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How To Train Large Language Models LLM like GPT 4 on PyTorch 2.0 | Distributed Model Training on GPU
Hi, thanks for watching our video about training large language models on PyTorch 2.0
In this video we’ll walk you through:
- GPT4
- GPU
- Multimodal
- LLM
- Model Training
- Model Parallelism
- Distributed Data Parallel DDP
- Deep Learning
- ChatGPT
- palm-e Models
- VALLE
- GOOGLE LAMDA
Mode Parallel, Gradient Accumulation, Data Parallel with PyTorch, Larger Batches
The PyTorch Distributed Stack is a set of PyTorch features that facilitate training models in distributed systems. PyTorch has introduced multiple new features in the distributed package to help support larger scale and higher efficiency for both data and model parallel. In this talk, Yanli Zhao (Software Engineer, Meta AI) shares tips on how to reduce memory footprint, fit larger models and achieve significant speedup with distributed systems with features like Zero Redundancy Optimizer, DistributedDataParallel, FullyShardedDataParallel, CUDA RDMA and ShardedTensor.
OpenAI has officially announced GPT-4 the latest version of its incredibly popular large language model powering artificial intelligence (AI) chatbots. ChatGPT is an AI chatbot that utilizes the language models of GPT-3 and GPT-4 for interactive communication. In other words you can use chatgpt with gpt4. A Generative Pre-Trained Transformer (GPT) is a sophisticated neural network architecture used to train large language models (LLMs). It makes use of large amounts of publicly available Internet text to simulate human communication.
Main improvements:
* It's multimodal (what it is is in video)
* Longer context length
* Better performance
* More safer
chatgpt,
gpt4,
google AI,
Google Lamda,
Google PALM,
artificial intelligence,
Machine learning,
transformer models,
deep learning,
Ray,Ray Tune,Hyperparameter optimization,machine learning,mlflow,open-source
TIMESTAMPS
0:00 Intro
0:55 PyTorch
1:25 Autograd
2:35 PyTorch Distributed
3:10 Types of Parallelization
5:20 DDP
6:38 FSDP
7:48 Fairscale
8:55 PyTorch Lightning
10:30 Model Parallel
11:48 Sagemaker Training
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LINKS/Sources USED:
PyTorch Distributed : https://www.youtube.com/watch?v=3XUG7cjte2U
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Fine-tuning GPT-3.
In this video we demonstrate how to use APIs and audio to create prompts and completions that can be used to fine-tune Transformers such as GPT-3. We show how to use a News API with Python to extract news articles and create a dataset for training and fine-tuning models.
We explain what prompt and completion datasets are, and the role they play in natural language processing in fine-tuning transformers. We then discuss using video and audio streams to build data pipelines for fine-tuning and transcribing speech to text using OpenAI's Whisper.
With over 500 hours of YouTube videos being uploaded every minute video is a rich source of detailed information with which to train AI.
The video first includes step-by-step demonstrations of using the News API to create a prompt and completion dataset and then shows how to build pipelines transcribing speech to text using Whisper.
Foundation models like GPT-3 can be 'Fine-Tuned'. This Fine_tuning allows them to learn the vocabulary and semantics of particular disciplines. If you want your AI solution to be familiar with options pricing algorithms, then you can show it lots of options pricing papers. If you'd like your AI to be able to converse and write about monetary policy then you can expose it to policy papers, books on economic theory, central bank meeting minutes and news conferences.
You train transformer AI models in a particular way. You don't simply feed in a file containing the text of the book or article that you are using to train the AI. You break the text up into a series of "Prompts" and "Completions". Prompts are the beginning of a passage of text and completions are what follow a prompt.
In this post we leverage the breadth and depth of content on YouTube. We propose a strategy to use selected videos to train our AI language models. These videos are chosen because they are rich in expertise in an area we want to train our language model in. The example used in this video is to train our AI in monetary policy by studying content generated by the Federal Reserve. We devise a strategy to use speech to text transcription of YouTube videos. We use OpenAI's 'Whisper' to perform this transcription. We then break this text up into 'prompts' and completions to train our model.
