Read to understand how Reinforcement learning is influenced by human learning.
What will you learn here?
- What is the difference between Supervised Learning, Unsupervised Learning, and Reinforcement Learning?
- Understand how Reinforcement Learning mimics human behavior,
- Different components of Reinforcement Learning(RL) and how they interact
- Applications of Reinforcement Learning(RL) in real-world scenarios.
This article is adapted and inspired from Reinforcement Learning: An Introduction by Richard S. Sutton and Andrew G. Barto.
What is the difference between Supervised Learning, Unsupervised Learning, and Reinforcement Learning?
Supervised Learning
Supervised Learning algorithms learn from a labeled dataset. The labels in the dataset provide the answer to the input data.
The Supervised algorithm's objective is to find the function(f)…
Train a CNN on MNIST Dataset using Keras and PyTorch
Here you will learn to create a CNN model with similar architecture using Keras and PyTorch on MNIST dataset
Features of Keras and PyTorch.
Keras
- Keras is a simpler, concise deep learning API written in Python that runs on TensorFlow's machine learning platform.
- It enables fast experimentation.
- Keras provides abstractions and building blocks for developing deep learning models.
- The model built using Keras is more readable, and skips the neural network implementation details.
- Keras can run on TPU or large clusters of GPUs.
- It implicitly performs computation on GPU.
- Supported by Google
PyTorch
- PyTorch is a lower-level API focused to directly work with array expressions
- PyTorch has…
A Novel Loss to address Class Imbalance for the Object detection
You will learn about Focal loss, how it is used in Object detection to detect hard negative examples, and then implement Focal loss for an imbalanced dataset.
Focal loss is based on the premise where the training focuses on a sparse set of hard examples and prevents the vast number of easy negatives from overwhelming the object detector during training.
Types of Object Detectors
One-stage detectors are a straightforward, efficient, and elegant architecture; hence they are faster and simpler. The outputs of the object detection network are classification probabilities and box offsets at each spatial position.
Example: YOLO, SSD, RetinaNet, CenterNet, CenterNet, etc.
Two-stage…
Deep Learning
Explore DropBlock, a new Regularization technique for Convolutional Neural Networks
Here we will explore
- A regularization technique and the different regularization techniques like L1, L2 regularization, Dropout, and Spatial Dropout.
- What is DropBlock, and how is it different than Dropout
- Compare the Dropout and DropBlock results on the CIFAR-10 dataset
Regularization is a strategy implemented in a deep neural network that will reduce the generalization error but not the training error to perform well on not just the training data but also on new unseen inputs.
An effective regularizer reduces the variance significantly while not overly increasing the bias, thus preventing overfitting.
We use regularization techniques like L1 and L2…
Computer Science
An optimization algorithm inspired by natural evolution such as selection, mutation, and cross-over
Learn about the Genetic Algorithm, what role it plays in artificial intelligence, how it works, and finally, look at an implementation.
The Genetic Algorithm(GA) is an evolutionary algorithm(EA) inspired by Charles Darwin’s theory of natural selection which espouses Survival of the fittest.
As per the natural selection theory, the fittest individuals are selected to produce offsprings. The fittest parents' characteristics are then passed on to their offsprings using cross-over and mutation to ensure better chances of survival.
Genetic algorithms are randomized search algorithms that generate high-quality optimization solutions by imitating the biologically inspired natural selection process such as selection, cross-over…
A quick snapshot of new and popular activation functions used in deep neural networks
Understand popular activation functions used in deep neural networks: Sigmoid, Softmax, tanh, ReLU, Softplus, PReLU, ReLU6, ELU, SELU, Swish, and Mish
Working of a Deep Neural Network
A deep neural network performs a linear transformation(z) using the node's input value(x), weight(w), and bias(b).
Learn Generalized IoU, Distance IoU, and Complete IoU Loss used in State of the art object detection algorithms
Object detection, which includes two sub-tasks: object classification and object localization.
Object Localization relies on a bounding box regression (BBR) module to localize objects.
Bounding Box Regression
Bounding-box regression is a popular technique in object detection algorithm used to predict target objects' location using rectangular bounding boxes. It aims to refine the location of a predicted bounding box.
Bounding box regression uses overlap area between the predicted bounding box and the ground truth bounding box referred to as Intersection over Union (IOU) based losses.
Intersection over Union
IoU loss only works when the predicted bounding boxes overlap with the ground truth box. …
Distilling knowledge from a Teacher to a Student in a Deep Neural Network
In this article, you will learn.
- An easy to understand explanation of Teacher-Student knowledge distillation neural networks
- Benefits of Knowledge distillation
- Implementation of Knowledge distillation on the CIFAR-10 dataset
Overview of Knowledge Distillation
Large deep neural networks or an ensemble of deep neural networks built for better accuracy are computationally expensive, resulting in longer inference times which may not be ideal for near-real-time inference required at the Edge.
The training requirements for a model are different from requirements at the inference. …
A simplistic explanation of what is Blockchain and how it works
You are in a grocery store and scan the QR code on your favorite coffee. You can view the details of where the coffee beans grew, cleaned, triaged, processed, and then roasted. what-if you can trace the supply and distribution to know the means of transport and how long it took to arrive at the store and so forth
Is there a technology that can help record information at each step and make that information available to everyone transparently?
Welcome to the world of Blockchain
Blockchain is an immutable, distributed, decentralized; peer-to-peer ledger replicated across multiple nodes connected in a…
A Stochastic Optimization Technique Inspired by Nature
Particle Swarm Optimization(PSO) is a meta-heuristic stochastic nonlinear optimization algorithm proposed by Kennedy and Eberhart in 1995 to solve computationally challenging optimization problems.
Inspiration
Particle Swarm Optimization(PSO) is inspired by nature. Flocks of bird and fish schools adjust their physical movement to avoid predators, seek food or mates, or optimize environmental parameters such as temperature.
Birds and fishes use swarm intelligence, social behavior, and movement dynamics to share information among their group for a greater survival advantage.
Objective
The PSO optimization objective is to find the optimum function that either maximizes the accuracy or precision or minimizes the loss.
The objective of PSO is to find a global optimum over a search space. A swarm in the algorithm is represented by a set of particles…
Renu Khandelwal
Loves learning, sharing, and discovering myself. Passionate about Machine Learning and Deep Learning
