🔹Deep Learning-powering machines with human intelligence

🖊 by Ashish Sukhadeve

🔻Deep learning uses neural networks with many intermediate layers of artificial “neurons” between the input and the output, inspired by the human brain. The technology excels at modeling extremely complicated relationships between these layers to classify and predict things.

🔻Deep learning is making a significant contribution to the business world, and the economy is already beginning to feel the impact. The deep learning market is expected to reach $18.2 billion by 2023 from $3.2 billion in 2018, growing at a CAGR of 41.7%. The confluence of three factors- the rise of big data, the emergence of powerful graphics processing units (GPUs), and increasing adoption of cloud computing is fuelling the rapid growth of deep learning.
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📌Via: @cedeeplearning
📌Other social media: https://linktr.ee/cedeeplearning

link: https://www.analyticsinsight.net/deep-learning-powering-machines-with-human-intelligence/

#deeplearning #machinelearning
#neuralnetworks
#business #market

🔹Are facial dataset sufficient to perform sentiment analysis using emotional AI?

🖊by Smriti Srivastava

According to Harvard Business Review, the days when #AI technology will be able to “recognize, process, and simulate” human emotions are not that far away. As the emotional AI has already made itself a unique place in the affective computing market, it has been predicted that its market size could grow to about US$ 41 billion by 2022.

The technology of #sentiment_analysis or emotion analysis caters great insights into rapidly growing customer service issues in an effort to conveniently identify and act on the root cause of issues or even mitigate them before they reach critical mass.
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📌Via: @cedeeplearning

https://www.analyticsinsight.net/facial-datasets-sufficient-perform-sentiment-analysis-using-emotional-ai/

#emotional_ai #deeplearning
#machinelearning #sentiment
#facial_recognition

🔻Speeding Up Deep Learning Inference Using TensorRT

📈 This version starts from a #PyTorch model instead of the #ONNX model, upgrades the sample application to use #TensorRT 7, and replaces the ResNet-50 #classification model with UNet, which is a segmentation model.

📈 NVIDIA TensorRT is an SDK for deep learning inference. TensorRT provides APIs and parsers to import trained models from all major deep learning frameworks. It then generates optimized runtime engines deployable in the datacenter as well as in automotive and embedded environments.

🔹Simple TensorRT example

🔻Convert the pretrained image segmentation PyTorch model into ONNX.
🔻Import the ONNX model into TensorRT.
🔻Apply optimizations and generate an engine.
🔻Perform inference on the GPU.

⭕️ DO NOT MISS OUT THIS ARTICLE
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📌Via: @cedeeplearning
📌Other social media: https://linktr.ee/cedeeplearning

link: https://devblogs.nvidia.com/speeding-up-deep-learning-inference-using-tensorrt/

#NVIDIA #deeplearning #neuralnetworks #python
#machinelearning #AI

🔹 A Primer on Neural Network Models
for Natural Language Processing

This tutorial covers input encoding for natural language tasks, feed-forward networks, convolutional networks, recurrent networks and recursive networks, as well as the computation graph abstraction for automatic gradient computation.
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📌Via: @cedeeplearning

#deeplearning #CNN #tutorial
#neuralnetworks #RNN #paper #nlp

🔻 How cognitive technologies are redefining the future of manufacturing?

🖊by Kanti S

As factories and equipment get smarter and armed with new technologies, like IoT, AI, and Cognitive Automation, Industry 4.0 has finally arrived. Industry 4.0 a term coined in Germany to computerize manufacturing, has now launched into a worldwide phenomenon.

🔹The Future is Bright for Cognitive Manufacturing

As the pace of technological advancement keeps increasing, Cognitive Manufacturing is a trend of today redefining commonplace activities that were impossible to comprehend by technology 10 or 15 years ago.
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📌Via: @cedeeplearning

https://www.analyticsinsight.net/how-cognitive-technologies-are-redefining-the-future-of-manufacturing/

#deeplearning
#cognitive #AI
#manufacturing

⚪️Basics of Neural Network Programming

✒️ by prof. Andrew Ng
🔹Source: Coursera

🔖 Lecture 4 Binary Classification

Neural Networks and Deep Learning
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📌Via: @cedeeplearning
📌Other social media: https://linktr.ee/cedeeplearning

#DeepLearning #NeuralNeworks
#machinelearning #AI #coursera
#free #python #supervised_learning
#classification #binary

FineGym: A Hierarchical Video Dataset for Fine-grained Action Understanding
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@cedeeplearning

[Received highest review score at CVPR 2020] FineGym: A Hierarchical Video Dataset for Fine-grained Action Understanding

