# Neural Networks For Your Dog - 3.4 Multiclass Support

3.4 Multiclass Support In this lecture, we’ll use softmax and cross entropy to turn our binary neural network classifier into a multiclass classifier. 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

# Neural Networks For Your Dog - 3.5 Deep Learning

3.5 Deep Learning In this lecture, we’ll see how we can generalize our neural network model to support multiple hidden layers (i.e. deep learning) and why doing so is helpful. 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

3.6 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

# Neural Networks For Your Dog - 3.7 Going Further

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

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). Now available in written format on Practice Probs! 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

# Python Pandas For Your Grandpa - 1.1 Introduction

Now available in written format on Practice Probs! 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() 2.8 Series View vs Copy 2.9 Challenge: Baby Names 2.10 Challenge: Bees Knees 2.11 Challenge: Car Shopping 2.12 Challenge: Price Gouging 2.13 Challenge: Fair Teams DataFrame 3.1 DataFrame Creation