Sigmoid

Binary classification NN is used with the sigmoid activation function on its final layer together with BCE loss. The final layer size should be 1.

Binary Classification: Understanding Activation and Loss Functions with a PyTorch Example

The brain of a human child is spectacularly amazing. Even in any previously unknown situation, the brain makes a decision based on its primal knowledge. Depending on the outcome, it learns and remembers the most optimal choices to be taken in that particular scenario. On a high level, this process of learning can be understood as a ’trial and error’ process, where the brain tries to maximise the occurrence of positive outcomes.

Reinforcement Learning: A Brief Introduction to Rules and Applications

In this post, we will talk about the Universal approximation theorem and we will also prove the theorem graphically. This is a follow-up post of my previous post on <a href="https://towardsdatascience.com/sigmoid-neuron-deep-neural-networks-a4cd35b629d7" target="_blank">Sigmoid Neuron</a>.

Illustrative Proof of Universal Approximation Theorem

Animate your web applications with a customizable ease-out.

Ease Out: The Half Sigmoid

It’s very common for animations to be specified as ease-in-out. It’s a very pleasing sensation to witness an object speed up, cruise, and slow to a halt. Most easings specify one of a small number of easing curves: easeInOutQuad, easeInOutSine, easeInOutCubic, etc. However, the sharpness of that curve is not configurable. Here I show how to create a configurable ease-in-out function that will work for animating any property you desire.

Ease In, Out: The Sigmoid Factory

A 12B parameter transformer trained on 250M image-text pairs generates high-quality, zero-shot images and emerging image-to-image capabilities.

DALL·E Research Paper: High Quality Image Generation Without Using Any of the Training Labels

Compute gradients in TensorFlow with GradientTape—watching, models, non-scalars, control flow, and performance tips, all with clear, runnable examples.

Why GradientTape Is the Most Underrated Feature in TensorFlow

Exploring Brain-like Dragon Hatchling (BDH) — a new AI model that learns on the fly, adapts like a brain, and challenges the transformer era.

The Dragon Hatchling Learns to Fly: Inside AI’s Next Learning Revolution

Guide explaining how to use Netron, visualkeras, and TensorBoard to visualize Keras machine learning models.

3 Ways to Easily Visualize Keras Machine Learning Models

Build real-time AI noise suppression for Android with ONNX Runtime. Complete tutorial with working code, performance tips, and 70% APK size reduction.

Mobile AI with ONNX Runtime: How to Build Real-Time Noise Suppression That Works

Explore TensorFlow NumPy: seamless interoperability, fast gradients, tf.function boosts, device control, and performance gains over classic NumPy.

Why TensorFlow NumPy Might Be the Future of Differentiable Programming

Airlines don’t just fly planes—they model your choices. This deep dive shows how layover times impact profits in codeshare agreements and passenger flows.

How Your Layover Time Can Make or Break an Airline’s Profit

Idefics2, an efficient 8B vision-language model, sets new multimodal AI benchmarks—while its release, dataset, and red-teaming highlight progress and risks.

How an 8B Open Model Sets New Standards for Safe and Efficient Vision-Language AI

Learn how to build a logistic regression model with TensorFlow Core to classify tumors using the Wisconsin Breast Cancer Dataset.

Logistic Regression for Binary Classification With Core APIs

Customize Keras model training by overriding train_step() while keeping the benefits of fit(), like callbacks and metrics.

Keep Keras fit() and Train Your Model Your Way

Learn how to customize saving and loading in Keras using advanced methods like save_own_variables(), load_assets(), and compile_from_config().

Learn How to Customize Every Step of Model Saving in Keras

Learn to build, train, and save flexible deep learning models using the Keras Functional API. Ideal for non-linear, multi-input, and complex architectures.

How To Build a Brain With Keras and a Few Lines of Code

Learn how to compute gradients using TensorFlow's GradientTape API for training models with automatic differentiation and eager execution.

Getting Started with Gradients and Automatic Differentiation in TensorFlow

Novel recurrence gates and complex-valued units boost stability and efficiency in linear recurrent models, optimized for TPU-v3 hardware.

Overcoming HBM-VMEM Bottlenecks in TPU-v3 Recurrent Workloads

Here are some Mindeye2 references you should check out.

Mindeye2: References You Should Check Out

A review of Face Recognition loss functions, exploring advancements from ArcFace to modern adaptive and prototype-based methods for improved accuracy.

Why AI Needs to Lose (a Little) to Recognize Your Face Better

I worked on optimizing object detection for autonomous vehicles using Atrous Spatial Pyramid Pooling (ASPP) and Transfer Learning. 

How I Made Object Detection Smarter for Self-Driving Cars With Transfer Learning & ASPP

A smarter way to allocate computing resources in AI transformers is making them faster and more efficient.

This Clever AI Hack Could Cut Processing Costs in Half

Researchers have developed a new method to generate additional data points by utilizing cross-validation resampling and latent variable modeling to train AI.

How Meat Fat and Heart Data Prove AI Gets Smarter with the Right Boost

Sigmoid is a Leading Data Solutions company offering best-in-class services in Data Engineering and Data Science

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Binary Classification: Understanding Activation and Loss Functions with a PyTorch Example

Reinforcement Learning: A Brief Introduction to Rules and Applications

Illustrative Proof of Universal Approximation Theorem

Ease Out: The Half Sigmoid

Ease In, Out: The Sigmoid Factory

DALL·E Research Paper: High Quality Image Generation Without Using Any of the Training Labels

Why GradientTape Is the Most Underrated Feature in TensorFlow

The Dragon Hatchling Learns to Fly: Inside AI’s Next Learning Revolution

3 Ways to Easily Visualize Keras Machine Learning Models

Mobile AI with ONNX Runtime: How to Build Real-Time Noise Suppression That Works

Why TensorFlow NumPy Might Be the Future of Differentiable Programming

How Your Layover Time Can Make or Break an Airline’s Profit

How an 8B Open Model Sets New Standards for Safe and Efficient Vision-Language AI

Logistic Regression for Binary Classification With Core APIs

Keep Keras fit() and Train Your Model Your Way

Learn How to Customize Every Step of Model Saving in Keras

How To Build a Brain With Keras and a Few Lines of Code

Getting Started with Gradients and Automatic Differentiation in TensorFlow

Overcoming HBM-VMEM Bottlenecks in TPU-v3 Recurrent Workloads

Mindeye2: References You Should Check Out

Why AI Needs to Lose (a Little) to Recognize Your Face Better

How I Made Object Detection Smarter for Self-Driving Cars With Transfer Learning & ASPP

This Clever AI Hack Could Cut Processing Costs in Half

How Meat Fat and Heart Data Prove AI Gets Smarter with the Right Boost

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