**Top 10 Great Sites with Free Data Sets**

1. data.world

2. Kaggle

3. FiveThirthyEight

4. BuzzFeed

5. Data.gov

6. Socrata OpenData

7. Quandl

8. Reddit or r/datasets

9. UCI Machine Learning Repository

10. Academic Torrents

**Introduction to PyTorch BigGraph – with Examples**

• Directly use the graph structure and feed it to a neural network. The graph structure is then preserved at every layer. graphCNNs use that approach, see for instance my post or this paper on that.

• But most graphs are too large for that. So it’s also reasonable to create a large embedding of the graph. And then use it as features in a traditional neural network.

PyTorch BigGraph handles the second approach, and we will do so as well below. Just for reference let’s talk about the size aspect for a second. Graphs are usually encoded by their adjacency matrix. If you have a graph with 3,000 nodes and an edge between each node, you end up with around 10,000,000 entries in your matrix. Even if that’s sparse, apparently this bursts most GPUs according to the paper linked above.

**An Introduction to Perceptron Algorithm**

**Creating an Easy Website Scraper for Data Science**

**How to Interpolate Time Series Data in Python Pandas**

**Hands-on TensorFlow Tutorial: Train ResNet-50 From Scratch Using the ImageNet Dataset**

**An introduction to high-dimensional hyper-parameter tuning**

Hyper-parameter tuning refers to the problem of finding an optimal set of parameter values for a learning algorithm.

Usually, the process of choosing these values is a time-consuming task.

Even for simple algorithms like Linear Regression, finding the best set for the hyper-parameters can be tough. With Deep Learning, things get even worse.

Some of the parameters to tune when optimizing neural nets (NNs) include:

• learning rate

• momentum

• regularization

• dropout probability

• batch normalization

In this short piece, we talk about the best practices for optimizing ML models. These practices come in hand mainly when the number of parameters to tune exceeds two or three.

**Machine Learning at Enterprise Scale**

Read this ebook to learn from real-world data science practitioners, who present their unique perspectives and advice on handling six common problems that include:

• Reconciling disparate interfaces

• Resolving environment dependencies

• Ensuring close collaboration among all ML stakeholders

• Building or renting adequate ML infrastructure

• Meeting the scalability needs of your application

• Enabling smooth deployment of ML projects

Download this free ebook today to learn more!

**Machine Learning and Data Science Applications in Industry**

**Top 10 Best Deep Learning Frameworks in 2019**

1. TensorFlow

2. PyTorch

3. Sonnet

4. Keras

5. MXNet

6. Gluon

7. Swift

8. Chainer

9. DL4J

10. ONNX

**The origins of bias and how AI may be the answer to ending its reign**

**Boosting, Bagging and Stacking – A Comparative Analysis (2019 India Elections Case Study)**

**The Problem Of Overfitting And How To Resolve It**

This article aims to explain following topics:

• What Is Overfitting In A Machine Learning Project?

• How Can We Detect Overfitting?

• How Do We Resolve Overfitting?