Pad_sequences смотреть последние обновления за сегодня на .

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How to use Tokenizer and also introduce Padded Sequences tool in NLP with Tensorflow and Keras. Simple examples in Python. The idea behind the pad sequences tool is that it allows you to use sentences of different lengths, and use padding or truncation to make all of the sentences the same length. Sequences longer than num_timesteps are truncated so that they fit the desired length. The position where padding or truncation happens is determined by the arguments padding and truncating, respectively. pad_sequences is used to ensure that all sequences in a list have the same length. By default this is done by padding 0 in the beginning of each sequence until each sequence has the same length as the longest sequence. Vytautas. #nlpython #nlptokenizers #NLPtensorflow

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In this video, you will learn about tokenizer in Tensorflow Other important playlists TensorFlow Tutorial:🤍 PyTorch Tutorial: 🤍 Python Tutorial: 🤍 Machine Learning: 🤍 Like, Subscribe, Follow, and Share YouTube: 🤍 Instagram: 🤍 Twitter: 🤍 Facebook: 🤍 Linktree: 🤍 #TensorFlow #Python #NLP

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【Tensorflow初学入门使用】统一数组维度 - tensorflow2, preprocessing, pad_sequences p.2

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How to resolve ImportError: cannot import name 'file_hash' from 'pooch.utils'

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محاضرات التعلم العميق الجزء العملي من المحاضرة الرابعة 0:00 Word embeddings تضمين الكلمات 13:15 word2vec ... skip gram 20:36 CBOW 23:40 example 1: Tokenizer, texts_to_sequences, pad_sequences 41:10 IMDB ratings dataset, embeddings layer 54:00 Irish lyrics, BiLSTM

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In this video, I explained what is Keras Tokenizer in Python. Then we talked about padding to make it ready for further analysis. Click "Show more" to learn more 👇:- What is Keras Tokenizer? Keras provides the Tokenizer class for preparing text documents. The Tokenizer must be constructed and then fit on either raw text documents or integer encoded text documents. Keras.preprocessing.text import Tokenizer... Keras Tokenizer is used to convert words into numbers. What is Padding? pad_sequences is used to make lists of the same length. It is achieved by putting the appropriate number of zeros before or after the sentence. 𝐒𝐮𝐩𝐩𝐨𝐫𝐭 𝐦𝐞 by: ☕ 𝐁𝐮𝐲ing 𝐌𝐞 𝐚 𝐂𝐨𝐟𝐟𝐞𝐞: 🤍 𝐆𝐞𝐭 𝐢𝐧𝐬𝐭𝐚𝐧𝐭 𝐮𝐩𝐝𝐚𝐭𝐞𝐬 𝐚𝐛𝐨𝐮𝐭 𝐭𝐡𝐞 𝐥𝐚𝐭𝐞𝐬𝐭 𝐯𝐢𝐝𝐞𝐨𝐬: ✉️ Join the Telegram Channel and be updated: 🤍 👔 Join the 𝐋𝐢𝐧𝐤𝐞𝐝𝐈𝐧 𝐆𝐫𝐨𝐮𝐩 for peer-to-peer discussion: 🤍 📺 Word embeddings with *KERAS* Embedding layer in Python: 🤍 𝐌𝐚𝐤𝐞 𝐬𝐮𝐫𝐞 𝐭𝐨 𝐬𝐮𝐛𝐬𝐜𝐫𝐢𝐛𝐞 𝐬𝐨 𝐲𝐨𝐮 𝐝𝐨𝐧'𝐭 𝐦𝐢𝐬𝐬 𝐨𝐮𝐭 𝐨𝐧 𝐦𝐲 𝐟𝐮𝐭𝐮𝐫𝐞 𝐯𝐢𝐝𝐞𝐨𝐬: ✅ 𝐒𝐮𝐛𝐬𝐜𝐫𝐢𝐛𝐞: 🤍 After subscribing, 𝐠𝐞𝐭 𝐟𝐫𝐞𝐞 𝐚𝐜𝐜𝐞𝐬𝐬 𝐭𝐨 𝐦𝐲 𝐆𝐨𝐨𝐠𝐥𝐞 𝐃𝐫𝐢𝐯𝐞: Follow steps on my YouTube Channel. 📸 Follow me on 𝐈𝐧𝐬𝐭𝐚𝐠𝐫𝐚𝐦: 🤍fp.ritvik : 🤍 👉🏼 Follow me on 𝐓𝐰𝐢𝐭𝐭𝐞𝐫: 🤍 👍 Like my 𝐅𝐚𝐜𝐞𝐛𝐨𝐨𝐤 𝐏𝐚𝐠𝐞 to be updated: 🤍 ⌚TimeStamps: Introduction (0:00) Keras Tokenizer Explained (1:12) Keras Tokenizer functions (4:30) Keras Tokenizer Padding (10:10) Outro (12:50) I used Anaconda jupyterlab for this code. It is an amazing platform for beginners. #fpritvik

