Top 22 Data Scientists Jobs in July

July 4, 2022

In order to adapt to digitalization and globalization in the current global tech market, deep learning positions are in high demand at numerous large tech organizations. 

Yes, huge tech companies are currently facing intense rivalry because of startups growing at a fast pace. As a result, they are advertising deep learning positions with attractive compensation packages for experienced deep learning specialists. 

Here in the blog, you’ll find 22 Deep Learning Scientists Jobs to apply for in July. This list of open positions at major organizations for July 2022 also includes machine learning positions. If a person possesses the necessary expertise and understanding in this field, they are qualified to apply for these deep learning positions. 

1. Data Scientist - Machine Learning, Deep Learning, NLP

Company: Enterprise Bot

Location: Bengaluru, Karnataka, India

Job responsibilities: The data scientist is in charge of creating deep learning models using both structured and unstructured data. Create and implement deep learning methods in production. Software modules for data input, data transformation, and analytics are designed and developed. Create applications that use NLP and deep learning.

Job Qualifications: Experience in the fields of Deep Learning, NLP, and Data Science must range from 2 to 5 years. Hands-on experience with deep learning and applications of machine learning, as well as knowledge of Transformers (hugging face) and NLP algorithms and techniques.

Apply Here 

2. Software Engineer- Data Analytics (Image and Deep Learning)

Company: Pentair

Location: Noida, UttarPradesh, India

Job Responsibilities: Should have practical knowledge of image processing and OpenCV. Employing deep learning frameworks (Tensorflow, Pytorch, etc.) to train and construct deep learning models. familiarity with contemporary deep learning architectures like Resnet, Transformers, VGG, etc. Knowledge of picture segmentation, object identification, and classification. 

Job Qualifications: M.E./M. Tech (Data Analytics) and Strong exposure in Data Science & Analytics.

Apply Here

3. AI Resident – Deep Learning

Company: Shell

Location: Hyderabad, Telangana, India

Job Responsibilities: Understanding business goals, creating models that help achieve them, creating metrics to measure progress, examining the ML algorithms that could be applied to a given situation, and ranking those ML algorithms according to their likelihood of success.

Job Qualifications: Proficiency with Python, fundamental deep learning libraries like scikit-learn and pandas, and experience with frameworks for deep learning like TensorFlow or Keras. A plus is having knowledge of scientific computing and analysis tools like NumPy, SciPy, Pandas, and Scikit-learn.

Apply Here

4. Machine Learning & Deep Learning

Company: WNS

Location: Bengaluru, Karnataka, India

Job Responsibilities: Should have practical knowledge of image processing and OpenCV. Employing deep learning frameworks (Tensorflow, Pytorch, etc.) to train and construct models Deep learning models and familiarity with contemporary deep learning architectures like Resnet, Transformers, VGG, etc. Knowledge of picture segmentation, object identification, and classification. locating the issue in the business and offering a solution through data analysis

Job Qualifications: Strong background in analytics and machine learning and a solid grounding in any language - Python strong numerical and analytical abilities Requirements: M.E./M. Tech (Data Analytics)

Apply Here

5.  Deep learning engineer/ Data scientist

Company: Paxcom

Location: Delhi, India

Job Responsibilities: Create and train cutting-edge algorithms to carry out visual recognition tasks including segmentation, detection, and classification at scale on datasets containing millions of images. aided in the creation of deep learning models at production level for computer vision applications. Deep learning models were used to develop solutions that could be used with a CPU or GPU. knowledge of PyTorch, TensorFlow, or other deep learning frameworks Strong knowledge of Linux environments, Python scripting, CUDA, numpy, scipy, matplotlib, scikit-learn, and bash scripting.

Job Qualifications: STEM" major (Science, Technology, Engineering, Mathematics) master's degree or equivalent + one year of experience developing analytics for commercial use OR STEM" major (Science, Technology, Engineering, Mathematics) doctorate or equivalent

Apply here

6. Machine Learning Engineer

Company: Gemini Solutions

Location: Gurugram, Haryana, India

Job Responsibilities: You will be expected to contribute to the NLP domain, which heavily relies on deep learning (e.g., doc2vec, transfer learning using State of the Art models), as well as the fixed income market domain, where you will combine deep learning with more conventional machine learning techniques to forecast returns.

Job Qualifications: Preferred master's degrees in CS, EE, mathematics, and computing. 2+ years of experience in the field of machine learning, ideally in the field of finance, as well as expertise in the areas of entity resolution and natural language processing.

