LSTM, Sampling, Smart Code Completion Tool

How Do You Generate Sequence Data?


Bidirectional RNNs, Encoding, Word Embedding and Tips

What's a Bidirectional RNN?


Recurrent Layers: SimpleRNN, LSTM, GRU

What’s SimpleRNN?

from keras.layers import SimpleRNN
  • All of the successive outputs aka the states
  • Just the last state
from keras.models import Sequential
from keras.layers import Embedding, SimpleRNN
model = Sequential() model.add(Embedding(10000…


Easing Into Recurrent Neural Networks

Translation Invariance

A convolutional model can learn a certain pattern in the lower right area, then after that point detect it anywhere on the image.

Spatial Hierarchy

A convolutional model can learn patterns in a hierarchical fashion, much like we do. The first layers will learn relatively simple patterns, like horizontalness and verticalness etc. Then the second layers will put these together to learn such things as corners. And so on with each new layer.


Convolutions, Kernels, Downsampling & Properties

What does a CNN model look like in code?

from keras import layers 
from keras import models
seq_model= models.Sequential()
seq_model.add(layers.Conv2D(32, (3, 3), activation='relu', input_shape=(28, 28, 1)))
seq_model.add(layers.MaxPooling2D((2, 2)))
seq_model.add(layers.Conv2D(64, (3, 3), activation='relu')) seq_model.add(layers.MaxPooling2D((2, 2)))
seq_model.add(layers.Conv2D(128, (3, 3), activation='relu'))
from keras import modelsseq_model= models.Sequential()
from keras.models import Sequential, Modelnon_seq_model = Model(input_tensor, output_tensor)
from keras import layersseq_model.add(layers.Conv2D(32, (3, 3), activation='relu', input_shape=(28, 28, 1)))
  • Here the input_shape is (28, 28, 1), which takes an input tensors of shape (image_height, image_width, image_channels). …


CODEX

Problem Solving


Minimization, Finite State Transducers, Regular Relations

Summary of the Previous Article

  • Language
  • Strings
  • Symbols
  • Sets
  • Operations
  • Properties
  • Rules
  • Classes of Languages


Search Functions, Statistics, Pronoun Resolution

https://www.newyorker.com/culture/culture-desk/living-in-alan-turings-future

Introduction

What is NLTK?


K-Fold Validation, Normalization, MAE

Overview

Basics:

  • Goal
  • Input & Output
  • Encoding & Decoding
  • Architecture
  • Regularization
  • Validation

Code:

  • Import
  • Model Definition
  • K-Fold Validation
  • Validation
  • Final Model

Goal

Input & Output

  1. CRIM per capita crime rate by town
  2. ZN proportion of residential land zoned for lots over 25,000 sq.ft.
  3. INDUS proportion of non-retail business acres per town
  4. CHAS Charles River dummy variable (= 1 if tract bounds river; 0…

Jake Batsuuri

I write about software && math

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