Applying machine learning to Laravel applications

•

5 likes•4,006 views

The document discusses applying machine learning in Laravel applications. It provides an introduction to machine learning, including definitions and examples. It then debunks common myths about machine learning and outlines different types of machine learning like supervised and unsupervised learning. The document lists applications of machine learning in fields like health, education, and traffic control. It also discusses popular machine learning APIs that can be integrated with Laravel applications, like TensorFlow and Scikit-learn. Finally, it describes a demo Laravel baby monitoring application that uses facial recognition via the Kairos API machine learning model.

Technology

by Oreoluwa Ogundipe
Applying Machine Learning in Laravel Applications

Speaker Bio
- Computer Vision/Machine Learning/Web Developer.
- Computer Science Student at University of Ghana.
- San Diego State University Alumni.
- Fomer Master Builder at BuildIT, San Diego State
University.
- Developer at hotels.ng.
- VSCode Advocate.
- Gamer.

What is Machine Learning?
- Machine Learning is the ability of a computer to predict the future based on past
experiences.
Examples
- Netflix Movie Prediction.
- Breast Cancer Prediction.
- Predicting Prices of Houses in Lekki.

Debunking Machine Learning Myths
- Machine Learning is difficult to understand.
- You must be a ‘bad’ mathematician to understand it.
- You need a laptop with maxed out specifications to run your application.
- Machine Learning is absent of human bias.

Types of Learning
-Supervised Learning.
- Linear Regression, Logistic Regression, Neural Networks
-Unsupervised Learning.
- SVMs, KMeans
-Reinforcement Learning.

Applications of Machine Learning
-Machine Learning in Health Sector.
-Machine Learning in Education.
-Machine Learning in Traffic Control.
- Machine Learning in Stock Market.

Machine Learning APIs
- Tensorflow by Google
- Scikit learn by Google.
- Clarifai AI.
- Microsoft Facial Detection API
- Kairos API.

BABEWATCH
- This is a Laravel Application I made for demo
purpose during this event.
HOW IT WORKS
- When a user signs their baby up, the user’s picture
and pictures of other authorized baby carriers are
taken.
- Users are being verified by the Machine Learning
Model provided by ‘Kairos API’.

@orehga
Thank you for listening !"
Any questions?
@oreeog
@ore.og

What's hot

Artificial intelligence - AI Complete Concept

Dr. Abdul Ahad Abro

Landscape of AI/ML in 2023

HyunJoon Jung

Introduction to AI/ML with AWS

Suman Debnath

ChatGPT_Prompts.pptx

Chakrit Phain

Machine Learning

Sajitha Burvin

Dive into the world of GPT-4, the state-of-the-art AI language model by OpenAI. Learn how to craft effective prompts and unlock the full potential of GPT-4 for a wide range of applications, including content generation. Keywords: GPT-4, OpenAI, artificial intelligence, language model, prompting, content generation, machine learning, natural language processing, NLP, deep learning, tokenization, context window, prompt engineering, reinforcement learning, fine-tuning, response quality, API, zero-shot learning, few-shot learning, AI ethics, use cases, best practices, performance optimization, transformer architecture, AI-powered solutions.

Prompting is an art / Sztuka promptowania

Michal Jaskolski

Choose our Artificial Intelligence Machine Learning Deep Learning PPT PowerPoint Presentation Slide Templates to understand this popular branch of computer science. Acquaint your audience with the process of building smart, capable machines that can perform intelligent tasks with the help of this neural network PPT presentation. Exhibit the difference between AI, machine learning, and deep learning through this informative robotics PPT design. Elaborate on the wide range of areas that can benefit from artificial intelligence like supply chain, customer experience, human resources, fraud detection, research, and development by taking the aid of this computer science PPT slideshow. Highlight the booming rate of AI business and its future revenue forecast by downloading this thought-provoking and indulging information technology PowerPoint graphics. Save your time and efforts with these pre-ready and professionally crafted content-specific slides. It will educate your audience about this complex process in an easy yet efficient way. Download this AI functioning PowerPoint deck to create a roadmap for the growth and expansion of your business. https://bit.ly/3x135nD

Artificial Intelligence Machine Learning Deep Learning Ppt Powerpoint Present...