Page: https://sdolivia.github.io/FineGym/

Arxiv: https://arxiv.org/abs/2004.06704v1?utm_content=bufferd6581&utm_medium=social&utm_source=twitter.com&utm_campaign=buffer

(the author will be livestreaming at AISC: https://buff.ly/3feLIrk) —> Time: Friday 15-May-2020 04:00

The Chinese University of Hong Kong: http://www.cuhk.edu.hk/english/index.html
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📌Via: @cedeeplearning

🔻 Learning to Smell: Using Deep Learning to Predict the Olfactory Properties of Molecules

🔹Smell is a sense shared by an incredible range of living organisms, and plays a critical role in how they analyze and react to the world. For humans, our sense of smell is tied to our ability to enjoy food and can also trigger vivid memories.

🔹In “Machine Learning for Scent: Learning Generalizable Perceptual Representations of Small Molecules”, we leverage graph neural networks (GNNs), a kind of deep neural network designed to operate on graphs as input, to directly predict the odor denoscriptors for individual molecules, without using any handcrafted rules.
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📌Via: @cedeeplearning

https://ai.googleblog.com/2019/10/learning-to-smell-using-deep-learning.html

#deeplearning #neuralnetworks
#GNN #graph #network #machinelearning

🔻Quantum Supremacy Using a Programmable #Superconducting #Processor

🔹Physicists have been talking about the power of quantum computing for over 30 years, but the questions have always been: will it ever do something useful and is it worth investing in? For such large-scale endeavors it is good engineering practice to formulate decisive short-term goals that demonstrate whether the designs are going in the right direction.

🔹Today we published the results of this quantum supremacy experiment in the Nature article, “Quantum Supremacy Using a Programmable Superconducting Processor”. We developed a new 54-qubit processor, named “Sycamore”, that is comprised of fast, high-fidelity quantum logic gates, in order to perform the benchmark testing.
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📌Via: @cedeeplearning

https://ai.googleblog.com/2019/10/quantum-supremacy-using-programmable.html

#quantomcomputing #deeplearning
#machinelearning #neuralnetworks
#AI #sycamore #hardware

📝 Machine Learning Resume Sample: how to build a strong ML Resume

🔻Tips to make machine learning resume
🔻What are the must-have skills for an AI resume
🔻Common skills that employers look for on an ML Resume
🔻How to master programming languages
🔻Creating your Machine Learning Resume
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📌Via: @cedeeplearning

https://www.mygreatlearning.com/blog/5-must-haves-machine-learning-resume/#tips

#resume #machinelearning
#datascience #skill #AI
#python #programming

⚪️Basics of Neural Network Programming

✒️ by prof. Andrew Ng
🔹Source: Coursera

🔖 Lecture 5 Derivatives

Neural Networks and Deep Learning
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📌Via: @cedeeplearning
📌Other social media: https://linktr.ee/cedeeplearning

#DeepLearning #NeuralNeworks
#machinelearning #AI #coursera
#free #python #supervised_learning
#classification #binary

🔗 Build your Own Object Detection Model using #TensorFlow API

🔻The World of Object Detection

🔹One of my favorite computer vision and deep learning concepts is object detection. The ability to build a model that can go through images and tell me what objects are present – it’s a priceless feeling!

👍 Nice reading article
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📌Via: @cedeeplearning

https://www.analyticsvidhya.com/blog/2020/04/build-your-own-object-detection-model-using-tensorflow-api/

#object_detection
#imagedetection
#deeplearning #computervision
#AI #machinelearning
#neuralnetworks

📗13 ‘Must-Read’ Papers from AI Experts

1. Learning to Reinforcement Learn (2016) - Jane X Wang et al

2. Gradient-based Hyperparameter Optimization through Reversible Learning (2015) - Dougal Maclaurin

3. Long Short-Term Memory (1997) - Sepp Hochreiter and Jürgen Schmidhuber

4. Efficient Incremental Learning for Mobile Object Detection (2019) - Dawei Li et al

5. Emergent Tool Use From Multi-Agent Autocurricula (2019) - Bowen Baker et al

6. Open-endedness: The last grand challenge you’ve never heard of (2017) - Kenneth Stanley et al

7. Attention Is All You Need (2017) - Ashish Vaswani et al

8. Modeling yield response to crop management using convolutional neural networks (2020) - Andre Barbosa et al.

9. A comparison of deep learning performance against health-care professionals in detecting diseases from medical imaging: a systematic review and meta-analysis (2019) - Xiaoxuan Liu et al

