Machine Learning With Python | Machine Learning Algorithms | Machine Learning Tutorial | Simplilearn
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This Machine Learning With Python presentation gives an introduction to Machine Learning and how to implement machine learning algorithms in Python. By the end of this presentation you will be able to understand Machine Learning workflow, steps to download Anaconda, types of Machine Learning and application of these in a demo showcasing Linear Regression and K-Means clustering. Below are the topics covered in this Machine Learning presentation: 1. Why Machine Learning? 2. Applications of Machine Learning 3. How does Machine Learning work? 4. Machine Learning Workflow 5. Steps to download Anaconda 6. Types of Machine Learning 7. Linear Regression Demo 8. K-Means Clustering Demo 9. Use Case - Weather Analysis - - - - - - - - About Simplilearn Machine Learning course: A form of artificial intelligence, Machine Learning is revolutionizing the world of computing as well as all people’s digital interactions. Machine Learning powers such innovative automated technologies as recommendation engines, facial recognition, fraud protection and even self-driving cars.This Machine Learning course prepares engineers, data scientists and other professionals with knowledge and hands-on skills required for certification and job competency in Machine Learning. - - - - - - - Why learn Machine Learning? Machine Learning is taking over the world- and with that, there is a growing need among companies for professionals to know the ins and outs of Machine Learning The Machine Learning market size is expected to grow from USD 1.03 Billion in 2016 to USD 8.81 Billion by 2022, at a Compound Annual Growth Rate (CAGR) of 44.1% during the forecast period. - - - - - - Who should take this Machine Learning Training Course? We recommend this Machine Learning training course for the following professionals in particular: 1. Developers aspiring to be a data scientist or Machine Learning engineer 2. Information architects who want to gain expertise in Machine Learning algorithms 3. Analytics professionals who want to work in Machine Learning or artificial intelligence 4. Graduates looking to build a career in data science and Machine Learning - - - - - -
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What is LSTM ?| Long Short Term Memory Explained with Example | Deep Learning...
In this video on What is LSTM, we will go through what is LSTM, moving forward we will learn what is RNN, and after this, we will see the types of gates in LSTM and some applications of LSTM. At the end of the video, we will see a hands-on lab demo of gold price prediction using the LSTM model in machine learning.
00:00 What is LSTM?
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What is LSTM?
Long Short-Term Memory (LSTM) is a type of Recurrent Neural Network (RNN) that can capture long-term dependencies in sequential data. LSTMs are able to process and analyze sequential data, such as time series, text, and speech. They use a memory cell and gates to control the flow of information, allowing them to selectively retain or discard information as needed and thus avoid the vanishing gradient problem that plagues traditional RNNs. LSTMs are widely used in various applications such as natural language processing, speech recognition, and time series forecasting.
What is RNN?
RNNs are a type of neural network that are designed to process sequential data. They can analyze data with a temporal dimension, such as time series, speech, and text. RNNs can do this by using a hidden state passed from one timestep to the next. The hidden state is updated at each timestep based on the input and the previous hidden state. RNNs are able to capture short-term dependencies in sequential data, but they struggle with capturing long-term dependencies.
Types of Gates in LSTM
Input gate
Output gate
Forget gate
Applications of LSTM
Language Simulation
Voice Recognition
Sentiment analysis
Time series prediction
Video analysis
Handwriting recognition
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00:00 Chat GPT Usecases
01:04 What is Chat GPT?
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How does chatGPT work?
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When the model receives input from a user, it uses the patterns it learned during fine-tuning to generate a response
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05:05 - React vs Next js : Server side rendering
05:35 - React vs Next js : Community
06:40 - React vs Next js : Configuration
07:18 - React vs Next js : Maintance
07:43 - React vs Next js : Development Cost
08:00 - React vs Next js : Features
08:37 - Where React js and Next js is used?
What is React JS?
1. React is a JavaScript library that builds fast, interactive mobile and web applications.
2. It is an open-source, reusable component-based front-end library of JavaScript.
3. React is a combination of HTML and JavaScript.
4. It provides a robust and opinionated way to build modern applications Interface.
What Next js is.
1.Next js is an open-source web framework created by Vercel.
2. Next js enables React-based web applications with server-side rendering and generating static websites.
3. In addition, next js offers additional structure, features, and optimizations for your application.
4. Next.js takes care of the tooling and settings required for React Js.
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This video covers What is Backpropagation in Neural Networks? Neural Network Tutorial for Beginners includes a definition of backpropagation, working of backpropagation, benefits of backpropagation, and applications.
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01:41 - What is Backpropagation in neural networks?
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02:23 - How does Backpropagation in neural networks work?
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05:28 - Benefits of Backpropagation
This content highlights the importance of backpropagation and gives you a reason to choose the same.
05:54 - Applications of Backpropagation
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