![TensorFlow](/img/default-banner.jpg)
- Видео 655
- Просмотров 121 773 334
TensorFlow
Добавлен 22 дек 2017
Welcome to the official TensorFlow RUclips channel. Stay up to date with the latest TensorFlow news, tutorials, best practices, and more!
TensorFlow is an open-source machine learning framework for everyone.
TensorFlow is an open-source machine learning framework for everyone.
Google I/O Special with Mat Velloso and Logan Kilpatrick
Join host Ashley Oldacre and guests Mat Velloso, VP of ML developer products at Google, and Logan Kilpatrick, Product Lead for Google AI, for a special episode on the latest on AI, recorded right from Google I/O.
Chapters:
0:00 - Introduction
01:54 - Mat and Logan’s story into Tech
07:12 - How did technology make your life better?
10:25 - Tech revolutions leading to AI
16:10 - Staying connected to tech in leadership
21:28 - What products do Mat and Logan work on?
25:45 - What is Google AI studio?
28:00 - Take us to I/O in person
33:05 - AI launches at I/O
39:00 - Useful AI applications that solve problems
47:52 - AI and automation
54:11 - Mat and Logan’s prediction for the future
Resources:
Top AI...
Chapters:
0:00 - Introduction
01:54 - Mat and Logan’s story into Tech
07:12 - How did technology make your life better?
10:25 - Tech revolutions leading to AI
16:10 - Staying connected to tech in leadership
21:28 - What products do Mat and Logan work on?
25:45 - What is Google AI studio?
28:00 - Take us to I/O in person
33:05 - AI launches at I/O
39:00 - Useful AI applications that solve problems
47:52 - AI and automation
54:11 - Mat and Logan’s prediction for the future
Resources:
Top AI...
Просмотров: 601
Видео
François Chollet - Creating Keras 3
Просмотров 17 тыс.2 месяца назад
Meet François Chollet, creator of Keras, software engineer, and AI researcher at Google. Join François and hosts Ashley Oldacre and Gus Martins as they discuss how Keras 3 was created, integrating Keras 3 with Gemma and Kaggle, artificial general intelligence (AGI), and much more! Resources: François Chollet research → goo.gle/443V3vG Deep Learning With Python, Second Edition → goo.gle/3UnpdH1 ...
Upskill your career in AI
Просмотров 8 тыс.2 месяца назад
Meet Tina Huang, a RUclipsr and the Founder of Lonely Octopus, a program that teaches students AI skills and then matches them with real companies to work on developing AI solutions. Ashley Oldacre and Tina Huang discuss how to incorporate AI into your career, unfair advantages and identity capital, if AI will steal our jobs, and much more on this podcast People of AI episode. Resources: Tina H...
Tris Warkentin - Introducing Gemma, Google's family of open models
Просмотров 2,6 тыс.2 месяца назад
Meet Tris Warkentin, Product Management lead for Google DeepMind’s next-generation AI research, working to achieve Artificial General Intelligence (AGI). Learn about the Gemini ecosystem and Google’s newest family of open models, Gemma! Discover what it can do and why this is a monumental step for the developer community. Unpack the power and exciting new capabilities this will unleash in the h...
Kathleen Kenealy - Creating, building, and releasing Gemma, Google's open model family
Просмотров 4 тыс.2 месяца назад
Meet Kathleen Kenealy, a Software Engineer at Google DeepMind, working on the next generation of large language models (LLMs) and open models. In the episode we talk about Gemma, Google’s newest family of lightweight, state-of-the-art open models built from the same research and technology used to create the Gemini models. Join us as we unpack the history and the importance of open technology a...
Jeanine Banks - Leveraging the power of the developer community
Просмотров 2 тыс.3 месяца назад
Meet Jeanine Banks, VP and GM of the Developer X and Developer Relations Business at Google. In this role, she empowers millions of developers to build AI enabled businesses and applications for billions of users worldwide. Hear about the latest Google AI ecosystem of tools from Gemini and Gemma to Projet IDX. Learn about her journey into the tech industry, solving real-world problems with AI, ...