Apply Here

7. ML Engineer_Senior Associate_C2MA

Company: DBS Bank

Location: Bengaluru, Karnataka, India

Job Responsibilities: As an ML Engineer, you will be in charge of planning, creating, testing, and implementing large-scale solutions and distributed machine learning and deep learning systems for our global clientele. You will work closely with a group of ML/DL scientists to create the team's road map and affect our overall strategy in this.

Job Qualifications: Any Graduate

Apply Here

8. DL / Computer vision Developer 

Company: Cyient  

Location: Noida, Uttar Pradesh, India

Job Responsibilities: Create and improve architecture, design, implementation, and deployment of cutting-edge systems for effective deep learning. Working collaboratively with AI researchers and architects, you will execute organizational goals through a number of releases that are well-executed. Additionally, the job entails finding solutions for low-level systems issues including GPU state and memory management, high-performance distributed storage, and networking optimizations for high-speed.

Job Qualifications: Knowledge of low-level operating systems, performance tweaking, and system design. Working knowledge of big data and the cloud also knowledge of Python, deep learning, and machine learning is a must. 

Apply Here

9. Deep Learning Engineer

Company: DroneBase

Location: Bengaluru, Karnataka, India

Job Responsibilities: Develop and create deep learning-based solutions for a variety of industries, including real estate, construction, and renewable energy. Build, Train, Test, and Implement Effective Deep Learning Models for Multiple Domains Object Detection and Segmentation Tasks (End to End Development including Data Preparation, Augmentation, Training, and Deployment). Understand, optimize, and improve the workflows, approaches, models, and code base currently being used to address important challenges.

Job Qualifications: Experience working in the field of deep learning for at least one year. strong theoretical foundation in state-of-the-art deep learning model architectures, transfer learning, convolutional neural networks, hyperparameters, and neural networks. Experience developing segmentation and object detection models from scratch and utilizing transfer learning.

Apply Here

10. Deep Learning Engineer

Company: RadiusAI

Location: Bengaluru, Karnataka, India

Job Responsibilities: Working with ongoing developments in the company and having a fundamental understanding of probability and programming. You will experiment with neural network designs, machine learning frameworks like Tensorflow and Pytorch, and data streaming pipelines in your day-to-day work.

Job Qualifications: Experience using Bayesian models and statistics to solve problems in the real world, preferably after earning a master's or Ph.D. in a STEM field experience with distributed computers, compilers, and programming languages knowledge of open source development

Apply Here

11. Machine Learning Engineer

Company: Cactus Communications

Location: Remote, India

Job Responsibilities: Immerse yourself in offering innovative answers to significant issues facing the actual world. To improve the functionality of various natural language generation and classification models used by Cactus digital editing tools, you will mostly deal with existing open-source deep learning libraries like TensorFlow, PyTorch, HuggingFace transformers, fairseq, etc. Deep neural network training on an extensive, distributed scale. This could entail combining the distribution algorithms in TensorFlow with off-the-shelf frameworks like DeepSpeed and Fairscale.

Job Qualifications: Experience in applied AI research and development spanning two years. You have strong problem-solving skills and are self-motivated. strong foundations in computer science (data structure, algorithms, architecture and OO design). A benefit is having relevant work experience, such as an internship or full-time industry research experience.

Apply Here

12. Deep Learning Software Engineer

Company: CONXAI

Location: Bengaluru, India

Job Responsibilities: Setting the frameworks, protocols, algorithms/topologies, and optimizations for AI in a strategic and technical manner. making high-level design decisions that are centered on the software structure, protocols, and algorithms' management, scalability, usability, resilience, availability, security, and/or safety. Identifying the needs for coding, development tools, validation, and standards compliance.

Job Qualifications: DL development experience in TensorFlow or Pytorch for multi-node training and inference for 5+ years. At least TWO of the following: image/video, natural language, recommendation systems, and data analysis experience and knowledge

Apply Here

13. Computer Vision & Deep Learning Research Scientist/Engineer

Company: OnePlus  

Location: Hyderabad, Telangana, India

Job Responsibilities: Create deep learning models for automotive applications in computer vision and natural language processing. Create and maintain extensive, problem-specific datasets to analyze and boost GPU implementation performance

Job Qualifications: 5+ years of solid expertise in the development of deep learning models. Should have knowledge of deep learning frameworks (e.g. TensorFlow, PyTorch, MXNet) Outstanding programming, debugging, performance analysis, and test design abilities

Apply Here

14. Machine Learning Engineer 1

Company: OLA

Location: Bengaluru, Karnataka, India

Job Responsibilities: The entire gamut of contemporary applied machine learning work will be done with you, including conception, experimentation, implementation, and maintenance. Collaborating closely with the founding team's engineers and developing tools and automation for ML/DL prediction models. You must understand how to create Docker containers and endpoints to host deep learning and machine, learning models. 