Following this approach we can easily train our NLP models to have expertise in any field we choose.
#NLP #Python #FineTuning #AI #MachineLearning #NewsAPI #Whisper #Lucidate #chatgpt #gpt3 #gpt-3 #openai #whisper #openai-whisper
Links:
Attention Mechanism in Transformers: https://youtu.be/sznZ78HquPc Transformer playlist: https://youtube.com/playlist?l....ist=PLaJCKi8Nk1hwaMU
Federal reserve Description: https://youtu.be/lJTmCv2kKKs
FOMC Feb 2023 Press Conference: https://www.youtube.com/watch?v=3Iv4aCN0OOo&t=2s
Python code for Transcriber class: https://github.com/mrspiggot/p....rompts_and_completio
=========================================================================
Link to introductory series on Neural networks:
Lucidate website: https://www.lucidate.co.uk/blog/categ...
YouTube: https://www.youtube.com/playlist?list...
Link to intro video on 'Backpropagation':
Lucidate website: https://www.lucidate.co.uk/post/intro...
YouTube: https://youtu.be/8UZgTNxuKzY
'Attention is all you need' paper - https://arxiv.org/pdf/1706.03762.pdf
=========================================================================
Transformers are a type of artificial intelligence (AI) used for natural language processing (NLP) tasks, such as translation and summarisation. They were introduced in 2017 by Google researchers, who sought to address the limitations of recurrent neural networks (RNNs), which had traditionally been used for NLP tasks. RNNs had difficulty parallelizing, and tended to suffer from the vanishing/exploding gradient problem, making it difficult to train them with long input sequences.
Transformers address these limitations by using self-attention, a mechanism which allows the model to selectively choose which parts of the input to pay attention to. This makes the model much easier to parallelize and eliminates the vanishing/exploding gradient problem.
Self-attention works by weighting the importance of different parts of the input, allowing the AI to focus on the most relevant information and better handle input sequences of varying lengths. This is accomplished through three matrices: Query (Q), Key (K) and Value (V). The Query matrix can be interpreted as the word for which attention is being calculated, while the Key matrix can be interpreted as the word to which attention is paid. The eigenvalues and eigenvectors of these matrices tend to be similar, and the product of these two matrices gives the attention score.
=========================================================================
#ai #deeplearning #chatgpt #gpt3 #neuralnetworks #attention #attentionisallyouneed
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00:00:47 What is Penetration Testing?
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This complete Artificial Intelligence and Machine Learning full course video covers all the topics that you need to know to become a master in the field of AI and Machine Learning. It covers all the basics of Machine Learning, the different types of Machine Learning, and the various applications of Machine Learning used in different industries. This video will also help us understand the basics of artificial intelligence. We will look at the future of AI and listen to some of the industry experts and learn what they have to say about AI. You will see the top 10 applications of AI in 2021. Then, we will understand Machine Learning and Deep Learning and the different algorithms used to build AI models. Finally, you will learn the Top 10 Artificial Intelligence Technologies In 2021.
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A good start at a Machine Learning definition is that it is a core sub-area of Artificial Intelligence (AI). ML applications learn from experience (well data) like humans without direct programming. When exposed to new data, these applications learn, grow, change, and develop by themselves.
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Artificial Intelligence is a method of making a computer, a computer-controlled robot, or software think intelligently like the human mind. AI is accomplished by studying the patterns of the human brain and by analyzing the cognitive process. The outcome of these studies develops intelligent software and systems.
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This Edureka video on 'Excel Tutorial' will help you learn all the basics of MS Excel. Here are all the topics discussed in this Excel Basics for Beginners video:
00:00:00 Introduction
00:00:24 Agenda
00:01:00 What is Excel
00:02:12 Excel SCreen Option
00:07:16 Backstage View
00:08:14 Workbooks & Worksheets
00:08:53 Editing Worksheets
00:19:34 Formatting MS Excel Worksheets
00:30:57 Formulas
00:33:08 Functions
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Jimmy Apples (who previously leaked a supposed AGI) was proven right again. OpenAI began work on Arrakis alongside GPT-4, its latest AI model, to create a system that could power AI applications like ChatGPT much more cheaply, the Information report said. The company ended up scrapping it earlier this year after it struggled to meet expectations around efficiency, the report added.
