1604165880

sorry for misspelling network , lol.

All the code files will be available at : https://github.com/ashwinhprasad/PyTorch-For-DeepLearning

Recurrence Neural Network are great for Sequence data and Time Series Data. Long short-term memory is an artificial recurrent neural network architecture used in the field of deep learning. LSTMs and RNNs are used for sequence data and can perform better for timeseries problems.

An LSTM is an advanced version of RNN and LSTM can remember things learnt earlier in the sequence using gates added to a regular RNN. Both LSTM’s and RNN’s working are similar in PyTorch. So, once we coded the Lstm Part, RNNs will also be easier to understand.

In this notebook, we are going to try and predict a sinewave with a Recurrence Neural Network.

Theory for RNNs and LSTMs will not be covered by this post. This is only for pytorch implementation of rnn and lstm.

**Importing the Libraries**

```
#importing the libraries
import numpy as np
import torch
import matplotlib.pyplot as plt
```

**2. Data Pre-processing**

I am creating a sinewave and as I already said, lstm takes sequence inputs.So, The input would be like the following :

**Input : [point 1, point 2, point 3……,point n] prediction : [point n+1].**

we need many rows like this to create the dataset.

```
#creating the dataset
x = np.arange(1,721,1)
y = np.sin(x*np.pi/180) + np.random.randn(720)*0.05
plt.plot(y)
```

#deep-learning #lstm #rnn #pytorch #data-science

1618317562

View more: https://www.inexture.com/services/deep-learning-development/

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

1620466520

If you accumulate data on which you base your decision-making as an organization, you should probably think about your data architecture and possible best practices.

If you accumulate data on which you base your decision-making as an organization, you most probably need to think about your data architecture and consider possible best practices. Gaining a competitive edge, remaining customer-centric to the greatest extent possible, and streamlining processes to get on-the-button outcomes can all be traced back to an organization’s capacity to build a future-ready data architecture.

In what follows, we offer a short overview of the overarching capabilities of data architecture. These include user-centricity, elasticity, robustness, and the capacity to ensure the seamless flow of data at all times. Added to these are automation enablement, plus security and data governance considerations. These points from our checklist for what we perceive to be an anticipatory analytics ecosystem.

#big data #data science #big data analytics #data analysis #data architecture #data transformation #data platform #data strategy #cloud data platform #data acquisition

1618464780

There has been a surge of advancements in automated analysis of 3D data caused by affordable LiDAR sensors, more efficient photogrammetry algorithms, and new neural network architectures. So much that the number of papers related to 3D data being presented at vision conferences is now on par with images, although this rapid methodological development is beneficial to the young field of deep learning for 3D, with its fast pace come several shortcomings:

- Adding new datasets, tasks, or neural architectures to existing approaches is a complicated endeavour, sometimes equivalent to reimplementing from scratch.
- Handling large 3D datasets requires a significant time investment and is prone to many implementation pitfalls.
- There is no standard approach for inference schemes and performance metrics, which makes assessing and reproducing new algorithms’ intrinsic performance difficult.

#developers corner #3d data #deep learning #deep learning frameworks #exploring 3d data in ai #kpconv #point cloud data #python libraries #pytorch 3d #pytorch geometric #torch-points3d

1604165880

sorry for misspelling network , lol.

All the code files will be available at : https://github.com/ashwinhprasad/PyTorch-For-DeepLearning

Recurrence Neural Network are great for Sequence data and Time Series Data. Long short-term memory is an artificial recurrent neural network architecture used in the field of deep learning. LSTMs and RNNs are used for sequence data and can perform better for timeseries problems.

An LSTM is an advanced version of RNN and LSTM can remember things learnt earlier in the sequence using gates added to a regular RNN. Both LSTM’s and RNN’s working are similar in PyTorch. So, once we coded the Lstm Part, RNNs will also be easier to understand.

In this notebook, we are going to try and predict a sinewave with a Recurrence Neural Network.

Theory for RNNs and LSTMs will not be covered by this post. This is only for pytorch implementation of rnn and lstm.

**Importing the Libraries**

```
#importing the libraries
import numpy as np
import torch
import matplotlib.pyplot as plt
```

**2. Data Pre-processing**

I am creating a sinewave and as I already said, lstm takes sequence inputs.So, The input would be like the following :

**Input : [point 1, point 2, point 3……,point n] prediction : [point n+1].**

we need many rows like this to create the dataset.

```
#creating the dataset
x = np.arange(1,721,1)
y = np.sin(x*np.pi/180) + np.random.randn(720)*0.05
plt.plot(y)
```

#deep-learning #lstm #rnn #pytorch #data-science

1603735200

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

**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.

**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.

**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