10. The Next Decade in AI: Four Steps Towards Robust Artificial Intelligence (2020) - Gary Marcus

11. On the Measure of Intelligence (2019) - François Chollet

12. Tackling climate change with Machine Learning (2019) - David Rolnick, Priya L Donti, Yoshua Bengio et al.

13. The Netflix Recommender System: Algorithms, Business Value, and Innovation (2015) - Carlos Gomez-Uribe & Neil Hunt.
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📌Via: @cedeeplearning

https://blog.re-work.co/ai-papers-suggested-by-experts/
#paper #resource #free #AI
#machinelearning #datascience

⚪️Basics of Neural Network Programming

✒️ by prof. Andrew Ng
🔹Source: Coursera

🔖 Lecture 6 Gradient Descent

Neural Networks and Deep Learning
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📌Via: @cedeeplearning
📌Other social media: https://linktr.ee/cedeeplearning

#DeepLearning #NeuralNeworks
#machinelearning #AI #coursera
#free #python #supervised_learning
#classification #gradient

🔹Jump-start Training for #Speech_Recognition Models in Different Languages with NVIDIA NeMo

🖊By Oleksii Kuchaiev

Transfer learning is an important machine learning technique that uses a model’s knowledge of one task to make it perform better on another. Fine-tuning is one of the techniques to perform transfer learning.
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📌Via: @cedeeplearning

https://devblogs.nvidia.com/jump-start-training-for-speech-recognition-models-with-nemo/

#deeplearning #neuralnetworks
#machinelearning #NVIDIA
#AI #datascience #math
#nemo #model #data

🔹Announcing NVIDIA NeMo: Fast Development of Speech and Language Models

🖊By Raghav Mani

🔻The inputs and outputs, coding style, and data processing layers in these models may not be compatible with each other. Worse still, you may be able to wire up these models in your code in such a way that it technically “works” but is in fact semantically wrong. A lot of time, effort, and duplicated code goes into making sure that you are reusing models safely.

🔻Build a simple ASR model to see how to use NeMo. You see how neural types provide semantic safety checks, and how the tool can scale out to multiple GPUs with minimal effort.
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📌Via: @cedeeplearning

https://devblogs.nvidia.com/announcing-nemo-fast-development-of-speech-and-language-models/

#deeplearning #neuralnetworks
#machinelearning #NVIDIA
#AI #datascience #math
#nemo #model #data

🔹⚙️ Everything about NVIDIA Deep Learning

Nvidia Deep Learning AI lets users pull insights from big data. This lets them realize their true value by utilizing them in creating solutions for current and forecasted problems. This allows them to arm themselves with the knowledge that can prove to be instrumental at a time when a challenge arises.

1. What is Nvidia Deep Learning AI?
2. Nvidia Deep Learning AI benefits
3. Overview of Nvidia Deep Learning AI features
4. Nvidia Deep Learning AI pricing
5. User satisfaction
6. Video
7. Technical details
8. Support details
9. User reviews
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📌Via: @cedeeplearning

https://reviews.financesonline.com/p/nvidia-deep-learning-ai/

#deeplearning #NVIDIA
#machinelearning
#bigdata #analytics
#neuralnetworks

⚪️Basics of Neural Network Programming

✒️ by prof. Andrew Ng
🔹Source: Coursera

Lecture 7 Logistic Regression Cost Function

Neural Networks and Deep Learning
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📌Via: @cedeeplearning
📌Other social media: https://linktr.ee/cedeeplearning

#DeepLearning #NeuralNeworks
#machinelearning #AI #coursera
#free #python #supervised_learning
#classification #logistic_regression
#regression

📕 State of Deep Reinforcement Learning: Inferring future outlook

Today machines can teach themselves based upon the results of their own actions. This advancement in Artificial Intelligence seems like a promising technology through which we can explore more innovative potentials of AI. The process is termed as deep reinforcement learning.

🔻What Future Holds for Deep Reinforcement Learning?

Experts believe that deep reinforcement learning is at the cutting-edge right now and it has finally reached a to be applied in real-world applications. They also believe that moving it will have a great impact on AI advancement and can eventually researchers closer to Artificial General Intelligence (AGI).
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📌Via: @cedeeplearning

https://www.analyticsinsight.net/state-deep-reinforcement-learning-inferring-future-outlook/

#deeplearning #AI #AGI
#reinforcement #math
#datascience #machinelearning

📕 Data Mining Methods for Recommender Systems

✒️ by Xavier Amatriain
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📌Via: @cedeeplearning

#datamining #recommendersystems
#clustering #classification #regression
#machinelearning #datascience