Deep Learning basics with Python, TensorFlow

🎥 Deep Learning basics with Python, TensorFlow
👁 1 раз ⏳ 644 сек.
Deep Learning basics with Python, TensorFlow

​How to Detect Outliers in a 2D Feature Space
Outlier detection using plotting and clustering techniques to analyze the dependency of two features with Python

https://towardsdatascience.com/outlier-detection-python-cd22e6a12098?source=collection_home---4------5-----------------------

🔗 Outlier Detection for a 2D Feature Space in Python
Outlier detection using plotting and clustering techniques to analyze the dependency of two features with Python

​Mixing policy gradient and Q-learning
Policy gradient algorithms is a big family of reinforcement learning algorithms, including reinforce, A2/3C, PPO and others.
https://towardsdatascience.com/mixing-policy-gradient-and-q-learning-5819d9c69074?source=collection_home---4------0-----------------------

🔗 Mixing policy gradient and Q-learning
Policy gradient algorithms is a big family of reinforcement learning algorithms, including reinforce, A2/3C, PPO and others. Q-learning is…

​Adversarial-Based Knowledge Distillation for Multi-Model Ensemble and Noisy Data Refinement

Authors: Zhiqiang Shen, Zhankui He, Wanyun Cui, Jiahui Yu, Yutong Zheng, Chenchen Zhu, Marios Savvides

Abstract: Generic Image recognition is a fundamental and fairly important visual problem in computer vision. One of the major challenges of this task lies in the fact that single image usually has multiple objects inside while the labels are still one-hot, another one is noisy and sometimes missing labels when annotated by humans
https://arxiv.org/abs/1908.08520

🔗 Adversarial-Based Knowledge Distillation for Multi-Model Ensemble and Noisy Data Refinement
Generic Image recognition is a fundamental and fairly important visual problem in computer vision. One of the major challenges of this task lies in the fact that single image usually has multiple objects inside while the labels are still one-hot, another one is noisy and sometimes missing labels when annotated by humans. In this paper, we focus on tackling these challenges accompanying with two different image recognition problems: multi-model ensemble and noisy data recognition with a unified framework. As is well-known, usually the best performing deep neural models are ensembles of multiple base-level networks, as it can mitigate the variation or noise containing in the dataset. Unfortunately, the space required to store these many networks, and the time required to execute them at runtime, prohibit their use in applications where test sets are large (e.g., ImageNet). In this paper, we present a method for compressing large, complex trained ensembles into a single network, where the knowledge from a variet

🎥 AlphaFold: improved protein structure prediction using potentials from deep learning
👁 1 раз ⏳ 3760 сек.
Andrew Senior is a research scientist at Google DeepMind and team lead on the AlphaFold project. This talk was recorded at the University of Washington on August 19, 2019.

00:01:25 — Protein structure prediction at DeepMind
00:05:05 — Protein folding problem (overview)
00:07:45 — CASP13 (overview)
00:12:28 — CASP13 results
00:14:55 — AlphaFold system (overview)
00:18:01 — Key aspects of AlphaFold
00:21:00 — Deep learning (overview)
00:25:35 — Why machine learning for protein structure modelling?
00:26:29 —

​Protobuf for NVIDIA Jetson
How to deploy a protobuf model from AutoML Vision with Python on a NVIDIA Jetson

https://medium.com/ri-rewe-digital/protobuf-for-nvidia-jetson-e8b2c6ee47cc?source=topic_page---------0------------------1

🔗 Deploy AutoML protobuf model on NVIDIA Jetson
How to deploy a protobuf model from AutoML Vision with Python on a NVIDIA Jetson

🎥 Support vector machines (machine learning ) in R
👁 1 раз ⏳ 532 сек.
I have explained how to perform support vector machine classifier in machine learning using R

​Seeing is Believing — Mesoscopic Neural Networks for Synthetic Image Detection: an Implementation in

🔗 Seeing is Believing — Mesoscopic Neural Networks for Synthetic Image Detection: an Implementation in
Inception layer-powered intermediate detail level recognition

​📝 Rashid_Sozdaem_neyronnuyu_set.djvu - 💾2 935 222

🎥 24x7 AI - Machine Learning - Data Science Tutorials by World's Best Instructors
👁 5 раз ⏳ 42760 сек.
Collection of besr tutorials in the field of Data Science - played 24x7 - Learn Machine Learning and Artificial Intelligence whenever you can - Just tune in this live Data Science Station

​AI Learns To Animate Your Face in VR

Paper:https://research.fb.com/publications/vr-facial-animation-via-multiview-image-translation/

video: https://www.youtube.com/watch?v=hkSfHCtpnHU

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🎥 AI Learns To Animate Your Face in VR
👁 1 раз ⏳ 243 сек.
❤️ Check out Linode here and get $20 free on your account:
https://www.linode.com/papers

