Neural Networks | Нейронные сети
11.6K subscribers
803 photos
184 videos
170 files
9.45K links
Все о машинном обучении

По всем вопросам - @notxxx1

№ 4959169263
Download Telegram
Наш телеграм канал - tglink.me/ai_machinelearning_big_data
https://www.youtube.com/watch?v=fcD6YeEYKNg

🎥 Deep Learning With Python | Deep Learning And Neural Networks | Deep Learning Tutorial | Simplilearn
👁 1 раз 3055 сек.
This video on Deep Learning with Python will help you understand what is deep learning, applications of deep learning, what is a neural network, biological versus artificial neural networks, introduction to TensorFlow, activation function, cost function, how neural networks work, and what gradient descent is. Deep learning is a technology that is used to achieve machine learning through neural networks. We will also look into how neural networks can help achieve the capability of a machine to mimic human be
🎥 Feature Ranking in Keras and Scikit-Learn: Perturbation Ranking
👁 1 раз 1035 сек.
Perturbation Ranking will tell which imports are the most important for any machine learning model, such as a deep neural network. The provided code work with TensorFlow and Keras. Because Perturbation ranking uses no internal model information (only results from generated inputs), it can be used with any classification or regression model.

Code for this video: https://github.com/drcannady/pub/tree/master/ijcnn-2017

Follow Me/Subscribe:
https://www.youtube.com/user/HeatonResearch
https://github.com/jeff
🎥 TensorFlow In 10 Minutes | TensorFlow Tutorial For Beginners | TensorFlow Training | Edureka
👁 1 раз 541 сек.
*** AI and Deep-Learning with TensorFlow - https://www.edureka.co/ai-deep-learning-with-tensorflow ***
This video provides you with a basic introduction to TensorFlow: The amazing deep learning framework by Google.


*** Complete Tensorflow Playlist: https://www.youtube.com/playlist?list=PL9ooVrP1hQOFJ8UZl86fYfmB1_P5yGzBT ***

Join our Meetup group and never miss any free live webinar: http://bit.ly/2DQO5PL

--------------------------------------------------

Subscribe to our Edureka YouTube channel to get
🎥 Машинное обучение 12. RNN
👁 1 раз 5060 сек.
Лекция 30.04.2019
Лектор: Радослав Нейчев

Снимал: Александр Гришутин
Монтировал: Александр Васильев
​Dataset bridges human vision and machine learning

🔗 Dataset bridges human vision and machine learning
Neuroscientists and computer vision scientists say a new dataset of unprecedented size—comprising brain scans of four volunteers who each viewed 5,000 images—will help researchers better understand ...
🎥 Deep Neural Networks step by step final prediction model #part 5
👁 1 раз 1737 сек.
In this tutorial we will use the functions we had implemented in the previous parts to build a deep network, and apply it to cat vs dog classification. Hopefully, we will see an improvement in accuracy relative to our previous logistic regression implementation. After this part we will be able to build and apply a deep neural network to supervised learning using only numpy library.

Full tutorial code and cats vs dogs image data-set can be found on my GitHub page: https://github.com/pythonlessons/Logistic-r
🎥 Deep Learning and Modern NLP - Zachary S Brown
👁 1 раз 5393 сек.
In this tutorial, we’ll cover the fundamental building blocks of neural network architectures and how they are utilized to tackle problems in modern natural language processing. Topics covered will include an overview of language vector representations, text classification, named entity recognition, and sequence to sequence modeling approaches. An emphasis will be placed on the shape of these types of problems from the perspective of deep learning architectures. This will help to develop an intuition for id
🎥 Leveraging NLP and Deep Learning for Document Recommendations in the CloudGuoqiong Song Intel
👁 1 раз 1254 сек.
Efficient recommender systems are critical for the success of many industries, such as job recommendation, news recommendation, ecommerce, etc. This talk will illustrate how to build an efficient document recommender system by leveraging Natural Language Processing(NLP) and Deep Neural Networks (DNNs). The end-to-end flow of the document recommender system is build on AWS at scale, using Analytics Zoo for Spark and BigDL. The system first processes text rich documents into embeddings by incorporating Global