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Comment and Subscribe!! how to add python path Open Explorer. Right-click 'Computer' in the Navigation Tree Panel on the left. Select 'Properties' at the bottom of the Context Menu. Select 'Advanced system settings' Click 'Environment Variables...' in the Advanced Tab. Under 'System Variables': Add. Every time I have had to set up a new language on my computer I always forget how to properly install the language properly! With this video, it is a reminder for me as well as all devs out there who may have forgotten how to add python and pip to PATH for the Windows 10 operating system. If you are a new dev learning python, this video will also instruct you how to get python and pip set up for PATH on Windows. If you want to know how to install python onto Windows in general I have a video for that here: 🤍

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Click here to subscribe - 🤍 ►Instagram - 🤍 Personal Facebook A/c - 🤍 Twitter - 🤍

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Делаем сентимент-анализ коротких высказываний с помощью рекуррентной сети на базе LSTM слоев в пакете Keras. Узнаете как готовить обучающую выборку, в каком формате ее представлять. Методы инструмента Tokenizer: fit_on_texts, texts_to_sequences. Функция pad_sequences для нормировки длины текстовых фрагментов. Класс слоя LSTM и что он из себя представляет. Создание и обучение рекуррентной нейронной сети, состоящей из двух LSTM-слоев. Телеграм-канал: 🤍 Инфо-сайт: 🤍 lesson 24. LSTM sentiment analysis.py: 🤍 LSTM: 🤍

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In this video, you can learn about how to upload your custom Text dataset after pre-processing on Deep Learning Studio. IMDB dataset of 25000 reviews has been used in this example with the below python script: from keras.preprocessing.text import Tokenizer from keras.preprocessing.sequence import pad_sequences import csv # First we will convert the text to sequence of integers text_file = open("reviews.txt", "r") lines = text_file.readlines() maxlen = 100 # We will cut reviews after 100 words training_samples = 200 # We will be training on 200 samples validation_samples = 10000 # We will be validating on 10000 samples max_words = 10000 # We will only consider the top 10,000 words in the dataset tokenizer = Tokenizer(num_words=max_words) tokenizer.fit_on_texts(lines) sequences = tokenizer.texts_to_sequences(lines) # Second we will pad the sequences to make same length for the rows sameLengthSequences = pad_sequences(sequences, maxlen=maxlen) # Third we will convert sequences to strings sequencesToStrings = [] for row in sameLengthSequences: sequencesToStrings.append(';'.join(str(col) for col in row)) # Fourth we will export the processed dataset into CSV file csvfile = "processed.csv" with open(csvfile, "w") as output: writer = csv.writer(output, lineterminator='\n') for val in sequencesToStrings: writer.writerow([val])

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In silico drug discovery approaches are becoming popular and cost-effective ways of identifying novel therapeutic candidates. In this learning experience, Akshay Bareja, D.Phil. will lead us through a demonstration of how to use deep learning to predict drug-protein interactions. Specifically, we will recreate a published model called "DeepDTA" that only uses drug and protein sequence data (in the form of SMILES strings and amino acid sequences respectively) to predict binding affinity.

