3.6 Stochastic Gradient Descent In this lecture, we’ll see how stochastic gradient descent can be used to improve the learning algorithm by reducing time, memory, and convergence on local minima. Code Course Curriculum (See the code on GitHub) Introduction 1.1 Introduction Perceptron 2.1 MNIST Dataset 2.2 Perceptron Model 2.3 Perceptron Learning Algorithm 2.4 Pocket Algorithm 2.5 Multiclass Support 2.6 Perceptron To Neural Network Neural Network
3.7 Going Further In this lecture, we’ll tie up some loose and talk about neural network topics beyond the scope of this course. Code Course Curriculum (See the code on GitHub) Introduction 1.1 Introduction Perceptron 2.1 MNIST Dataset 2.2 Perceptron Model 2.3 Perceptron Learning Algorithm 2.4 Pocket Algorithm 2.5 Multiclass Support 2.6 Perceptron To Neural Network Neural Network 3.1 Simple Images 3.2 Random Weights 3.3 Gradient Descent
Python Pandas For Your Grandpa Wanna learn Pandas? I did. And I consolidated everything I learned into a 43 videos spanning roughly three hours of content, including 23 lecture videos (~2 hrs) and 20 challenge videos (~1 hr). Course Curriculum Introduction 1.1 Introduction Series 2.1 Series Creation 2.2 Series Basic Indexing 2.3 Series Basic Operations 2.4 Series Boolean Indexing 2.5 Series Missing Values 2.6 Series Vectorization 2.7 Series apply()
Introduction Hey, thanks for checking out my course - Python Pandas for your Grandpa, so easy your grandpa could learn it! Things To Know. I’m using Pandas version 1.20.0 If you’re using a later version, it probably doesn’t matter. (Most of what I teach is unlikely to break for at least a few years). I’m using Google Colab as my IDE. You don’t need to use Google Colab, but if you want to, it’s a really awesome, free way to run Python directly inside your browser with essentially zero maintenance and zero hassle setting things up.
Pandas is a vast library of data wrangling tools, but all those tools are centered around two fundamental data structures: Series and DataFrame. If you imagine a table of data, you can think of each column as a Series and the structured collection of every column as a DataFrame. If you know NumPy, you might be wondering, what’s the difference between a Series and a Numpy 1-d array? After all, they both represent a 1-dimensional set of values.
Setup Given, two Series bees and knees, if the ith value of bees is NaN, double the ith value inside knees. import numpy as np import pandas as pd bees = pd.Series([True, True, False, np.nan, True, False, True, np.nan]) print(bees) ## 0 True ## 1 True ## 2 False ## 3 NaN ## 4 True ## 5 False ## 6 True ## 7 NaN ## dtype: object knees = pd.