Deep dive into TensorFlow Ranking for recommendations
Просмотров 9 тыс.3 месяца назад
Learn how to use TensorFlow Ranking alone for recommendation. Wei, a Developer Advocate at Google, covers retrieval-ranking pipeline for large recommendation models, ranking-ony recommendations, and the relationship between TF Recommenders and TF Ranking. Chapters: 0:00 - Introduction 1:18 - TensorFlow Ranking 4:22 - Differences between TF Recommenders and TF Ranking Resources: Recommend movies...
How to perform object detection with KerasCV
Просмотров 3,2 тыс.3 месяца назад
Learn how to perform object detection with a pretrained model in KerasCV. Prefer to build a custom model? Wei, a Developer Advocate at Google, has got you covered! He also covers how to build an object detection pipeline to train a custom model easily so that you can use it for your special use cases. Chapters: 0:00 - Introduction 1:39 - Object detection with a pre trained model 4:21 - Train a ...
Accelerate embedding lookup operation (Building recommendation systems with TensorFlow)
Просмотров 7673 месяца назад
Learn how to leverage TPU embeddings to accelerate embedding lookup operation with SpareCore. SpareCore is the specially-designed hardware on Google’s latest TPU. Wei, a Developer Advocate at Google, covers how to speed up the embedding lookup operation with large embedding tables in recommendation models. Chapters: 0:00 - Introduction 0:35 - How retrieval works in large scale recommendation sy...
Perform text classification with KerasNLP
Просмотров 1,5 тыс.3 месяца назад
Learn how to perform text classification using KerasNLP with Wei, a Developer Advocate at Google. This video covers a progressive approach of going from basic inference with a pretrained classifier and fine-tuning a custom model. Chapters: 0:00 - Introduction 1:48 - Inference with a pretrained classifier 2:30 - Fine-tuning a pretrained BERT backbone 2:53 - Fine-tuning with a user-controlled pre...
Generate images with stable diffusion using KerasCV
Просмотров 1,6 тыс.3 месяца назад
Learn how to use stable diffusion for image generation with KerasCV. Wei, a Developer Advocate at Google, shares how to create images from text prompts. Find out how TensorFlow tooling, mixed precision, and XLA compilation, allows you to experiment much faster. Chapters: 0:00 - Introduction 0:35 - How does stable diffusion work? 2:34 - How to run stable diffusion faster 4:13 - Resources Resourc...
Overview of KerasCV and KerasNLP
Просмотров 2,4 тыс.3 месяца назад
Learn about KerasCV and KerasNLP with Wei, a developer advocate at Google. In this video, you’ll learn about Keras Core, a modular backend architecture which allows you to run Keras code on top of arbitrary frameworks. From setting up the backend with KerasCV and KerasNLP. We also cover what you can do with KerasCV from image classification and object detection to data augmentation and image ge...
Generate text with KerasNLP
Просмотров 2,4 тыс.3 месяца назад
Learn how to generate text with KerasNLP. Wei, a Developer Advocate at Google, shares how to fine-tune a Reddit dataset and how you can use the fine-tuned model on-device. Chapters: 0:00 - Introduction 0:31 - Generate text with a pretrained model 1:53 - Fine-tuning on Reddit dataset 3:04 - On-device running 3:22 - Sampling methods 3:58 - Resources Resources: Tutorial → goo.gle/3V9iuBf I/O 2023 ...
How to leverage KerasCV for image classification
Просмотров 1,6 тыс.3 месяца назад
Learn how to leverage KerasCV for image classification. Wei, a Developer Advocate at Google, covers basic inference with a pretrained classifier, fine-tuning a pretrained backbone, and a more advanced task of training an image classifier from scratch. Learn how you can re-use the built-in models and modules to build powerful image classifiers! Chapters: 0:00 - Introduction 0:27 - Inference with...