AI models that enable applications like ChatGPT are expensive and resource-intensive, requiring expensive compute power from cloud providers such as Microsoft, Amazon, and Google. Estimates have suggested that ChatGPT costs more than $700,000 per day to run.
OpenAI struck a partnership with Microsoft in January, giving it access to the tech giant's Azure platform. The Information reported that "some executives at Microsoft" were left disappointed by the shortcomings of Arrakis.
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Every day is a day closer to the Technological Singularity. Experience Robots learning to walk & think, humans flying to Mars and us finally merging with technology itself. And as all of that happens, we at AI News cover the absolute cutting edge best technology inventions of Humanity.
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TIMESTAMPS:
00:00 Jimmy Apple's New OpenAI Leaks
01:04 All New OpenAI Leaks
04:08 What is Artificial General Intelligence
05:58 Future of OpenAI
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#openai #ai #leaks
🔥𝐄𝐝𝐮𝐫𝐞𝐤𝐚 𝐌𝐢𝐜𝐫𝐨𝐬𝐨𝐟𝐭 𝐀𝐳𝐮𝐫𝐞 𝐂𝐞𝐫𝐭𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧 𝐓𝐫𝐚𝐢𝐧𝐢𝐧𝐠 𝐂𝐨𝐮𝐫𝐬𝐞 (𝐔𝐬𝐞 𝐂𝐨𝐝𝐞: 𝐘𝐎𝐔𝐓𝐔𝐁𝐄𝟐𝟎) :https://www.edureka.co/microso....ft-certified-azure-s
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Python has recently emerged as one of the fastest-growing programming languages. Not only is it extensively used, but it is also an excellent language to learn if you want to go into programming. On the other hand, Machine Learning trains computers by analysing data and statistics. It represents a step toward artificial intelligence (AI). A machine learning programme analyses data and learns to anticipate outcomes.
There are many reasons why you should consider learning Python for Machine Learning. Out of those, a few important ones are codable simplicity, a huge number of libraries and frameworks, fast development, flexible integration and massive online support.
Many other languages may be used in AI applications, but Python remains the best and should be given critical consideration. It may be used in collaboration with other popular tools in the developer's workflow. Python is now widely used by developers for machine learning applications due to its numerous advantages.
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A todos les gusta el flujo laminar pero el flujo turbulento es realmente fascinante.
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Un agradecimiento especial a:
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Destin de Smarter Every Day https://www.youtube.com/smartereveryday
Nicole Sharp de la FYFD https://ve42.co/fyfd
Simulaciones de turbulencias de Pavol Dobryakov: https://paveldogreat.github.io..../WebGL-Fluid-Simulat
Me introduje en el flujo turbulento a través del caos. La transición a la turbulencia a veces implica un período de duplicación. La turbulencia en sí misma es un movimiento caótico, es impredecible y sensiblemente dependiente de las condiciones iniciales. Lo que me sorprendió es todas las formas en que el flujo turbulento nos es útil. Es difusivo, lo que significa que favorece que las cosas se mezclen. Esto es útil en los motores a reacción o en las toberas de los cohetes (que estudia Destin) y es importante conseguirlo en los dispositivos microfluídicos, que son tan pequeños que el flujo turbulento es realmente difícil de conseguir. El flujo turbulento puede energizar una capa límite, lo que es importante para mantener el flujo sobre un ala, manteniendo la sustentación y retrasando la pérdida. De manera similar, una capa límite turbulenta sobre una pelota de golf reduce la resistencia a la presión permitiendo que las pelotas de golf vuelen más lejos. Esta es la razón de los hoyos en las pelotas de golf. Este hermoso fenómeno puede verse en la calle de vórtices de von Kàrmàn en las nubes vistas desde el espacio. La turbulencia está en todas partes, en las corrientes de aire de una habitación, en la aorta, en las respiraciones que exhalamos, en los oleoductos y tuberías de agua, en el flujo sobre coches, barcos y aviones. Los animales han evolucionado para ello (como los peces muertos nadando río arriba) y hemos diseñado nuestro entorno, nuestros aviones y bolas de golf para ello. El flujo laminar puede ser agradable de ver (por eso lo usamos en fuentes decorativas) pero el flujo turbulento da el verdadero impulso.