SlideTeam

LLMs_talk_March23.pdf

ChaoYang81

Explainable AI (XAI) - A Perspective

Saurabh Kaushik

AI as a general-purpose technology akin to steam engines and electricity, holds the potential for profound global socio-economic change. In this talk, we delve into a new form of disruptive AI known as Generative AI (GenAI) and its revolutionary impact on how we live, work, and interact with our environment. This discussion will cover GenAI’s arrival, capability and its impact. We will also discuss the challenges and opportunities that GenAI presents to industry leaders and practitioners including the defence sector. We'll explore its potential to reshape industries, push creative boundaries, and expand consolidated knowledge -- GenAI has become the cornerstone upon which new platforms, companies, and industries are built.

Generative AI: Shifting the AI Landscape

Deakin University

Machine Learning Training with Python: https://www.edureka.co/python ) This Edureka Machine Learning tutorial (Machine Learning Tutorial with Python Blog: https://goo.gl/fe7ykh ) on "AI vs Machine Learning vs Deep Learning" talks about the differences and relationship between AL, Machine Learning and Deep Learning. Below are the topics covered in this tutorial: 1. AI vs Machine Learning vs Deep Learning 2. What is Artificial Intelligence? 3. Example of Artificial Intelligence 4. What is Machine Learning? 5. Example of Machine Learning 6. What is Deep Learning? 7. Example of Deep Learning 8. Machine Learning vs Deep Learning Machine Learning Tutorial Playlist: https://goo.gl/UxjTxm

AI vs Machine Learning vs Deep Learning | Machine Learning Training with Pyth...

Edureka!

Artificial Intelligence is increasingly playing an integral role in determining our day-to-day experiences. Moreover, with proliferation of AI based solutions in areas such as hiring, lending, criminal justice, healthcare, and education, the resulting personal and professional implications of AI are far-reaching. The dominant role played by AI models in these domains has led to a growing concern regarding potential bias in these models, and a demand for model transparency and interpretability. In addition, model explainability is a prerequisite for building trust and adoption of AI systems in high stakes domains requiring reliability and safety such as healthcare and automated transportation, and critical industrial applications with significant economic implications such as predictive maintenance, exploration of natural resources, and climate change modeling. As a consequence, AI researchers and practitioners have focused their attention on explainable AI to help them better trust and understand models at scale. The challenges for the research community include (i) defining model explainability, (ii) formulating explainability tasks for understanding model behavior and developing solutions for these tasks, and finally (iii) designing measures for evaluating the performance of models in explainability tasks. In this tutorial, we present an overview of model interpretability and explainability in AI, key regulations / laws, and techniques / tools for providing explainability as part of AI/ML systems. Then, we focus on the application of explainability techniques in industry, wherein we present practical challenges / guidelines for effectively using explainability techniques and lessons learned from deploying explainable models for several web-scale machine learning and data mining applications. We present case studies across different companies, spanning application domains such as search & recommendation systems, sales, lending, and fraud detection. Finally, based on our experiences in industry, we identify open problems and research directions for the data mining / machine learning community.

Explainable AI in Industry (AAAI 2020 Tutorial)

Krishnaram Kenthapadi

Adversarial Attacks and Defense

Kishor Datta Gupta

Gain insights into the practical applications of ChatGPT, Bard, and other AI-based technological advancements, including GitHub CoPilot and Applitools Self-Healing Cloud, in this session with Anand Bagmar. Through specific use cases, Anand demonstrates how to enhance test automation processes—making them faster, more stable, and easier to implement. Session recording and more info at applitools.com Uncover how these tools can revolutionize your testing strategies and stay ahead of the curve in the ever-evolving world of test automation.

Unlocking the Power of ChatGPT and AI in Testing - NextSteps, presented by Ap...

Applitools

Google io event presentation

Nitin Verma [nitin.verma@dbydx.com]

Speaker: Jim Spohrer Date: Tuesday April 18, 2023 Place: UC Santa Cruz - Silicon Valley Title: "Generative AI and Design: From Present Practice to Future Vision” Abstract: "AI upskilling is a top priority for everyone who wishes to improve their productivity and creativity. I will share some simple examples of how I use generative AI tools today in my work; as well as who I follow to learn more advanced tricks. Despite today's many limitations, AI tool capabilities will continue to improve rapidly (including a new explosion of smartphones apps), so it is important to understand how AI may disrupt work, especially creative work (see for example this Harvard Business Review post - https://hbr.org/2023/04/how-generative-ai-could-disrupt-creative-work). With the larger goal of humanity-centered design (beyond human-centered design), all responsible actors can learn to invest systematically and wisely in becoming better future versions of themselves, with improved win-win interaction and change processes that maximize benefits and minimize harms to diverse stakeholders. In conclusion, I will share the X+AI vision (described in my co-authored book 'Service in the AI Era: Science, Logic, and Architecture Perspectives'), which describes a world design where we all possess a digital twin of ourselves - our trusted cognitive mediators."