Job Qualifications: 2+ years of industry experience in applied machine learning and software engineering. Proficient in at least one object-oriented language, such as C++, Python, or Scala

Apply Here

15. Machine Learning Engineer II

Company: Adobe

Location: Noida, Uttar Pradesh, India

Job Responsibilities: Drive organizational modifications and develop governance frameworks to put AI/ML-related solutions into practice. Create scalable and user-friendly machine learning workflows and implement them to enable training, assessment, and inference on client infrastructure as well as in the cloud. To enable continuous digital business transformation, develop and mature the AI/ML Platform architecture.

Job Qualifications: Knowledge of the basics of machine learning and artificial intelligence, as well as their application, familiarity with the ML lifecycle, AI ethics, and ML frameworks like TensorFlow, Caffe, Torch, and others.

Apply Here

16. Machine Learning Engineer 

Company: Ubiquity

Location: Gurgaon, India

Job Responsibilities: Provide Machine Learning / Deep Learning algorithms and solutions for data processing and analysis projects. Offer your experience in machine learning and algorithms, and work together with project teams.. Keep the machine learning solution integrated inside the software. 

Job Qualifications: You've worked as a machine learning scientist or engineer for at least three years, deploying models in mass production. Previous experience delivering models as a component of a (potentially stateful) microservice. You are passionate about using AI/ML/DL systems that have been put into production to directly optimize key goods or operations.

Apply Here

17. Senior Deep Learning Software Engineer

Company: NVIDIA

Location: Bengaluru, Karnataka, India

Job Responsibilities: Create deep learning models for automotive applications in computer vision and natural language processing. Create and maintain extensive, problem-specific datasets to analyze and boost GPU implementation performance

Job Qualifications: B.Tech. or M.Tech. in computer engineering or a similar engineering field, or comparable experience. 5+ years of solid expertise in the development of deep learning models and should have knowledge of deep learning frameworks (e.g. TensorFlow, PyTorch, MXNet)

Apply Here

18.  Software Engineer - Embedded Deep Learning Quantization

Company: MathWorks

Location: Bengaluru, Karnataka, India

Job Responsibilities: The optimal toolchains for using Model-Based Design methods to address issues in the field of Artificial Intelligence are MATLAB and Simulink. It can be difficult to transform such creative ideas into something that can be effectively implemented on an embedded device. It is your responsibility to create new features that automate this change by enabling simulation and deployment. Delivering exceptional user-friendliness and optimum user productivity is your challenge.

Job Qualifications: Experience working independently on cross-disciplinary teams to conceive, develop, and test strong knowledge of software architecture and Object-Oriented programming understanding of one or more branches of statistics, deep learning, machine learning, or optimization

Apply Here

19. Deep Learning Engineer

Company: Toothsi

Location: Mumbai, Maharashtra, India

Job Responsibilities: From data collection, cleaning, and preprocessing to model training and deployment to production, you will oversee every process. The ideal applicant will be enthusiastic about artificial intelligence and knowledgeable about the most recent advancements in the field. We are moving forward in this direction to leverage Artificial Intelligence and Deep Learning to make our aim simple and provide a richer user experience.

Job Qualifications: Having knowledge of a deep learning framework like TensorFlow or Keras. Knowledge of Python and the fundamental machine learning and deep learning libraries such as scikit-learn, NumPy, and pandas.

Apply Here

20. Senior Deep Learning Compiler Engineer

Company: Mulya Technologies

Location: Hyderabad, Telangana, India

Job Responsibilities: You will be in charge of creating the tools required to create cutting-edge deep learning models for unique Ceremorphic chips. To speed the development of the generation of deep learning software, you will cooperate with members of the hardware architecture and deep learning software framework teams.

Job Qualifications: Excellent debugging, performance analysis, and test design skills, as well as programming in C/C++. Working knowledge of advanced machine learning frameworks (Tensorflow, PyTorch, MXNet) and understanding of the hardware accelerator market for machine learning (basic architectures, common techniques shared across the space, etc).

Apply Here

21. Computer Vision/Deep Learning Engineer

Company: Staqu Technologies 

Location: Gurugram, Haryana, India

Job Responsibilities: Full-stack computer vision solutions on the PyTorch/Theano/TensorFlow. The framework may be designed, implemented, and deployed. Investigate and resolve challenging problems in deep learning, categorization, and image recognition. To solve challenging challenges, conduct research, and develop scalable computer vision and machine learning solutions.