Animaciones de:
Jonny Hyman (Sol, Júpiter, Reynolds, aerodinámica, lapso de tiempo de la Tierra)
Investigación y escritura:
AJ Fillo y Derek Muller. AJ también creó los tiros de pelota de golf del túnel del viento
Filmado por:
Daniel Bydlowski y Derek Muller
Material adicional:
Imágenes de Júpiter cortesía de la NASA
Turbulencia en las corrientes de aire por la Chica de la Física, Dan Walsh, y Grant Sanderson https://youtu.be/N7d_RWyOv20
https://youtu.be/_UoTTq651dE
La música:
illBird "Shaffuru" https://youtu.be/5rkt53fNMgc
De EpidemicSound https://epidemicsound.com "Seaweed" "Colorful Animation 4”
Kevin MacLeod https://incompetech.com "Sneaky Adventure"
Video en Inglés del Canal @veritasium :https://youtu.be/5zI9sG3pjVU
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![Test Automation engineer Full course in 10 hours [2024] | Testing Course For Beginners | Edureka](https://i.ytimg.com/vi/R3JxCOZdL-c/maxresdefault.jpg)
🔥 𝐄𝐝𝐮𝐫𝐞𝐤𝐚 𝐀𝐮𝐭𝐨𝐦𝐚𝐭𝐢𝐨𝐧 𝐓𝐞𝐬𝐭𝐢𝐧𝐠 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫 𝐌𝐚𝐬𝐭𝐞𝐫𝐬 𝐂𝐨𝐮𝐫𝐬𝐞: https://www.edureka.co/masters....-program/automation- (𝐔𝐬𝐞 𝐂𝐨𝐝𝐞: 𝐘𝐎𝐔𝐓𝐔𝐁𝐄𝟐𝟎)
In this Edureka's Full course explore the comprehensive world of Test Automation Engineering in this Full course. From fundamental concepts to advanced strategies, learn how to design, implement, and maintain automated test suites. Additionally, explore various software testing tools to enhance your skill set.
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📢📢 𝐓𝐨𝐩 𝟏𝟎 𝐓𝐫𝐞𝐧𝐝𝐢𝐧𝐠 𝐓𝐞𝐜𝐡𝐧𝐨𝐥𝐨𝐠𝐢𝐞𝐬 𝐭𝐨 𝐋𝐞𝐚𝐫𝐧 𝐢𝐧 2023 𝐒𝐞𝐫𝐢𝐞𝐬 📢📢
⏩ NEW Top 10 Technologies To Learn In 2023 - https://youtu.be/udD_GQVDt5g
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Decision Tree In Machine Learning | Decision Tree Algorithm In Python |Machine Learning |Simplilearn
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This Decision Tree in the Machine Learning tutorial will help you understand all the basics of the Decision Tree and how the Decision Tree algorithm works. In the end, we will implement a Decision Tree algorithm in Python on loan payment prediction. This Decision Tree tutorial is ideal for both beginners as well as professionals who want to learn Machine Learning Algorithms.
Below topics are covered in this Decision Tree Algorithm Tutorial:
0. Intro (0:00)
1. What is Machine Learning? ( 02:25 )
2. Types of Machine Learning? ( 03:27 )
3. Problems in Machine Learning ( 04:43 )
4. What is a Decision Tree? ( 06:29 )
5. What are the problems a Decision Tree Solves? ( 07:11 )
6. Advantages of Decision Tree ( 07:54 )
7. How does Decision Tree Work? ( 10:55 )
8. Use Case - Loan Repayment Prediction ( 14:32 )
Dataset Link - https://drive.google.com/drive..../folders/1JybOOdRsMY
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What is a Decision Tree Algorithm?