Spohrer UCSC-SV 20230418 v14.pptx

ISSIP

Fine tuning large LMs

SylvainGugger

Intro to LLMs

Loic Merckel

Introduction to AI

Sathittham Sangthong

Artificial Intelligence is increasingly playing an integral role in determining our day-to-day experiences. Moreover, with proliferation of AI based solutions in areas such as hiring, lending, criminal justice, healthcare, and education, the resulting personal and professional implications of AI are far-reaching. The dominant role played by AI models in these domains has led to a growing concern regarding potential bias in these models, and a demand for model transparency and interpretability. In addition, model explainability is a prerequisite for building trust and adoption of AI systems in high stakes domains requiring reliability and safety such as healthcare and automated transportation, as well as critical industrial applications with significant economic implications such as predictive maintenance, exploration of natural resources, and climate change modeling. As a consequence, AI researchers and practitioners have focused their attention on explainable AI to help them better trust and understand models at scale. The challenges for the research community include (i) defining model explainability, (ii) formulating explainability tasks for understanding model behavior and developing solutions for these tasks, and finally (iii) designing measures for evaluating the performance of models in explainability tasks. In this tutorial, we will first motivate the need for model interpretability and explainability in AI from societal, legal, customer/end-user, and model developer perspectives. [Note: Due to time constraints, we will not focus on techniques/tools for providing explainability as part of AI/ML systems.] Then, we will focus on the real-world application of explainability techniques in industry, wherein we present practical challenges / implications for using explainability techniques effectively and lessons learned from deploying explainable models for several web-scale machine learning and data mining applications. We will present case studies across different companies, spanning application domains such as search and recommendation systems, sales, lending, and fraud detection. Finally, based on our experiences in industry, we will identify open problems and research directions for the research community.

Explainable AI in Industry (FAT* 2020 Tutorial)

Krishnaram Kenthapadi

What's hot (20)

Artificial intelligence - AI Complete Concept

Landscape of AI/ML in 2023

Introduction to AI/ML with AWS

ChatGPT_Prompts.pptx

Machine Learning

Prompting is an art / Sztuka promptowania

Artificial Intelligence Machine Learning Deep Learning Ppt Powerpoint Present...

LLMs_talk_March23.pdf

Explainable AI (XAI) - A Perspective

Generative AI: Shifting the AI Landscape

AI vs Machine Learning vs Deep Learning | Machine Learning Training with Pyth...

Explainable AI in Industry (AAAI 2020 Tutorial)

Adversarial Attacks and Defense

Unlocking the Power of ChatGPT and AI in Testing - NextSteps, presented by Ap...

Google io event presentation

Spohrer UCSC-SV 20230418 v14.pptx

Fine tuning large LMs

Intro to LLMs

Introduction to AI

Explainable AI in Industry (FAT* 2020 Tutorial)

Similar to Applying machine learning to Laravel applications

Advantages of Artificial Intelligence - Avantika University

Avantika University

Emerging Tech webinar

rkpv2002

Area's of Artificial Inteligence .pptx

JIMSVKII

Top Technologies to Learn in 2019 - Avantika University

Avantika University

Two Day Integrated Workshop on “AI and Machine Learning for Business Transformation” It is essential for the success of every organization to stay ahead of the rapidly changing market by having access to reliable and trustworthy information. Across the AI world, AI challenges are being converted by innovators into tomorrow's business opportuni􀀉es. Success in this environment will depend on the speed of analysis and decision making, addressing customer needs upskilling workforces and ge􀀡ng the right person to work on the right opportunity. This one-of-a-kind integrated AI workshop enables you to dive deep into new depths of understanding and insights into how AI is transforming work in organiza􀀉ons. This workshop will unlock the right Data-driven AI techniques and opportunities for project implementation in your organization that will set your business apart.

Program Link to Workshop AI.pdf

HIMADRI BANERJI

AI and Machine Learning In Cybersecurity | A Saviour or Enemy?

SahilRao25

Brochure for 2022 Webinar Dr.Himadri AI.pdf

HIMADRI BANERJI

AI INTRODUCTION.pptx,INFORMATION TECHNOLOGY

santoshverma90

Rise of the Mobile Apps and AI #COMIT2018

Comit Projects Ltd

SkillsFuture Festival at NUS 2019- Artificial Intelligence for Everyone - A P...