Job Qualifications: A or BE degree is required, as well as 1-2 years of expertise in computer vision and deep learning. Knowledge of OpenCV, sklearn, Theano, TensorFlow, and PyTorch.

Apply Here

22. Machine Learning Engineer - NLP

Company: Charmboard

Location: Bengaluru North, Karnataka, India

Job Responsibilities: Create innovative structures for detecting, categorizing, and tracking objects. Create effective Deep Learning architectures that can be used with NVIDIA hardware in real-time. Improve the stack for use with embedded devices. Enhance the process of Data gathering, preparation, and analysis.

Job Qualifications: Knowledge of languages and frameworks like  Python, C++, CUDA, TensorRT, Pytorch, Tensorflow, and ONNX. Adequate familiarity with Linux and version control (Git, GitHub, GitLab). Skilled in using OpenCV and Deep Learning to address issues in the image domain. Knowing how to use Nvidia platforms like Drive AGX Pegasus, Jetson AGX Xavier, etc. to deploy Deep Learning models for real-time applications.

Apply Here

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This is a decorative image for: A Complete Guide To Customer Acquisition For Startups
October 18, 2022

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.

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This is a decorative image for: Constructing 3D objects through Deep Learning
October 18, 2022

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:

  • Input

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.

  • Output

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

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This is a decorative image for: Comprehensive Guide to Deep Q-Learning for Data Science Enthusiasts
October 18, 2022

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:

  1. Initializing parameters – The RL (reinforcement learning) model learns the set of actions that the agent requires in the state, environment and time.
  2. 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.
  3. 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.
  4. 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.

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This is a decorative image for: GAUDI: A Neural Architect for Immersive 3D Scene Generation
October 13, 2022

GAUDI: A Neural Architect for Immersive 3D Scene Generation

The evolution of artificial intelligence in the past decade has been staggering, and now the focus is shifting towards AI and ML systems to understand and generate 3D spaces. As a result, there has been extensive research on manipulating 3D generative models. In this regard, Apple’s AI and ML scientists have developed GAUDI, a method specifically for this job.

An introduction to GAUDI

The GAUDI 3D immersive technique founders named it after the famous architect Antoni Gaudi. This AI model takes the help of a camera pose decoder, which enables it to guess the possible camera angles of a scene. Hence, the decoder then makes it possible to predict the 3D canvas from almost every angle.

What does GAUDI do?

GAUDI can perform multiple functions –

  • The extensions of these generative models have a tremendous effect on ML and computer vision. Pragmatically, such models are highly useful. They are applied in model-based reinforcement learning and planning world models, SLAM is s, or 3D content creation.
  • Generative modelling for 3D objects has been used for generating scenes using graf, pigan, and gsn, which incorporate a GAN (Generative Adversarial Network). The generator codes radiance fields exclusively. Using the 3D space in the scene along with the camera pose generates the 3D image from that point. This point has a density scalar and RGB value for that specific point in 3D space. This can be done from a 2D camera view. It does this by imposing 3D datasets on those 2D shots. It isolates various objects and scenes and combines them to render a new scene altogether.
  • GAUDI also removes GANs pathologies like mode collapse and improved GAN.
  • GAUDI also uses this to train data on a canonical coordinate system. You can compare it by looking at the trajectory of the scenes.

How is GAUDI applied to the content?

The steps of application for GAUDI have been given below:

  • Each trajectory is created, which consists of a sequence of posed images (These images are from a 3D scene) encoded into a latent representation. This representation which has a radiance field or what we refer to as the 3D scene and the camera path is created in a disentangled way. The results are interpreted as free parameters. The problem is optimized by and formulation of a reconstruction objective.
  • This simple training process is then scaled to trajectories, thousands of them creating a large number of views. The model samples the radiance fields totally from the previous distribution that the model has learned.
  • The scenes are thus synthesized by interpolation within the hidden space.
  • The scaling of 3D scenes generates many scenes that contain thousands of images. During training, there is no issue related to canonical orientation or mode collapse.
  • A novel de-noising optimization technique is used to find hidden representations that collaborate in modelling the camera poses and the radiance field to create multiple datasets with state-of-the-art performance in generating 3D scenes by building a setup that uses images and text.

To conclude, GAUDI has more capabilities and can also be used for sampling various images and video datasets. Furthermore, this will make a foray into AR (augmented reality) and VR (virtual reality). With GAUDI in hand, the sky is only the limit in the field of media creation. So, if you enjoy reading about the latest development in the field of AI and ML, then keep a tab on the blog section of the E2E Networks website.

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