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deep learing

Python Machine Learning Third Edition Machine Learning and Deep Learning with Python,  scikit-learn, and TensorFlow 2 Sebastian Raschka Vahid Mirjalili

Dive deep into the world of AI with our Deep Learning with Python and PyTorch. Enroll at BITA Academy for a promising AI career

Dive deep into the world of AI with our Deep Learning with Python and PyTorch. Enroll at BITA Academy for a promising AI career

Machine Learning, Data Science And Deep Learning With Python

Deep Learning With Python

Python For Computer Vision With Open CV And Deep Learning

Ian Lewis - Deep Learning with Python & TensorFlow [EuroPython 2016] [22 July 2016 / 2016-07-22] [Bilbao, Euskadi, Spain] (https://ep2016.europython.eu//conference/talks/deep-learning-with-python-tensorflow) Python has lots of scientific, data analysis, and machine learning libraries. But there are many problems when starting out on a machine learning project. Which library do you use? How do they compare to each other? How can you use a model that has been trained in your production app? In this talk I will discuss how you can use TensorFlow to create Deep Learning applications. I will discuss how it compares to other Python machine learning libraries, and how to deploy into production. ----- Python has lots of scientific, data analysis, and machine learning libraries. But there are many problems when starting out on a machine learning project. Which library do you use? How do they compare to each other? How can you use a model that has been trained in your production application? TensorFlow is a new Open-Source framework created at Google for building Deep Learning applications. Tensorflow allows you to construct easy to understand data flow graphs in Python which form a mathematical and logical pipeline. Creating data flow graphs allow easier visualization of complicated algorithms as well as running the training operations over multiple hardware GPUs in parallel. In this talk I will discuss how you can use TensorFlow to create Deep Learning applications. I will discuss how it compares to other Python machine learning libraries like Theano or Chainer. Finally, I will discuss how trained TensorFlow models could be deployed into a production system using TensorFlow Serve.

Python for Computer Vision By Jose Poritla

Jupyter notebooks for the code samples of the book "Deep Learning with Python" Companion Jupyter notebooks for the book "Deep Learning with Python" This repository contains Jupyter notebooks implementing the code samples found in the book Deep Learning with Python (Manning Publications) . Note that the original text of the book features far more content than you will find in these notebooks, in particular further explanations and figures. Here we have only included the code samples themselves and immediately related surrounding comments. These notebooks use Python 3.6 and Keras 2.0.8. They were generated on a p2.xlarge EC2 instance. Table of contents Chapter 2: 2.1: A first look at a neural network Chapter 3: 3.5: Classifying movie reviews 3.6: Classifying newswires 3.7: Predicting house prices Chapter 4: 4.4: Underfitting and overfitting Chapter 5: 5.1: Introduction to convnets 5.2: Using convnets with small datasets 5.3: Using a pre-trained convnet 5.4: Visualizing what convnets learn Chapter 6: 6.1: One-hot encoding of words or characters 6.1: Using word embeddings 6.2: Understanding RNNs 6.3: Advanced usage of RNNs 6.4: Sequence processing with convnets Chapter 8: 8.1: Text generation with LSTM 8.2: Deep dream 8.3: Neural style transfer 8.4: Generating images with VAEs 8.5: Introduction to GANs To restore the repository, download the bundle fchollet-deep-learning-with-python-notebooks_-_2017-09-06_17-02-25.bundle and run: git clone fchollet-deep-learning-with-python-notebooks_-_2017-09-06_17-02-25.bundle -b master Source: https://github.com/fchollet/deep-learning-with-python-notebooks Uploader: fchollet Upload date: 2017-09-06

Jupyter notebooks for the code samples of the book "Deep Learning with Python" Companion Jupyter notebooks for the book "Deep Learning with Python" This repository contains Jupyter notebooks implementing the code samples found in the book Deep Learning with Python (Manning Publications) . Note that the original text of the book features far more content than you will find in these notebooks, in particular further explanations and figures. Here we have only included the code samples themselves and immediately related surrounding comments. These notebooks use Python 3.6 and Keras 2.0.8. They were generated on a p2.xlarge EC2 instance. Table of contents Chapter 2: 2.1: A first look at a neural network Chapter 3: 3.5: Classifying movie reviews 3.6: Classifying newswires 3.7: Predicting house prices Chapter 4: 4.4: Underfitting and overfitting Chapter 5: 5.1: Introduction to convnets 5.2: Using convnets with small datasets 5.3: Using a pre-trained convnet 5.4: Visualizing what convnets learn Chapter 6: 6.1: One-hot encoding of words or characters 6.1: Using word embeddings 6.2: Understanding RNNs 6.3: Advanced usage of RNNs 6.4: Sequence processing with convnets Chapter 8: 8.1: Text generation with LSTM 8.2: Deep dream 8.3: Neural style transfer 8.4: Generating images with VAEs 8.5: Introduction to GANs To restore the repository download the bundle wget https://archive.org/download/github.com-fchollet-deep-learning-with-python-notebooks_-_2019-12-15_19-21-33/fchollet-deep-learning-with-python-notebooks_-_2019-12-15_19-21-33.bundle and run: git clone fchollet-deep-learning-with-python-notebooks_-_2019-12-15_19-21-33.bundle Source: https://github.com/fchollet/deep-learning-with-python-notebooks Uploader: fchollet Upload date: 2019-12-15

Machine learning