How to make better decisions with AI simulations
Have you ever wondered what your world would be like if you could just wind back the clock, make a different decision about something you weren’t happy with and seeing how things played out? Wouldn’t it be wonderful if only we could try out different scenarios before committing to them? Well I have good news my friends, because this is an area where machine learning can really come to your aid and in this article we’ll take a look at what you can do and why it’s so useful.
Win more with eXplainable AI
AI has helped us to extract more meaningful insights from sales data, enabling us to improve the processes that drive more sales. When eXplainable AI (XAI) is added to the mix, you’ll find that suddenly you’re able to answer questions like “Why will I win this deal?” rather than “Am I going to win this deal?”.
9 Pitfalls to avoid predicting Sales Opportunities
In this post, we’ll explore some of the common challenges that can derail your efforts to predict sales opportunities accurately. Drawing from experience, I’ll highlight potential solutions to avoid these pitfalls and build a more reliable system.
Our Top 7 Forecasting Models We Benchmarked For Monash
Top 7 Forecasting Models We Benchmarked For Monash
Building an AI SaaS product on a shoestring budget with aws serverless (Part 1)
In this post we’ll show you what you need to consider when building an enterprise class AI SaaS product from someone who’s been there and done it.
Revin: What really grinds my gears
A review of Revin - Reversible Instance Normalization used in time series forecasting
NeurIPs Ariel Challenge: What's it like to take part in a Kaggle competition?
An opinion piece on participating in a Kaggle competition
Multivariate Time Series models: Do we really need them?
A comparison of local, global, univariate and multivariate configurations using the DLinear and NLinear models
LTSF-Linear: Embarrassingly simple time series forecasting models
A review of the 2022 Paper Are Transformers Effective for Time Series Forecasting that introduced DLinear and NLinear models
Pytorch vs MXNet: Which is faster?
An analysis of the computational efficiency of Pytorch and MXNet
The F1 Score: Time Series Model Championships
A ranking system of time series models based on the Monash dataset benchmarks using the mase metric and the formula 1 scoring system.
GenAI: I don't care what it is and you shouldn't either
A look at the history of AI and how the terms AI, Machine Learning, Deep Learning and Generative AI have evolved over time.
1Cycle scheduling: State of the art timeseries forecasting
How to get state of the art timeseries forecasting results using machine learning with my variant of DeepAR and 1Cycle Scheduling
Super-convergence: Supercharge your Neural Networks
A look at 1Cycle scheduling, one of my favourite techniques at improving model performance and practical guidance on how to use it
A fistful of MASE: Deconstructing DeepAR
A deep dive into the GluonTS DeepAR neural network model architecture for time series forecasting and an ablation study of the covariates.
Predicting covariates: Is it a good idea?
A study which evaluates the effectiveness of predicting covariates in LSTM Neural Networks for Time Series Forecasting
Teacher Forcing: A look at what it is and the alternatives
Review of teacher forcing, free running, scheduled-sampling, professor forcing and attention forcing for training auto-regresssive neural networks
Human-level control through deep reinforcement learning
Recreating the experiments from the classic 2015 Deepmind Paper by Mnih et al.: Human-level control through deep reinforcement learning
Revisiting Playing Atari with Deep Reinforcement Learning
Recreating the experiments from the classic DQN Deepmind paper by Mnih et al.: Playing Atari with Deep Reinforcement Learning