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Un second exemple d'analyse de sentiment avec de l'embedding Toutes nos vidéos : 🤍 Le site de notre formation : 🤍 Ce second exemple utilise une couche d'embedding Keras. L'objectif est d'analyser des critiques de films issues de la base IMBD.

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مجتمع اللسانيات الحاسوبية | سؤال وجواب 🤍 قناة التليجرام 🤍

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Follow along with me and code in Python using Pandas and Tensorflow. We will predict the TripAdvisor star rating of hotels from the description left in the review. In this project the recurrent layer SimpleRNN was used but the LSTM or GRU layer can be used as well. We will use the text from the review to predict which star rating the left. We will use WordCloud to complete EDA on the text data. Also important preprocessing steps like treating punctuation, stemming of verbs using PorterStemmer, and using Tensorflows text_to_sequences and pad_sequences to get our data ready for our neural network. check out more data videos datasimple.education/blog/ 🤍ion/post/nlp-sentiment-prediction-tensorflow-guided-project-predict-rating-from-tripadvisor-reviews/ Use the link for a discount 30% on first class with Data Science Teacher Brandyn or any language teacher 🤍 Send me a message if you have any questions 🤍 Showcase your DataArt 🤍 Python data analysis group, share your analysis 🤍 Machine learning in sklearn group 🤍 Join the deep learning with tensorflow for more info 🤍

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Machine Learning Foundations is a free training course where you’ll learn the fundamentals of building machine learned models using TensorFlow. In Episode 10, we’re going to put our knowledge of how to preprocess text data to use and train a text classifier to detect sarcasm in news headlines. Sarcasm in News Headlines Dataset by Rishabh Misra → 🤍 Sarcasm detector Colab 1 → 🤍 Sarcasm detector Colab 2 → 🤍 TensorFlow is Google’s end-to-end open source machine learning platform. For more videos about TensorFlow, subscribe to the TF YouTube channel → 🤍 Machine Learning Foundations playlist → 🤍 Subscribe to Google Developers → 🤍

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This is it, this is a day in the life of a data scientist. It's all I do most of the time. Managing to maintain focus over extended working hours is crucial to mastering any topic that may seem impossible at first. Machine Learning is one of those area that seem impossible until you put in the effort. Then many concepts become trivial as you dive deeper and gain a more solid understanding of the field. 🎁 1 MONTH FREE TRIAL! Financial and Alternative Datasets for today's Data Analysts & Scientists: 🤍 📚 RECOMMENDED DATA SCIENCE BOOKS: 🤍 ✅ Subscribe and support us: 🤍 💻 Data Science resources I strongly recommend: 🤍 🌐 Let's connect: 🤍 - At DecisionForest we serve both retail and institutional investors by providing them with the data necessary to make better decisions: 🤍 #DecisionForest

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In this video I'm creating a baseline NLP model for Text Classification with the help of Embedding and LSTM layers from TensorFlow's high-level API Keras. 00:00 NLP with TensorFlow 00:48 How to clean text data for machine learning 01:56 How to count the occurences of each word in a corpus 03:40 Why we need to define the sequence length for NLP Projects with Tensorflow 04:00 How to split the dataset into a train and test set 04:42 How to use Tokenizer from Keras to index words and transform text to sequences 05:49 How to pad text sequences to have a specific length for NLP Projects with Tensorflow 08:15 LSTM Model for NLP Projects with Tensorflow 08:25 Understanding Embedding and why we need to use it for NLP Projects With Embedding, we map each word to a vector of fixed size with real-valued elements. In contrast to one hot encoding, we can use finite sized vectors to represent an infinite number of real numbers. This feature learning technique can learn the most important features to represent the words in the data. LSTMs are Recurrent Neural Networks (RNN) used for modeling sequences. LSTM units have a memory cell as the building block and it represents the hidden layer. In an LSTM cell there are three different types of gates: the forget gate, the input gate and the output gate. The most important one, the forget gate allows the LSTM memory cell to reset the cell state. The forget gate decides which information is allowed to go through and which to hold back. - You can access the Jupyter notebook here (login required): 🤍 🎁 1 MONTH FREE TRIAL! Financial and Alternative Datasets for today's Data Analysts & Scientists: 🤍 📚 RECOMMENDED DATA SCIENCE BOOKS: 🤍 ✅ Subscribe and support us: 🤍 💻 Data Science resources I strongly recommend: 🤍 🌐 Let's connect: 🤍 - At DecisionForest we serve both retail and institutional investors by providing them with the data necessary to make better decisions: 🤍 #DecisionForest

