Understanding LeNet: A Detailed Walkthrough

Table of Contents

  1. An Overview of LeNet
  2. Convolutional Neural Network Basics
  3. A Walkthrough of LeNet-1’s Architecture
  4. A Walkthrough of LeNet-4’s Architecture
  5. A Walkthrough of LeNet-5’s Architecture
  6. Analysis of LeNet
  7. Summary of LeNet

1. An Overview of LeNet

LeNet was a group of Convolutional Neural Networks (CNNs) developed by Yann Le-Cun and others in the late 1990s. The networks were broadly considered as the first set of true convolutional neural networks. They were capable of classifying small single-channel (black and white) images, with promising results. LeNet consisted of three distinct networks, and they were:

  1. LeNet-1 (five layers): A simple CNN.
  2. LeNet-4 (six layers): An improvement over LeNet-1.
  3. LeNet-5 (seven layers): An improvement over LeNet-4 and the most popular.

#computer-vision #lenet #supervised-learning #data-science #machine-learning

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Understanding LeNet:  A Detailed Walkthrough

Understanding LeNet: A Detailed Walkthrough

Table of Contents

  1. An Overview of LeNet
  2. Convolutional Neural Network Basics
  3. A Walkthrough of LeNet-1’s Architecture
  4. A Walkthrough of LeNet-4’s Architecture
  5. A Walkthrough of LeNet-5’s Architecture
  6. Analysis of LeNet
  7. Summary of LeNet

1. An Overview of LeNet

LeNet was a group of Convolutional Neural Networks (CNNs) developed by Yann Le-Cun and others in the late 1990s. The networks were broadly considered as the first set of true convolutional neural networks. They were capable of classifying small single-channel (black and white) images, with promising results. LeNet consisted of three distinct networks, and they were:

  1. LeNet-1 (five layers): A simple CNN.
  2. LeNet-4 (six layers): An improvement over LeNet-1.
  3. LeNet-5 (seven layers): An improvement over LeNet-4 and the most popular.

#computer-vision #lenet #supervised-learning #data-science #machine-learning

Guide to Understanding Generics in Java

Introduction

Java is a type-safe programming language. Type safety ensures a layer of validity and robustness in a programming language. It is a key part of Java’s security to ensure that operations done on an object are only performed if the type of the object supports it.

Type safety dramatically reduces the number of programming errors that might occur during runtime, involving all kinds of errors linked to type mismatches. Instead, these types of errors are caught during compile-time which is much better than catching errors during runtime, allowing developers to have less unexpected and unplanned trips to the good old debugger.

Type safety is also interchangeably called strong typing.

Java Generics is a solution designed to reinforce the type safety that Java was designed to have. Generics allow types to be parameterized onto methods and classes and introduces a new layer of abstraction for formal parameters. This will be explained in detail later on.

There are many advantages of using generics in Java. Implementing generics into your code can greatly improve its overall quality by preventing unprecedented runtime errors involving data types and typecasting.

This guide will demonstrate the declaration, implementation, use-cases, and benefits of generics in Java.

#java #guide to understanding generics in java #generics #generics in java #guide to understanding generics in java

Understanding AlexNet: A Detailed Walkthrough

Table of Contents

  1. Introduction to AlexNet
  2. Walkthrough of AlexNet’s Architecture
  3. Training of AlexNet
  4. Summary of AlexNet

1. Introduction to AlexNet

AlexNet was a deep neural network that was developed by Alex Krizhevsky and others in 2012. It was designed to classify images for the ImageNet LSVRC-2010 competition, where it achieved state of the art results [1]. It also worked with multiple GPUs. The AlexNet model contained 11 layers, they were:

  1. Layer C1: Convolution Layer (96, 11×11)
  2. Layer S2: Max Pooling Layer (3×3)
  3. Layer C3: Convolution Layer (256, 5×5)
  4. Layer S4: Max Pooling Layer (3×3)
  5. Layer C5: Convolution Layer (384, 3×3)
  6. Layer C6: Convolution Layer (384, 3×3)
  7. Layer C7: Convolution Layer (256, 3×3)
  8. Layer S8: Max Pooling Layer (3×3)
  9. Layer F9: Fully-Connected Layer (4096)
  10. Layer F10: Fully-Connected Layer (4096)
  11. Layer F11: Fully-Connected Layer (1000)

#data-science #artificial-intelligence #machine-learning #convolutional-network

Queenie  Davis

Queenie Davis

1621376340

Understand Artificial Intelligence (AI)

You were dreaming about understanding AI and machine learning? Well, this article is made for you. We are going to demystify AI.

What is machine learning?

Before we go further in this article, we do need to define what is machine learning.

To summarize, machine learning is the fact of solving a problem without telling a computer how to solve it. What I mean by that is that in classic programming you would write code to explain to the computer how to solve a problem and explain to him what are the _different step_s to do it. With machine learning, the computer is using statistical algorithms to solve a problem by itself thanks to input data. It does this, by finding the patterns between the input and the output of the problem.

What do I need to do to do machine learning?

To do machine learning, you will need data, a lot of data.

When you do have these data, what you will need to do is to split your data into two datasets:

  • The test dataset: the data you will use for testing if your machine learning model (algorithm) is properly working.
  • The** training dataset:** the data you will use for training your machine learning model.

So remember, data is key, you need to have a proper amount of data, and clean these data (we will talk about how to clean your data for your dataset in another article).

What can I use machine learning for?

You can actually use machine learning for solving a lot of problems. Here are a few examples:

  • Recommendations of products on e-commerce website (Amazon, eBay, …).
  • Recommendations for a search engine website (Google, Facebook search, …).
  • Netflix uses it as well to recommend movies and TV series depending on what you actually like.
  • Youtube to put the subtitles under your videos, …

How can I teach machines to learn?

There are different ways for machines to learn, here are the four most popular ways:

  • Supervised learning: your model will learn thanks to input labeled data that you provide to it (your data are already tagged with the correct labels). Which means that we show the correct answers to the machine. It can be used for classifying data, for example, classify cats by breeds.
  • Unsupervised learning: your model will learn by observing. Which means that it will learn and improve by trial and error. In that case, we are not working with labeled data, so we don’t show the machine the correct answer. It can be used for clustering data, for example, group the loyal customers.
  • Semi-supervised learning: your model starts with a small dataset and applies supervised learning (labeled data). Then we will feed the rest of the data to our model and observe them by applying unsupervised learning (non-labeled data). This will allow the computer to expand its vocabulary based on what it learned and classified during the supervised learning stage.
  • Reinforcement learning: we train our model by rewarding it every time it has the correct output. Then the computer will try to get as many rewards as possible and will learn by itself. It can be used to create an AI for video games.

#data-science #supervised-learning #understand #artificial #intelligence #ai

John Garcia

John Garcia

1623438000

How to Stake Injective Protocol Step $INJ Tokens (Crypto Walkthrough Guide 2021)

Staking altcoins can be a great way to earn passive income on your long term holdings that are just sitting in your wallet. That is why today I will be walking you through step by step how to stake you $INJ tokens on Injective Protocol’s staking portal, we even go through how to buy the token on both Uniswap and Pancakeswap so there is no misunderstanding.

After watching this video do you understand how to stake your $INJ tokens?
📺 The video in this post was made by Invest Global
The origin of the article: https://www.youtube.com/watch?v=qeCQ9BKzsTA
🔺 DISCLAIMER: The article is for information sharing. The content of this video is solely the opinions of the speaker who is not a licensed financial advisor or registered investment advisor. Not investment advice or legal advice.
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