NUS-ISS

Cognitive Automation - Your AI Coworker

Tamilselvan Subramanian

Offshore Ruby on Rails Development and Bacancy Technology – A Match Made in H...

Katy Slemon

Machine Learning for the Automotive World

Jeff Rehm

Machine learning app development

ShiraCox1

“Making Artificial Intelligence (AI) Work In Managing Change ” is a Capability Improvement webinar which has been stitched together by a two member team comprising of a Senior CEO level Oil Gas and Energy Sector business executive who in his professional career spanning over decades has led multiple projects in digital transformation in decision support systems in top organizations in the country and abroad in the contexts of competion and uncertainty and an a senior IT expert who has hands on experience in designing the configurations of hardware and the associated software in Digital Transformations in various areas including Data Analysis, Machine Learning, Deep Learning, Neural Networks for implementing state of the art Artificial Intelligence Systems in the Silicon Valley.

Making Artificial Intelligence Work in Managing Change

HIMADRI BANERJI

"MyLife".A New Era of the UI for the Smartphone

典玄三田

MyLife with english

Mita Norihiro

Machine Learning Fundamentals.docx

HaritvKrishnagiri

Merits and Demerits of AI -

HarshGajraj

Presenters: Bhaskaran Srinivasan, Senior Strategy Consultant Ashish Gupta, Senior Product Manager, Google Abstract: This workshop is designed to introduce participants to the opportunities that Generative AI offers through the process steps of a standard NPI. The program provides insights into the capabilities and limitations of Generative AI, offering a hands-on exploration of Gen AI tools tailored for product managers. Attendees will learn how to seamlessly integrate Generative AI into their daily product management workflows, identifying opportunities and prioritizing them based on impact and feasibility. The workshop introduces a robust framework for developing Generative AI-powered products, taking into account crucial factors such as customer pain points, market segment, data and algorithm biases, transparency, user control, and privacy. To enhance the learning experience, the workshop incorporates interactive talks, case study coverage, and group-based hands-on exercises. Geared towards mid-level product managers with a foundational understanding of product management best practices, the workshop is facilitated by two seasoned speakers with expertise in product innovation.

Building Products That Think- Bhaskaran Srinivasan & Ashish Gupta

ISPMAIndia

Similar to Applying machine learning to Laravel applications (20)

Advantages of Artificial Intelligence - Avantika University

Emerging Tech webinar

Area's of Artificial Inteligence .pptx

Top Technologies to Learn in 2019 - Avantika University

Program Link to Workshop AI.pdf

AI and Machine Learning In Cybersecurity | A Saviour or Enemy?

Brochure for 2022 Webinar Dr.Himadri AI.pdf

AI INTRODUCTION.pptx,INFORMATION TECHNOLOGY

Rise of the Mobile Apps and AI #COMIT2018

SkillsFuture Festival at NUS 2019- Artificial Intelligence for Everyone - A P...

Cognitive Automation - Your AI Coworker

Offshore Ruby on Rails Development and Bacancy Technology – A Match Made in H...

Machine Learning for the Automotive World

Machine learning app development

Making Artificial Intelligence Work in Managing Change

"MyLife".A New Era of the UI for the Smartphone

MyLife with english

Machine Learning Fundamentals.docx

Merits and Demerits of AI -

Building Products That Think- Bhaskaran Srinivasan & Ashish Gupta

Recently uploaded

From Daily Decisions to Bottom Line: Connecting Product Work to Revenue by VP...

Product School

The field of Information retrieval (IR) is currently undergoing a transformative shift, at least partly due to the emerging applications of generative AI to information access. In this talk, we will deliberate on the sociotechnical implications of generative AI for information access. We will argue that there is both a critical necessity and an exciting opportunity for the IR community to re-center our research agendas on societal needs while dismantling the artificial separation between the work on fairness, accountability, transparency, and ethics in IR and the rest of IR research. Instead of adopting a reactionary strategy of trying to mitigate potential social harms from emerging technologies, the community should aim to proactively set the research agenda for the kinds of systems we should build inspired by diverse explicitly stated sociotechnical imaginaries. The sociotechnical imaginaries that underpin the design and development of information access technologies needs to be explicitly articulated, and we need to develop theories of change in context of these diverse perspectives. Our guiding future imaginaries must be informed by other academic fields, such as democratic theory and critical theory, and should be co-developed with social science scholars, legal scholars, civil rights and social justice activists, and artists, among others.