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15.03.2020

This video shows: - Popular BERT implementation - Creating Custom Datasets and using it in DataLoader - Tokenization using BERT tokenizer - Using pad_sequence to make it of the same length This series will follow the book Transformers for Natural Language Processing (🤍 and will contain the exercises/experiments contained in the book. If you want to look and experiment with a fine-tuned German BERT language model on a medical domain, it is available here (🤍 To learn about BERT tokenization using PyTorch and Huggingface, please visit here: 🤍 To stay tuned, please subscribe here: 🤍

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10.07.2022

Do you struggle finding the right emoji at right time? Why don't we make an ML model to suggest us the emoji while typing? In this video, we will make Emoji Prediction in Tensorflow. The main aim of this video, is help you understand how to use LSTM in Tensorflow and also to use Word Embeddings. This video will teach you implementing LSTM in Tensorflow, RNN in Tensorflow, and Word Embeddings in Python. I covered the working details of LSTM in my previous video, which you can find here: 🤍 Also, the detailed explanation of word embeddings can be found here: 🤍 ➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖ 📕 Complete Code and Dataset: 🤍 ➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖ Timestamps: 0:00 Intro 1:23 Video overview 2:37 LSTM in Tensorflow 23:57 Making predictions 27:08 End ➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖ Follow my entire playlist on Recurrent Neural Network (RNN) : 📕 RNN Playlist: 🤍 ➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖ ✔ CNN Playlist: 🤍 ✔ Complete Neural Network: 🤍 ✔ Complete Logistic Regression Playlist: 🤍 ✔ Complete Linear Regression Playlist: 🤍 ➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖ If you want to ride on the Lane of Machine Learning, then Subscribe ▶ to my channel here: 🤍

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Today in this part we have built Inference model for our Chatbot model/ seq2seq model for predictions. Inference model is a model used to make predictions from our encoder decoder model. We used simple encoder decoder model to train our chatbot We haven't used attention yet but in later video we might build attention too. code in this video : 🤍 🟥 you might find some errors while running the code shown in video that's all my fault I'm sorry for that. so that's why I've uploaded another no error code above 🔝 I'll soon make video on that so keep subscribed for more videos. In this series we are building a chatbot from scratch and step by step and using cornell movie dataset for that.we use seq2seq encoder decoder model and we will use keras functional approach to build this model. Playlist : 🤍 STAY TUNED FOR MORE VIDEOS. 🙂 * Readings embedding : 🤍 return sequences vs return state : 🤍 code on github : 🤍 code on kaggle : 🤍 dataset : 🤍 LIKE 👍🏼 + SHARE + SUBSCRIBE = support :) website : 🤍 LIKE + SHARE + SUBSCRIBE = support :) contact me : 📞 facebook : 🤍 twitter 🐦 : 🤍 github : 🤍 discord : 🤍 linkedin : 🤍 youtube ❤️: 🤍 Atlast if you have any queries or error in your installation feel free to ask them in below comment section. queries: chatbot using python machine learning, [22, chatbot python machine learning, {google:suggestsubtypes:[[], chatbot python machine learning,22,30], 30]], how to create a chatbot in python using machine learning, [], how to create a chatbot in python using machine learning github], 30], seq2seq,keras,chatbot,from scratch,cornell movie dataset,encoder decoder keras,chatbot seq2seq model,functional keras api,deep learning,sequence to sequence,neural networks,

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celebrating our 1 year engagement and 8 years dating anniversary!!! So lucky to be with Kyle and have a little baby on the way, I love celebrating these special days! telling my fiancé I'm pregnant 🤍 how I found out I was pregnant + symptoms 🤍 ttc video - clomid and pcos 🤍 moving vlog 🤍 LETS BE FRIENDS~ 🤍chelseanicolecrouch 🤍 tiktok 🤍 Pinterest: 🤍 #highschoolsweethearts

