2020-05-01から1ヶ月間の記事一覧
Chapter 19 Training and Deploying TensorFlow Models at Scale Hands-On Machine Learning with Scikit-Learn, Keras & Tensorflow 2nd Edition by A. Geron Chapter 2は、KaggleのTitanicと並行して勉強していたので、何をどこまで学んだか忘れてしまった…
Chapter 18 Reinforcement Learning Hands-On Machine Learning with Scikit-Learn, Keras & Tensorflow 2nd Edition by A. Geron Reinforcement Learning (RL) is one of the most exciting fields of Machine Learning today, and also one of the oldeat.…
Chapter 17 Representation Learning and Generative Learning Using Autoencoders and GANs Hands-On Machine Learning with Scikit-Learn, Keras & Tensorflow 2nd Edition by A. Geron Autoencoders are artificial neural networks capable of learning …
Chapter 16 Natural Langage Processing with RNNs and Attention Hands-On Machine Learning with Scikit-Learn, Keras & Tensorflow 2nd Edition by A. Geron style=137 iteration=1 style=137 iteration=20 style=137 iteration=500
Chapter 15 Processing Sequences Using RNNs and CNNs Hands-On Machine Learning with Scikit-Learn, Keras & Tensorflow 2nd Edition by A. Geron RNNs are not the only types of neural networks capable of handling sequential data: for small seque…
Chapter 14 Deep Computer Vision Using Convolutional Neural Network Hands-On Machine Learning with Scikit-Learn, Keras & Tensorflow 2nd Edition by A. Geron In this Chapter we will explore where CNNs came from, what their building blocks loo…
Chapter 13 Loading and Preprocessing Data with TensorFlow Hands-On Machine Learning with Scikit-Learn, Keras & Tensorflow 2nd Edition by A. Geron So far we have used only datasets that fit in memory, but Deep Learning systema are often tra…
Chapter 12 Custom Models and Training with TensorFlow Hands-On Machine Learning with Scikit-Learn, Keras & Tensorflow 2nd Edition by A. Geron Up until now, we've used only TensorFlow's high-level API, tf.keras, but it already got us pretty…
Chapter 11 Training Deep Neural Networks Hands-On Machine Learning with Scikit-Learn, Keras & Tensorflow 2nd Edition by A. Geron In Chapter 10 we introduced artificial neural networks and trained our first deep neural networks. But they we…
Chapter 10 Introduction to Artificial Neural Networks with Keras Hands-On Machine Learning with Scikit-Learn, Keras & Tensorflow 2nd Edition by A. Geron From Biological to Artificial Neurons Biological Neurons Logical Computations with Neu…
Chapter 9 Unsupervised Learning Techniques Hands-On Machine Learning with Scikit-Learn, Keras & Tensorflow 2nd Edition by A. Geron Although most of the applications of Machine Learning today are based on supervised learning (and as a resul…
Chapter 8 Dimensionality Reduction Hands-On Machine Learning with Scikit-Learn, Keras & Tensorflow 2nd Edition by A. Geron Many Machine Learning problems involve thousands or even millions of features for each training instance. Not only d…
Chapter 7 Ensemble Learning and Random Forests Hands-On Machine Learning with Scikit-Learn, Keras & Tensorflow 2nd Edition by A. Geron Suppose you pose a complex question to thousands of random people, then aggregate their answers. In many…
Chapter 6 Decision Trees Hands-On Machine Learning with Scikit-Learn, Keras & Tensorflow 2nd Edition by A. Geron Like SVMs Decision Trees are versatile Machine Learning algorithms that can perform both classification and regression tasks, …
Chapter 5 Support Vector Machines Hands-On Machine Learning with Scikit-Learn, Keras & Tensorflow 2nd Edition by A. Geron A Support Vector Machine (SVM) is a powerful and versatile Machine Learning model, capable of performing linear or no…
Chapter 4 Training Models Hands-On Machine Learning with Scikit-Learn, Keras & Tensorflow 2nd Edition by A. Geron 2章、3章で、housingデータを使ったregressionと、MNISTデータを使ったclassifierを学んで、いろいろなモデルやテクニックを実際に使…
Chapter 3 Classification Hands-On Machine Learning with Scikit-Learn, Keras & Tensorflow 2nd Edition by A. Geron MNIST MNISTの説明：手書き数字のデータベースで、機械学習の"hello world"である。 Scikit-Learnからダウンロードできる。 from sklea…
Titanic: Machine Learning from Disaster - 2 A. Geronさんのテキスト第2章の勉強の成果を試そう！ 意気込んでとりかかったのはよいが、いきなり、つまづいた。 A. Geronさんのテキストで学んだhousingは、住宅価格を予測するということで、回帰問題、regre…
Titanic: Machine Learning from Disaster Kaggleを、一からやりなおそう！ やりなおす理由： １．地固めせずにいろいろ手を出し、背伸びしすぎて、現在地がわからなくなった。 ２．コンペに参加しても、結果を提出できるところまでたどり着けない。 ３．pan…
Deep Learning with Python 9.2 The limitations of deep learning F. Chollet氏の"The Measure of Intelligence"は、非常に難しい。 F. Chollet氏のテキスト、"Deep Learning with Python"の9章が、"The Measure of Intelligence"の序章になっているように…