Search and Society: Reimagining Information Access for Radical Futures

Bhaskar Mitra

AI for Every Business: Unlocking Your Product's Universal Potential by VP of ...

Product School

IOS-PENTESTING-BEGINNERS-PRACTICAL-GUIDE-.pptx

Abida Shariff

Discover the essentials of performance testing in the IT sector with our concise guide. Learn about various testing types such as load, stress, endurance, spike, scalability, and volume testing. Understand key performance metrics like response time, throughput, CPU and memory utilization, and error rate. Explore top tools like Apache JMeter, LoadRunner, Gatling, Neoload, and BlazeMeter. Gain insights into best practices for defining objectives, creating realistic scenarios, automating tests, and optimizing performance to ensure user satisfaction, reliability, scalability, and cost efficiency. Ideal for developers, QA engineers, and IT professionals. Visit Expeed Software for more information. https://expeed.com/

In-Depth Performance Testing Guide for IT Professionals

Expeed Software

In this session, we will showcase how to revolutionize automated testing for your software, automation, and QA teams with UiPath Test Suite. In part 1 of UiPath test automation using UiPath Test Suite – developer series, we will cover, Software testing overview What is software testing Why software testing is required Typical test types and levels Continuous testing and challenges Introduction to UiPath Test Suite UiPath Test Suite family of products Speaker: Atul Trikha, Chief Technologist & Solutions Architect, Peraton and UiPath MVP Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP

UiPath Test Automation using UiPath Test Suite series, part 1

DianaGray10

The standard Salesforce Approval process can be limiting in many ways, especially in complex scenarios. What if there was a way to implement very flexible approvals where one can use Apex code to make data updates in unrelated records, dynamically generate next steps details, and compute assignees on the fly? And still use UI-based configurations to implement concrete approval processes. In this session, we will share ideas behind such a solution and show a few lines of code to get you started.

Custom Approval Process: A New Perspective, Pavel Hrbacek & Anindya Halder

CzechDreamin

Welcome to UiPath Test Automation using UiPath Test Suite series part 2. In this session, we will cover API test automation along with a web automation demo. Topics covered: Test Automation introduction API Example of API automation Web automation demonstration Speaker Pathrudu Chintakayala, Associate Technical Architect @Yash and UiPath MVP Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP

UiPath Test Automation using UiPath Test Suite series, part 2

DianaGray10

As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other? Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.

Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024

Tobias Schneck

UiPath Test Automation using UiPath Test Suite series, part 3

DianaGray10

Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to: Create a campaign using Mailchimp with merge tags/fields Send an interactive Slack channel message (using buttons) Have the message received by managers and peers along with a test email for review But there’s more: In a second workflow supporting the same use case, you’ll see: Your campaign sent to target colleagues for approval If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team But—if the “Reject” button is pushed, colleagues will be alerted via Slack message Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors. And... Speakers: Akshay Agnihotri, Product Manager Charlie Greenberg, Host

Connector Corner: Automate dynamic content and events by pushing a button

DianaGray10

Speed Wins: From Kafka to APIs in Minutes

confluent

To Graph or Not to Graph Knowledge Graph Architectures and LLMs

Paul Groth

Designing Great Products: The Power of Design and Leadership by Chief Designe...

Product School

Empowering NextGen Mobility via Large Action Model Infrastructure (LAMI): pav...

Thierry Lestable

Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application. In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics. Length: 30 minutes Session Overview ------------------------------------------- During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana: - What out-of-the-box solutions are available for real-time monitoring JMeter tests? - What are the benefits of integrating InfluxDB and Grafana into the load testing stack? - Which features are provided by Grafana? - Demonstration of InfluxDB and Grafana using a practice web application To view the webinar recording, go to: https://www.rttsweb.com/jmeter-integration-webinar

JMeter webinar - integration with InfluxDB and Grafana

RTTS

How to differentiate Sales Cloud and CPQ on first glance might be tricky if you do not know where to look and what to look at. You will know :-) Managing the sales process within Salesforce is a common use case that can be managed with standart Sales Cloud. If you want to do entire quoting process you will find out Salesforce CPQ solution exists. What is then the difference if both can handle selling products? You will see comparison of 10 different features, which Sales Cloud and Salesforce CPQ handle differently. Simple question you will always remember if you should consider using Salesforce CPQ will be a cherry on top.

