Master LLMs with our FREE course in collaboration with Activeloop & Intel Disruptor Initiative. Join now!

Publication

How Much Math Do I Need in Data Science?… And more!
Newsletter

How Much Math Do I Need in Data Science?… And more!

Last Updated on November 10, 2021 by Editorial Team

Author(s): Towards AI Team

 

How Much Math Do I Need in Data Science?… And more!
Source: Unsplash

📚 Editor’s choice featured articles for the month: 📚

How Much Math Do I Need in Data Science? By Benjamin O. Tayo, Ph.D.

Exploring math skills are essential in data science and machine learning. Can I become a data scientist with little or no math background? What essential math skills are important in data science?…

Generating Synthetic Sequential Data using GANs by Armando Vieira, Hazy AI

Sequential data — data that has time dependency — is very common in business, ranging from credit card transactions to medical healthcare records to stock market prices. But privacy regulations limit and dramatically slow-down access to useful data, essential to research and development. This creates a demand for highly representative, yet…

Neural Networks from Scratch with Python Code and Math in Detail by Pratik Shukla, Roberto Iriondo

Building neural networks from scratch. From the math behind them to step-by-step implementation coding samples in Python with Google Colab. Note: In our second tutorial on neural networks, we dive in-depth on the limitations and advantages of using neural networks. We show how…

Do You Understand Gradient Descent and Backpropagation? Most Don’t by Michel Kana, Ph.D.

A simple mathematical intuition behind one of the commonly used optimization algorithms in Machine Learning. Binary classification is very common in machine learning. It is a good example of getting into the dark world of gradient descent and back-propagation…

How Web Frameworks Streamline and Structure Websites by Joaquin de Castro

On MVT architecture, URL dispatching, templating, and putting that all together. Web frameworks make building and deploying web applications simple and efficient. With them, we can make high-level applications without too much technical knowledge. Nonetheless, it is still important to be able to…

The Universe of Time-series Forecasting Techniques: A Primer by Mab Alam, Ph.D.

Time-series forecasting is one of the most talked-about topics in data science. Not surprisingly, there is a rich forecasting toolbox with many different options to choose from for data scientists. The possibilities are so many that they often leave data scientists being overwhelmed…

Predict the Stock Trend Using Deep Learning by Pushkara Sharma

Predicting the upcoming trend of stock using the deep learning model (Recurrent Neural Network). In this article, we will build a deep learning model (specifically the RNN Model) that will help us to predict whether the given stock will go up or down in the future. Remember…

Machine Learning Algorithms for Beginners with Code Examples in Python by Pratik Shukla, Roberto Iriondo, and Sherwin Chen

Best machine learning algorithms for beginners with coding samples in Python. Launch the coding samples with Google Colab. Machine learning (ML) is rapidly changing the world, from diverse types of applications and research pursued in industry and academia…

Thank you for being a subscriber with Towards AI!

🙏 Follow Towards AI on social media ↓ 🙏

Facebook | Twitter | Instagram | LinkedIn | Google News | Flipboard | Mobile Feed

📈 Amazon Web Services consultants, engineers, and practitioners make $ 100.00–250.00+ per hour. Most companies use cloud computing for better security, low costs, speed, and unlimited storage. Learn from the expert, Daniel Vassallo, ex-Amazon, and learn all of his secrets on his AWS book — The Good Parts of AWS. 📈


How Much Math Do I Need in Data Science?… And more! was originally published in Towards AI — Multidisciplinary Science Journal on Medium, where people are continuing the conversation by highlighting and responding to this story.

Published via Towards AI

Feedback ↓