Differences Between Machine Learning Ml Artificial Intelligence AI And Deep Learning DL

Jun 18, 20200 likes469 views

Differences between Machine Learning ML Artificial Intelligence AI and Deep Learning DL is for the mid level managers to give information about what is AI, what is Machine Learning, what is deep learning, Machine learning process. You can also know the difference between Machine learning and Deep learning to understand AI, ML, and DL in a better way for business growth. https://bit.ly/2zFJeSC

Differences between Machine
Learning ML Artificial Intelligence
AI and Deep Learning DL
Your C ompany N ame

2
Difference between AI vs ML vs DL01
o What is AI?
o What is ML?
o What is Deep Learning?
o Machine Learning Process
o Deep Learning Process
o Difference between Machine Learning and Deep Learning
o Which is better to start AI,ML or Deep learning

Difference between AI vs ML vs DL
3This slide is 100% editable. Adapt it to your needs and capture your audience's attention.
Engineering of
making intelligent
machines and
programs
Artificial
Intelligence
Ability to learn without
being explicitly
programmed
Machine
Learning
Learning
based on deep
neural network
Deep
Learning

4
What is AI?
Artificial
Intelligence
(AI) is a popular branch of computer science that
concerns with building “intelligent” smart
machines capable of performing intelligent tasks.
With rapid advancements in deep learning and
machine learning, the tech industry is
transforming radically.
This slide is 100% editable. Adapt it to your needs and capture your audience's attention.

What is ML?
5
Ordinary System With AI Machine Learning
Machine Learning
is a type of AI that enables machines to learn from data and deliver predictive models. The
machine learning is not dependent on any explicit programming but the data fed into it. It is a
complicated process. Based on the data you feed into machine learning algorithm and the training
given to it, an output is delivered. A predictive algorithm will create a predictive model.
Introduction to Machine learning
Learns
Predicts
Improves
This slide is 100% editable. Adapt it to your needs and capture your audience's attention.

What is Deep Learning?
6
Artificial intelligence (AI) is a popular branch of
computer science that concerns with building
“intelligent” smart machines capable of
performing intelligent tasks.
With rapid advancements in deep learning and
machine learning, the tech industry is
transforming radically.
This slide is 100% editable. Adapt it to your needs and capture your audience's attention.

Modelling
Candidate & Final
Visualisation
Predictions & Strategy
Data
Raw & Training Data
Machine Learning Process
7
Data
Gathering
Data
Cleaning
Selecting
Right Algorithms
Building
Model & Finalising
Data Transformation
into Predictions
This slide is 100% editable. Adapt it to your needs and capture your audience's attention.

Deep Learning Process
8
Understand
the Problem
Identify
Data
Select Deep
Learning
Algorithm Training
the Model
Test
the Model
This slide is 100% editable. Adapt it to your needs and capture your audience's attention.

Difference between Machine Learning and Deep Learning
9
Machine Learning
Input Feature Extraction Classification Output
Car
Not Car
Deep Learning
Input OutputFeature Extraction + Classification
Car
Not Car
This slide is 100% editable. Adapt it to your needs and capture your audience's attention.

Which is better to start AI,ML or DL?
10
Any Technique which enables computers to mimic human
behavior.
Artificial Intelligence
Subset of ML which make the Computation of Multi-layer
Neural Networks Feasible.
Deep Learning
Subset of AI Techniques which use Statistical Methods to
Enable Machines to Improve with Experiences.
Machine Learning
This slide is 100% editable. Adapt it to your needs and capture your audience's attention.

Prompt engineering is a fundamental concept within the field of artificial intelligence, with particular relevance to natural language processing. It involves the strategic embedding of task descriptions within the input data of an AI system, often in the form of a question or query, as opposed to explicitly providing the task description separately. This approach optimizes the efficiency and effectiveness of AI models by encapsulating the desired outcome within the input context, thereby enabling more streamlined and context-aware responses.

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Session 1 👉This first session will cover an introduction to Generative AI & harnessing the power of large language models. The following topics will be discussed: Introduction to Generative AI & harnessing the power of large language models. What’s generative AI & what’s LLM. How are we using it in our document understanding & communication mining models? How to develop a trustworthy and unbiased AI model using LLM & GenAI. Personal Intelligent Assistant Speakers: 📌George Roth - AI Evangelist at UiPath 📌Sharon Palawandram - Senior Machine Learning Consultant @ Ashling Partners & UiPath MVP 📌Russel Alfeche - Technology Leader RPA @qBotica & UiPath MVP

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The document provides an introduction to artificial intelligence (AI) and its history. It defines key AI terms like artificial intelligence, machine learning, and deep learning. It explains how deep learning helps solve limitations of classic machine learning by determining representations of data. The summary highlights major developments in AI history including early algorithms, expert systems, neural networks, and breakthroughs with deep learning starting in 2006. It differentiates modern AI using deep learning from prior approaches and provides examples of AI applications.

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Artificial intelligence (AI) involves creating intelligent computer programs and machines that can interact with the real world similarly to humans. AI uses techniques like machine learning, deep learning, and neural networks to allow programs to learn from data and experience without being explicitly programmed. While AI has potential benefits, some experts warn that advanced AI could pose risks if not developed carefully due to concerns it could become difficult for humans to control once a certain level of intelligence is achieved.

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The presentation briefly answers the questions: 1. What is Machine Learning? 2. Ideas behind Neural Networks? 3. What is Deep Learning? How different is it from NN? 4. Practical examples of applications. For more information: https://www.quora.com/How-does-deep-learning-work-and-how-is-it-different-from-normal-neural-networks-and-or-SVM http://stats.stackexchange.com/questions/114385/what-is-the-difference-between-convolutional-neural-networks-restricted-boltzma https://www.youtube.com/watch?v=n1ViNeWhC24 - presentation by Ng http://techtalks.tv/talks/deep-learning/58122/ - deep learning tutorial and slides - http://www.cs.nyu.edu/~yann/talks/lecun-ranzato-icml2013.pdf Deep learning for NLP - http://www.socher.org/index.php/DeepLearningTutorial/DeepLearningTutorial papers: http://www.cs.toronto.edu/~hinton/science.pdf http://machinelearning.wustl.edu/mlpapers/paper_files/AISTATS2010_ErhanCBV10.pdf http://arxiv.org/pdf/1206.5538v3.pdf http://arxiv.org/pdf/1404.7828v4.pdf More recommendations - https://www.quora.com/What-are-the-best-resources-to-learn-about-deep-learning

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Talk given at the 2nd Winter Academy on Artificial Intelligence and International Law of the Asser Institute. The birth of AI: Dartmouth workshop. The biggest AI waves: classic symbolic AI (reasoning, knowledge systems, problem-solving), machine learning (induction). Current problems: explainability, trustworthyness, impact and transformation on society and people, the rise of artificially dumber systems.

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This document appears to be a presentation template on artificial intelligence and automation. It includes sections on intelligent process automation frameworks, the intelligent process automation spectrum and continuum, and how automation solutions can scale from initial automation to cognitive computing and artificial intelligence. There are also example slides on the company providing the solutions, their team, targets, financial information and contact details. The slides are meant to be fully customizable for the specific needs of the audience and company using the presentation.

Back Propagation Neural Network In AI PowerPoint Presentation Slide Templates...SlideTeam

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