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28.05.2021

In this video, you will learn about padding in Tensorflow Other important playlists TensorFlow Tutorial:🤍 PyTorch Tutorial: 🤍 Python Tutorial: 🤍 Machine Learning: 🤍 Like, Subscribe, Follow, and Share YouTube: 🤍 Instagram: 🤍 Twitter: 🤍 Facebook: 🤍 Linktree: 🤍 #TensorFlow #Padding #Python

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29.09.2021

🔥Edureka Tensorflow Training: 🤍 This Edureka LSTM Explained video will help you in understanding why we need Recurrent Neural Networks (RNN) and what exactly it is. It also explains few issues with training a Recurrent Neural Network and how to overcome those challenges using LSTMs. 00:00:00 Introduction 00:00:16 Agenda 00:02:37 What is natural language processing? 00:04:04 What are the ways to process text data? 00:05:49 Recurrent neural networks 00:12:34 Long short- Term Memory 00:17:09 Long short- Term Memory Use Cases 00:18:14 Real Time Applications of LSTM 00:24:15 Hands-On 🔹Check our complete Deep Learning With TensorFlow playlist here: 🤍 🔹Check our complete Deep Learning With TensorFlow Blog Series: 🤍 🔴Do subscribe to our channel and hit the bell icon to never miss an update from us in the future: 🤍 Edureka Community: 🤍 Instagram: 🤍 Facebook: 🤍 Twitter: 🤍 LinkedIn: 🤍 Telegram: 🤍 SlideShare: 🤍 Meetup: 🤍 #Edureka #DeepLearningEdureka #LSTMExplained #NeuralNetworks #DeepLearningTraining #DeepearningTutorial #EdurekaTraining -Edureka Post Graduate Courses- 🔵 Artificial Intelligence and Machine Learning PGD: 🤍 How it Works? 1. This is 21 hrs of Online Live Instructor-led course. Weekend class: 7 sessions of 3 hours each. 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 have to undergo a 2-hour LIVE Practical Exam based on which we will provide you a Grade and a Verifiable Certificate! - - - - - - - - - - - - - - About the Course Edureka's Deep learning with Tensorflow course will help you to learn the basic concepts of TensorFlow, the main functions, operations and the execution pipeline. Starting with a simple “Hello Word” example, throughout the course you will be able to see how TensorFlow can be used in curve fitting, regression, classification and minimization of error functions. This concept is then explored in the Deep Learning world. You will evaluate the common, and not so common, deep neural networks and see how these can be exploited in the real world with complex raw data using TensorFlow. In addition, you will learn how to apply TensorFlow for backpropagation to tune the weights and biases while the Neural Networks are being trained. Finally, the course covers different types of Deep Architectures, such as Convolutional Networks, Recurrent Networks and Autoencoders. Delve into neural networks, implement Deep Learning algorithms, and explore layers of data abstraction with the help of this Deep Learning with TensorFlow course. - - - - - - - - - - - - - - Who should go for this course? The following professionals can go for this course: 1. Developers aspiring to be a 'Data Scientist' 2. Analytics Managers who are leading a team of analysts 3. Business Analysts who want to understand Deep Learning (ML) Techniques 4. Information Architects who want to gain expertise in Predictive Analytics 5. Professionals who want to captivate and analyze Big Data 6. Analysts wanting to understand Data Science methodologies However, Deep learning is not just focused to one particular industry or skill set, it can be used by anyone to enhance their portfolio. - - - - - - - - - - - - - - Why Learn Deep Learning With TensorFlow? TensorFlow is one of the best libraries to implement Deep Learning. TensorFlow is a software library for numerical computation of mathematical expressions, using data flow graphs. Nodes in the graph represent mathematical operations, while the edges represent the multidimensional data arrays (tensors) that flow between them. It was created by Google and tailored for Machine Learning. In fact, it is being widely used to develop solutions with Deep Learning. For more information, please write back to us at sales🤍edureka.co or call us at IND: 9606058406 / US: 18338555775 (toll-free). Facebook: 🤍 Twitter: 🤍 LinkedIn: 🤍