A Decision Tree is a supervised machine learning algorithm for solving classification problems. Generally, a decision tree is drawn upside down with its root at the top and it is known as the Top-Down Approach. Decision trees are used for handling non-linear data sets effectively. The decision tree tool is used in real life in many areas, such as engineering, civil planning, law, and business.
➡️ About Post Graduate Program In AI And Machine Learning
This AI ML course is designed to enhance your career in AI and ML by demystifying concepts like machine learning, deep learning, NLP, computer vision, reinforcement learning, and more. You'll also have access to 4 live sessions, led by industry experts, covering the latest advancements in AI such as generative modeling, ChatGPT, OpenAI, and chatbots.
✅ Key Features
- Post Graduate Program certificate and Alumni Association membership
- Exclusive hackathons and Ask me Anything sessions by IBM
- 3 Capstones and 25+ Projects with industry data sets from Twitter, Uber, Mercedes Benz, and many more
- Master Classes delivered by Purdue faculty and IBM experts
- Simplilearn's JobAssist helps you get noticed by top hiring companies
- Gain access to 4 live online sessions on latest AI trends such as ChatGPT, generative AI, explainable AI, and more
- Learn about the applications of ChatGPT, OpenAI, Dall-E, Midjourney & other prominent tools
✅ Skills Covered
- ChatGPT
- Generative AI
- Explainable AI
- Generative Modeling
- Statistics
- Python
- Supervised Learning
- Unsupervised Learning
- NLP
- Neural Networks
- Computer Vision
- And Many More…
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Watch sample class recording: http://www.edureka.co/pmp?utm_source=youtube&utm_medium=webinar&utm_campaign=pmp-webinar-5-6-2015
PMI's Project Management Professional (PMP)® credential is the most important industry-recognized certification for project managers. Globally recognized and demanded, the PMP® demonstrates that you have the experience, education and competency to lead and direct projects.
The Topics covered in the webinar are:
1.Introduction to PMP
2.Prerequisites for PMP
3.PMP Examination Pattern
4.PMP Examination Pattern
5.PMBOK Guide
6.Impact of Variable on Project Life Cycle
7.Project Integration Management
Edureka is a New Age e-learning platform that provides Instructor-Led Live, Online classes for learners who would prefer a hassle free and self paced learning environment, accessible from any part of the world.
The topics related to ‘Introduction to PMP’ have extensively been covered in our course ‘PMP Exam Preparation’.
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Random Forest in Machine Learning | Machine Learning Training | Edureka | Machine Learning Rewind- 3
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This Edureka video on Random Forest in Machine Learning explains the concept of the Random Forest algorithm in Python and how is it used.
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How it Works?
1. This is a 5 Week Instructor led Online Course,40 hours of assignment and 20 hours of project work
2. We have a 24x7 One-on-One LIVE Technical Support to help you with any problems you might face or any clarifications you may require during the course.
3. At the end of the training you will be working on a real time project for which we will provide you a Grade and a Verifiable Certificate!
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About the Course :
Edureka’s Machine Learning Course using Python is designed to make you grab the concepts of Machine Learning. The Machine Learning training will provide deep understanding of Machine Learning and its mechanism. As a Data Scientist, you will be learning the importance of Machine Learning and its implementation in python programming language. Furthermore, you will be taught of Reinforcement Learning which in turn is an important aspect of Artificial Intelligence. You will be able to automate real life scenarios using Machine Learning Algorithms. Towards the end of the course we will be discussing various practical use cases of Machine Learning in python programming language to enhance your learning experience.
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Why Learn Machine Learning with Python?