Augmenting image data with Keras CV
Просмотров 1,3 тыс.3 месяца назад
Learn how to perform data augmentation with KerasCV. Wei, a Developer Advocate at Google, covers how to augment image data with some of the most popular and useful augmentation layers: ‘RandAugment,’ ‘CutMix,’ and ‘MixUp.’ These layers are used in nearly all state-of-the-art image classification pipelines. Chapters: 0:00 - Introduction 0:56 - Layers 2:24 - Customizing augmentation pipeline 3:12...
Indira Negi - Investing in AI hardware for health
Просмотров 6 тыс.4 месяца назад
Indira Negi - Investing in AI hardware for health
Adrit Rao - AI student, app developer, and researcher
Просмотров 6 тыс.4 месяца назад
Adrit Rao - AI student, app developer, and researcher
Take action and start building with Google AI
Просмотров 5 тыс.6 месяцев назад
Take action and start building with Google AI
The impact of Generative AI in different industries
Просмотров 2,3 тыс.6 месяцев назад
The impact of Generative AI in different industries
From recovering Pro Golfer to AI Entrepreneur
Просмотров 1,3 тыс.6 месяцев назад
From recovering Pro Golfer to AI Entrepreneur
Community matters: 8 reasons why you should be involved with Kaggle
Просмотров 8756 месяцев назад
Community matters: 8 reasons why you should be involved with Kaggle
AI-powered infrastructure: Cloud TPUs
Просмотров 1 тыс.6 месяцев назад
AI-powered infrastructure: Cloud TPUs
Latest Generative AI products and solutions on Google Cloud
Просмотров 9136 месяцев назад
Latest Generative AI products and solutions on Google Cloud
Unlock the power of Generative AI: From understanding its principles to utilizing its power
Просмотров 7286 месяцев назад
Unlock the power of Generative AI: From understanding its principles to utilizing its power
Deploying ML models to mobile devices
Просмотров 1,2 тыс.6 месяцев назад
Deploying ML models to mobile devices
How autonomous agents will support data exploration
Просмотров 8186 месяцев назад
How autonomous agents will support data exploration
The future of frameworks: Navigate the OSS landscape
Просмотров 1,1 тыс.6 месяцев назад
The future of frameworks: Navigate the OSS landscape
We need to have a Men's Symposium in ML. Are you okay with that?
For more information, check out the description.
Oh look👀 lz
🙂
can i use it in react native ?
讲解得太透彻了
what was the instructor name?
Thank you so much. Greetings from Cali, Colombia.
Running that code, I got an error: `ValueError: Unrecognized data type: x=[10.0] (of type <class 'list'>)` -- fixed when I changed the predict() arg to `np.array([10.0])`
when using tfrs.metrics.FactorizedTopK in tf version 2.16 we get the error : Cannot convert '('c', 'o', 'u', 'n', 't', 'e', 'r')' to a shape. Found invalid entry 'c' of type '<class 'str'>'. how to resolve this
Very interesting video. Greetings from Popayan, Colombia.
print(model.predict(np.array([[10.0]])))
Personal anecdote from early 2024: Using OpenAI ChatGPT 4 LLM prompting plus using the ChatGPT Python Code Interpreter module _together_ enabled the AI to solve 1 difficult problem from the ARC challenge fairly quickly. So LLM alone = no general AI, but LLM + code interpreter is much more capable and hence more intelligent . So does combining LLM + code interpreter + prompt rewriting + Self-refine + ... move us even more closer to general AI?.... That's a Q I would have asked in hindsight
great , I am more clear about it , but still I dont know should I move to MLE or I should continue working as SWE ?