📝 The paper "VR Facial Animation via Multiview Image Translation" is available here:
https://research.fb.com/publications/vr-facial-animation-via-multiview-image-translation/

🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible:
313V, Alex Haro, Andrew Melnychuk, Angelos Evripiotis, Anthony Vdovitchenko, Brian Gilman, Bruno Brito, Bryan Learn, Christian Ahlin, Christoph Jadanows

​Bayesian Deep Learning Benchmarks

https://github.com/OATML/bdl-benchmarks

🔗 OATML/bdl-benchmarks
Bayesian Deep Learning Benchmarks. Contribute to OATML/bdl-benchmarks development by creating an account on GitHub.

​TensorFlow with Apache Arrow Datasets

Apache Arrow enables the means for high-performance data exchange with TensorFlow that is both standardized and optimized for analytics and machine learning. The Arrow datasets from TensorFlow I/O provide a way to bring Arrow data directly into TensorFlow tf.data that will work with existing input pipelines and tf.data.Dataset APIs.

https://medium.com/tensorflow/tensorflow-with-apache-arrow-datasets-cdbcfe80a59f

🔗 TensorFlow with Apache Arrow Datasets
An Overview of Apache Arrow Datasets Plus Example To Run Keras Model Training

​U-Net Training with Instance-Layer Normalization
Наш телеграм канал - tglink.me/ai_machinelearning_big_data
Authors: Xiao-Yun Zhou, Qing-Biao Li, Mali Shen, Peichao Li, Zhao-Yang Wang, Guang-Zhong Yang

Abstract: Normalization layers are essential in a Deep Convolutional Neural Network (DCNN). Various normalization methods have been proposed. The statistics used to normalize the feature maps can be computed at batch, channel, or instance level. However, in most of existing methods, the normalization for each layer is fixed
https://arxiv.org/abs/1908.08466

🔗 U-Net Training with Instance-Layer Normalization
Normalization layers are essential in a Deep Convolutional Neural Network (DCNN). Various normalization methods have been proposed. The statistics used to normalize the feature maps can be computed at batch, channel, or instance level. However, in most of existing methods, the normalization for each layer is fixed. Batch-Instance Normalization (BIN) is one of the first proposed methods that combines two different normalization methods and achieve diverse normalization for different layers. However, two potential issues exist in BIN: first, the Clip function is not differentiable at input values of 0 and 1; second, the combined feature map is not with a normalized distribution which is harmful for signal propagation in DCNN. In this paper, an Instance-Layer Normalization (ILN) layer is proposed by using the Sigmoid function for the feature map combination, and cascading group normalization. The performance of ILN is validated on image segmentation of the Right Ventricle (RV) and Left Ventricle (LV) using U-Net

​The Poisson Process: Everything you need to know
Learn about the Poisson process and how to simulate it using Python

https://towardsdatascience.com/the-poisson-process-everything-you-need-to-know-322aa0ab9e9a?source=collection_home---4------2-----------------------

🔗 The Poisson Process: Everything you need to know
Learn about the Poisson process and how to simulate it using Python

​What is the difference between Optimization and Deep Learning and why should you care
Deep Learning is not just Optimization and we need to do something about it
https://towardsdatascience.com/what-is-the-difference-between-optimization-and-deep-learning-and-why-should-you-care-e4dc7c2494fe?source=collection_home---4------0-----------------------

🔗 What is the difference between Optimization and Deep Learning and why should you care
Deep Learning is not just Optimization and we need to do something about it

​A New Consciousness of Inclusion in Machine Learning
http://blog.shakirm.com/2019/06/a-new-consciousness-of-inclusion-in-machine-learning/

🔗 A New Consciousness of Inclusion in Machine Learning
On LGBT Freedoms and our Support for Machine Learning in Africa This is an exploration of my thinking and my personal views. Read in · 1147 words · Soon, in two neighbouring countries in Africa, tw…

🎥 Industrialized Capsule Net for Text Analytics by Dr. Vijay Agneeswaran & Abhishek Kumar #ODSC_India
👁 1 раз ⏳ 2683 сек.
Multi-label text classification is an interesting problem where multiple tags or categories may have to be associated with the given text/documents. Multi-label text classification occurs in numerous real-world scenarios, for instance, in news categorization and in bioinformatics (gene classification problem, see [Zafer Barutcuoglu et. al 2006]). Kaggle data set is representative of the problem: https://www.kaggle.com/jhoward/nb-svm-strong-linear-baseline/data.

Several other interesting problem in text ana