Data Science is a set of techniques that enables the computers to learn the desired behavior from data without explicitly being programmed. It employs techniques and theories drawn from many fields within the broad areas of mathematics, statistics, information science, and computer science. This course exposes you to different classes of machine learning algorithms like supervised, unsupervised and reinforcement algorithms. This course imparts you the necessary skills like data pre-processing, dimensional reduction, model evaluation and also exposes you to different machine learning algorithms like regression, clustering, decision trees, random forest, Naive Bayes and Q-Learning.
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Large Language Models like ChatGPT have remarkable abilities to generate content based on training data but do they have actual intelligence? Find out more about how LLM's and Chatbots work as we explore this question.
Featuring:
Mira Murati the CTO of OpenAI
Cristóbal Valenzuela the creator of Runway
Presented by: Code.org, ETS, ISTE, Khan Academy
Start learning today!
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In this ChatGPT full course, we'll explore how chatgpt is useful in various domains, what is chat gpt and how to use chatgpt, how strong are the competitors of chatgpt and much more.
00:00 ChatGPT Complete Course
What is GPT 4?
Automate Excel using ChatGPT
How to use ChatGPT for SEO?
Chat GPT for Content Creation
Master SEO Content Using ChatGPT
ChatGPT for Marketing
How to Build a Website Using ChatGPT
Chat GPT for Programming
Can ChatGPT Solve LeetCode Problems?
Create a Telegram Bot Using ChatGPT
How To Integrate ChatGPT With Whatsapp
Automate WhatsApp using ChatGPT
Build a Game using Python and ChatGPT
ChatGPT in Cybersecurity
ChatGPT for UI/UX Design
6 Reasons Why ChatGPT Can Make Your Business
9 Ways to Make Money Online Using ChatGPT
Bing vs Bard vs ChatGPT : Comparison
Will ChatGPT and Bard Take Over SEO?
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#ChatGPTCompleteCourse #ChatGPTFullCourse #ChatGPTCourse #ChatGPTExpert #ChatGPTForBeginners #ChatGPT #Chat_GPT #ChatGPTTutorial #ChatGPTOpenAI #Simplilearn
➡️ About Post Graduate Program In AI And Machine Learning
This AI ML course is designed to enhance your career in AI and ML by demystifying concepts like machine learning, deep learning, NLP, computer vision, reinforcement learning, and more. You'll also have access to 4 live sessions, led by industry experts, covering the latest advancements in AI such as generative modeling, ChatGPT, OpenAI, and chatbots.
✅ Key Features
- Post Graduate Program certificate and Alumni Association membership
- Exclusive hackathons and Ask me Anything sessions by IBM
- 3 Capstones and 25+ Projects with industry data sets from Twitter, Uber, Mercedes Benz, and many more
- Master Classes delivered by Purdue faculty and IBM experts
- Simplilearn's JobAssist helps you get noticed by top hiring companies
- Gain access to 4 live online sessions on latest AI trends such as ChatGPT, generative AI, explainable AI, and more
- Learn about the applications of ChatGPT, OpenAI, Dall-E, Midjourney & other prominent tools
✅ Skills Covered
- ChatGPT
- Generative AI
- Explainable AI
- Generative Modeling
- Statistics
- Python
- Supervised Learning
- Unsupervised Learning
- NLP
- Neural Networks
- Computer Vision
- And Many More…
👉 Enroll Now: https://www.simplilearn.com/pgp-ai-machine-learning-certification-training-course?utm_campaign=28September2023ChatGPTCompleteCourse2023&utm_medium=Description&utm_source=youtube
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El Deep Learning ha cambiado el mundo en sólo una década y hoy os contaré cómo esta rama de la informática podría seguir evolucionando. Y lo haremos desde el comienzo, con las redes neuronales más sencillas hasta Google Gemini, la futura promesa de Google DeepMind, pasando eso sí por los enormes modelos fundacionales como ChatGPT. ¡Bienvenidos a la nueva temporada de DotCSV!
📹 EDICIÓN: Carlos Santana y Diego Gonzalez (Diocho)
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