 3D-VAE-GAN, which has an encoder and a decoder, with TL-Net and conditional GAN. At the same time, the testing architecture is 3D-VAE, which has an encoder and a decoder.
- Training used
The degree of supervision used in 2D vs 3D supervision, weak supervision along with loss functions have to be included in this system. The training procedure is adversarial training with joint 2D and 3D embeddings. Also, the network architecture is extremely important for the speed and processing quality of the output images.
- Practical applications and use cases
Volumetric representations and surface representations can do the reconstruction. Powerful computer systems need to be used for reconstruction.
Given below are some of the places where 3D Object Reconstruction Deep Learning Systems are used:
- 3D reconstruction technology can be used in the Police Department for drawing the faces of criminals whose images have been procured from a crime site where their faces are not completely revealed.
- It can be used for re-modelling ruins at ancient architectural sites. The rubble or the debris stubs of structures can be used to recreate the entire building structure and get an idea of how it looked in the past.
- They can be used in plastic surgery where the organs, face, limbs or any other portion of the body has been damaged and needs to be rebuilt.
- It can be used in airport security, where concealed shapes can be used for guessing whether a person is armed or is carrying explosives or not.
- It can also help in completing DNA sequences.
So, if you are planning to implement this technology, then you can rent the required infrastructure from E2E Networks and avoid investing in it. And if you plan to learn more about such topics, then keep a tab on the blog section of the website.
A Comprehensive Guide To Deep Q-Learning For Data Science Enthusiasts
For all data science enthusiasts who would love to dig deep, we have composed a write-up about Q-Learning specifically for you all. Deep Q-Learning and Reinforcement learning (RL) are extremely popular these days. These two data science methodologies use Python libraries like TensorFlow 2 and openAI’s Gym environment.
So, read on to know more.
What is Deep Q-Learning?
Deep Q-Learning utilizes the principles of Q-learning, but instead of using the Q-table, it uses the neural network. The algorithm of deep Q-Learning uses the states as input and the optimal Q-value of every action possible as the output. The agent gathers and stores all the previous experiences in the memory of the trained tuple in the following order:
State> Next state> Action> Reward
The neural network training stability increases using a random batch of previous data by using the experience replay. Experience replay also means the previous experiences stocking, and the target network uses it for training and calculation of the Q-network and the predicted Q-Value. This neural network uses openAI Gym, which is provided by taxi-v3 environments.
Now, any understanding of Deep Q-Learning is incomplete without talking about Reinforcement Learning.
What is Reinforcement Learning?
Reinforcement is a subsection of ML. This part of ML is related to the action in which an environmental agent participates in a reward-based system and uses Reinforcement Learning to maximize the rewards. Reinforcement Learning is a different technique from unsupervised learning or supervised learning because it does not require a supervised input/output pair. The number of corrections is also less, so it is a highly efficient technique.
Now, the understanding of reinforcement learning is incomplete without knowing about Markov Decision Process (MDP). MDP is involved with each state that has been presented in the results of the environment, derived from the state previously there. The information which composes both states is gathered and transferred to the decision process. The task of the chosen agent is to maximize the awards. The MDP optimizes the actions and helps construct the optimal policy.
For developing the MDP, you need to follow the Q-Learning Algorithm, which is an extremely important part of data science and machine learning.
What is Q-Learning Algorithm?
The process of Q-Learning is important for understanding the data from scratch. It involves defining the parameters, choosing the actions from the current state and also choosing the actions from the previous state and then developing a Q-table for maximizing the results or output rewards.
The 4 steps that are involved in Q-Learning:
- Initializing parameters – The RL (reinforcement learning) model learns the set of actions that the agent requires in the state, environment and time.
- Identifying current state – The model stores the prior records for optimal action definition for maximizing the results. For acting in the present state, the state needs to be identified and perform an action combination for it.
- Choosing the optimal action set and gaining the relevant experience – A Q-table is generated from the data with a set of specific states and actions, and the weight of this data is calculated for updating the Q-Table to the following step.
- Updating Q-table rewards and next state determination – After the relevant experience is gained and agents start getting environmental records. The reward amplitude helps to present the subsequent step.
In case the Q-table size is huge, then the generation of the model is a time-consuming process. This situation requires Deep Q-learning.
Hopefully, this write-up has provided an outline of Deep Q-Learning and its related concepts. If you wish to learn more about such topics, then keep a tab on the blog section of the E2E Networks website.