James Le

James Le is a Data Scientist, with a focus on Deep Learning & Recommendation Systems, and a Data Journalist with over 1 Million Views. Contact him on LinkedIn.

8 Useful Advices for Aspiring Data Scientists

By James Le, Machine Learning Engineer on May 4, 2018 in Advice, Claudia Perlich, Daniel Tunkelang, Data Science, Data Scientist, Jake Porway, Yann LeCun
I recently read Sebastian Gutierrez’s “Data Scientists at Work”, in which he interviewed 16 data scientists. I want to share the best answers that these data scientists gave for the question: "What advice would you give to someone starting out in data science?"
12 Useful Things to Know About Machine Learning

By James Le, Machine Learning Engineer on April 12, 2018 in Causation, Correlation, Feature Engineering, High-dimensional, Machine Learning, Overfitting, Pedro Domingos
This is a summary of 12 key lessons that machine learning researchers and practitioners have learned include pitfalls to avoid, important issues to focus on and answers to common questions.
The 8 Neural Network Architectures Machine Learning Researchers Need to Learn

By James Le, Machine Learning Engineer on January 31, 2018 in Architecture, Deep Learning, Machine Learning, Neural Networks
In this blog post, I want to share the 8 neural network architectures from the course that I believe any machine learning researchers should be familiar with to advance their work.
The 10 Deep Learning Methods AI Practitioners Need to Apply

By James Le, Machine Learning Engineer on December 13, 2017 in Backpropagation, Convolutional Neural Networks, Deep Learning, Dropout, Gradient Descent, LSTM, Neural Networks, Transfer Learning
Deep learning emerged from that decade’s explosive computational growth as a serious contender in the field, winning many important machine learning competitions. The interest has not cooled as of 2017; today, we see deep learning mentioned in every corner of machine learning.
The 10 Statistical Techniques Data Scientists Need to Master

By James Le, Machine Learning Engineer on November 15, 2017 in Algorithms, Data Science, Data Scientist, Machine Learning, Statistical Learning, Statistics
The author presents 10 statistical techniques which a data scientist needs to master. Build up your toolbox of data science tools by having a look at this great overview post.
The 10 Algorithms Machine Learning Engineers Need to Know

By James Le, Machine Learning Engineer on August 18, 2016 in Algorithms, Machine Learning, Supervised Learning, Unsupervised Learning
Read this introductory list of contemporary machine learning algorithms of importance that every engineer should understand.

James Le

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