Isn't c and c++ faster than python?
i was having troubles with the code till i used this (from keras_preprocessing.text import Tokenizer sentences = [ 'i love my dog', 'i love my cat', 'i hate dog!' ] tokenizer = Tokenizer(num_words=100) tokenizer.fit_on_texts(sentences) word_index = tokenizer.word_index print(word_index) )
What a great learning experience! Thank you Laurence Moroney
안녕하세용
Hindi version sounded pretty good to me. All the examples are pretty amazing
from flask import Flask, request, jsonify from pyngrok import ngrok import pandas as pd import numpy as np import re # إعداد Flask app = Flask(__name__) # تحميل البيانات ومعالجتها كما هو في الكود الأصلي PATH = '/content/mobile_recommendation_system_dataset.csv' data = pd.read_csv(PATH) # هنا نضع بقية كود معالجة البيانات كما هو في المثال السابق def recommend(brand=None, system=None, min_price=None, max_price=None, top_n=10): filtered_data = data if brand: filtered_data = filtered_data[filtered_data['Brand'] == brand] if system: filtered_data = filtered_data[filtered_data['System'] == system] if min_price is not None: filtered_data = filtered_data[filtered_data['price'] >= min_price] if max_price is not None: filtered_data = filtered_data[filtered_data['price'] <= max_price] if filtered_data.empty: return [] recommendations = filtered_data.sort_values(by='ratings', ascending=False).head(top_n) return list(recommendations.name.values) @app.route('/recommend', methods=['GET']) def get_recommendations(): brand = request.args.get('brand') system = request.args.get('system') min_price = request.args.get('min_price', type=int) max_price = request.args.get('max_price', type=int) top_n = request.args.get('top_n', type=int, default=10) recommendations = recommend(brand, system, min_price, max_price, top_n) return jsonify(recommendations) # تشغيل ngrok لإتاحة الخادم عبر الإنترنت url = ngrok.connect(5000) print(f"Public URL: {url}") app.run()
i love you
Great lecture! Really easy to follow. Even being familiar with Lin Alg, this gave me some cool insights on Data Science.
amei 💙
amei 💙
amei !!!! 💙
Good
very clear thank you
Im another one who don't catch why 128 neurons in the middle layer. Great Video, thanks.
1:14 lol they compared TPU runtimes with GPU runtimes
Thanks sir
0:16
sentences = [ 'كم سعر الراجحي', 'ما هي قيمة الراجحي؟', 'هل تعرف سعر أرامكو؟' ] {'سعر': 1, 'كم': 2, 'الراجحي': 3, 'ما': 4, 'هي': 5, 'قيمة': 6, 'الراجحي؟': 7, 'هل': 8, 'تعرف': 9, 'أرامكو؟': 10} its putting الراجحي and الراجحي? as two tokens, is that becuase of arabic?
Every step has been followed but keep getting this error "RuntimeError: Input tensor has type kTfLiteFloat32: it requires specifying NormalizationOpt ions metadata to preprocess input images." Raspberry Pi 4 Bullseye, 32-bit
Karmel are you sitting down while making this video? JW 😊
Obama!
❤😂🎉😢😅😮😅😊
Interesting
the following command fails in JupyterLab: file = files.upload()
This is so clear and easy to understand. Thank you, now I have a better understanding about NLP.
Palm pilots are back?
print(Hashing == Tokenization ) whats the output??
wow just wow
Start with random values, then the optimizer "will generate new parameters to see if it can do better". How? Either this glosses over the core of the solution, or there is no core and the answer is arrived at completely randomly. Not very clever, if so.
this is huge!!
token_list = tokenizer.texts_to_sequences([line])[0], why we put [line] instead of line simply here?
The "adam = Adam(lr=0.01) ValueError: Argument(s) not recognized: {'lr': 0.01}" can be fixed as follows: adam = Adam(learning_rate=0.01)
bless you
its not Win 11 USB plug and play and no driver self installation - the Ai is not shown in the Win 11 Taskmanager - if this comes in 11 it would be nice - also parallel Coral USB Sticks in use would be nice - USB 1,2,3, ... to geht more Ai power 🙂 ... on my Win 11 PC i have VM with a Linux Mint Cinnamon virtual PC - it would be fine if this Windows Ai would be pushed through into the Virtual Mint PC ... like my mouse, Soundblaster, Wifi as a Bridge ... and so on
Debugging
Perfect, Thanks for clarification of the new KerasCV library
hello today i saw this video i dont understand one things what is validation data ? i have to create a model for full face detection only face without any background