10 Differences between Sales Cloud and CPQ, Blanka Doktorová

CzechDreamin

This talk focuses on the practical aspects of integrating various telephony systems with Salesforce, drawing on examples from implementations in the Czech scene. It aims to inform attendees about the spectrum of telephony solutions available, from small to large scale, and their compatibility with Salesforce. The presentation will highlight key considerations for selecting a telephony provider that integrates smoothly with Salesforce, including important questions to support the decision-making process. It will also discuss methods for integrating existing telephony systems with Salesforce, aimed at companies contemplating or in the process of adopting this CRM platform. The discussion is designed to provide a straightforward overview of the steps and considerations involved in telephony and Salesforce integration, with an emphasis on functionality, compatibility, and the practical experiences of Czech companies.

Integrating Telephony Systems with Salesforce: Insights and Considerations, B...

CzechDreamin

Unsubscribed: Combat Subscription Fatigue With a Membership Mentality by Head...

Product School

Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place. Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects. Here’s what you’ll gain: - Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows. - Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy. - Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency. - Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity. We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic. Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.

Essentials of Automations: Optimizing FME Workflows with Parameters

Safe Software

Recently uploaded (20)

From Daily Decisions to Bottom Line: Connecting Product Work to Revenue by VP...

Search and Society: Reimagining Information Access for Radical Futures

AI for Every Business: Unlocking Your Product's Universal Potential by VP of ...

IOS-PENTESTING-BEGINNERS-PRACTICAL-GUIDE-.pptx

In-Depth Performance Testing Guide for IT Professionals

UiPath Test Automation using UiPath Test Suite series, part 1

Custom Approval Process: A New Perspective, Pavel Hrbacek & Anindya Halder

UiPath Test Automation using UiPath Test Suite series, part 2

Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024

UiPath Test Automation using UiPath Test Suite series, part 3

Connector Corner: Automate dynamic content and events by pushing a button

Speed Wins: From Kafka to APIs in Minutes

To Graph or Not to Graph Knowledge Graph Architectures and LLMs

Designing Great Products: The Power of Design and Leadership by Chief Designe...

Empowering NextGen Mobility via Large Action Model Infrastructure (LAMI): pav...

JMeter webinar - integration with InfluxDB and Grafana

10 Differences between Sales Cloud and CPQ, Blanka Doktorová

Integrating Telephony Systems with Salesforce: Insights and Considerations, B...

Unsubscribed: Combat Subscription Fatigue With a Membership Mentality by Head...

Essentials of Automations: Optimizing FME Workflows with Parameters

Applying machine learning to Laravel applications

1. by Oreoluwa Ogundipe Applying Machine Learning in Laravel Applications

2. Speaker Bio - Computer Vision/Machine Learning/Web Developer. - Computer Science Student at University of Ghana. - San Diego State University Alumni. - Fomer Master Builder at BuildIT, San Diego State University. - Developer at hotels.ng. - VSCode Advocate. - Gamer.

3. BRIEF INTRODUCTION TO MACHINE LEARNING

4. What is Machine Learning? - Machine Learning is the ability of a computer to predict the future based on past experiences. Examples - Netflix Movie Prediction. - Breast Cancer Prediction. - Predicting Prices of Houses in Lekki.

5. Debunking Machine Learning Myths - Machine Learning is difficult to understand. - You must be a ‘bad’ mathematician to understand it. - You need a laptop with maxed out specifications to run your application. - Machine Learning is absent of human bias.

6. Types of Learning -Supervised Learning. - Linear Regression, Logistic Regression, Neural Networks -Unsupervised Learning. - SVMs, KMeans -Reinforcement Learning.

7. Applications of Machine Learning -Machine Learning in Health Sector. -Machine Learning in Education. -Machine Learning in Traffic Control. - Machine Learning in Stock Market.

8. Machine Learning APIs - Tensorflow by Google - Scikit learn by Google. - Clarifai AI. - Microsoft Facial Detection API - Kairos API.

9. USING MACHINE LEARNING WITH LARAVEL

10.

11. BABEWATCH - This is a Laravel Application I made for demo purpose during this event. HOW IT WORKS - When a user signs their baby up, the user’s picture and pictures of other authorized baby carriers are taken. - Users are being verified by the Machine Learning Model provided by ‘Kairos API’.

12. @orehga Thank you for listening !" Any questions? @oreeog @ore.og

Applying machine learning to Laravel applications

Recommended

Recommended

More Related Content

What's hot

What's hot (20)

Similar to Applying machine learning to Laravel applications

Similar to Applying machine learning to Laravel applications (20)

Recently uploaded

Recently uploaded (20)

Applying machine learning to Laravel applications