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31.12.2021

🔥Edureka Tensorflow Training: 🤍 This Edureka LSTM Explained video will help you in understanding why we need Recurrent Neural Networks (RNN) and what exactly it is. It also explains few issues with training a Recurrent Neural Network and how to overcome those challenges using LSTMs. 00:00 Introduction 00:36 Agenda 00:47 Introduction to NLP 01:46 Ways to Process Text Data 02:57 Recurrent Neural Networks 22:02 Long Short-term Memory 51:46 LSTM Use Cases 53:45 Real Time Applications of LSTM 🔹Check our complete Deep Learning With TensorFlow playlist here: 🤍 🔹Check our complete Deep Learning With TensorFlow Blog Series: 🤍 🔴Do subscribe to our channel and hit the bell icon to never miss an update from us in the future: 🤍 Edureka Community: 🤍 Instagram: 🤍 Facebook: 🤍 Twitter: 🤍 LinkedIn: 🤍 Telegram: 🤍 SlideShare: 🤍 Meetup: 🤍 #Edureka #DeepLearningEdureka #LSTMExplained #NeuralNetworks #DeepLearningTraining #DeepearningTutorial #EdurekaTraining How it Works? 1. This is 21 hrs of Online Live Instructor-led course. Weekend class: 7 sessions of 3 hours each. 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 have to undergo a 2-hour LIVE Practical Exam based on which we will provide you a Grade and a Verifiable Certificate! - - - - - - - - - - - - - - About the Course Edureka's Deep learning with Tensorflow course will help you to learn the basic concepts of TensorFlow, the main functions, operations and the execution pipeline. Starting with a simple “Hello Word” example, throughout the course you will be able to see how TensorFlow can be used in curve fitting, regression, classification and minimization of error functions. This concept is then explored in the Deep Learning world. You will evaluate the common, and not so common, deep neural networks and see how these can be exploited in the real world with complex raw data using TensorFlow. In addition, you will learn how to apply TensorFlow for backpropagation to tune the weights and biases while the Neural Networks are being trained. Finally, the course covers different types of Deep Architectures, such as Convolutional Networks, Recurrent Networks and Autoencoders. Delve into neural networks, implement Deep Learning algorithms, and explore layers of data abstraction with the help of this Deep Learning with TensorFlow course. - - - - - - - - - - - - - - Who should go for this course? The following professionals can go for this course: 1. Developers aspiring to be a 'Data Scientist' 2. Analytics Managers who are leading a team of analysts 3. Business Analysts who want to understand Deep Learning (ML) Techniques 4. Information Architects who want to gain expertise in Predictive Analytics 5. Professionals who want to captivate and analyze Big Data 6. Analysts wanting to understand Data Science methodologies However, Deep learning is not just focused to one particular industry or skill set, it can be used by anyone to enhance their portfolio. - - - - - - - - - - - - - - Why Learn Deep Learning With TensorFlow? TensorFlow is one of the best libraries to implement Deep Learning. TensorFlow is a software library for numerical computation of mathematical expressions, using data flow graphs. Nodes in the graph represent mathematical operations, while the edges represent the multidimensional data arrays (tensors) that flow between them. It was created by Google and tailored for Machine Learning. In fact, it is being widely used to develop solutions with Deep Learning. For more information, please write back to us at sales🤍edureka.co or call us at IND: 9606058406 / US: 18338555775 (toll-free). Facebook: 🤍 Twitter: 🤍 LinkedIn: 🤍

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In this video we go through a bit more in depth into custom datasets and implement more advanced functions for dealing with text. Specifically we're looking at a image captioning dataset (Flickr8k data set) with an image and a corresponding caption text that describes what's going on in the image. I think the general principles from this video can be utilized to any project you're working with when dealing with text data be it either translation, question answering, sentiment analysis etc. I also recommend taking a look at my Torchtext which can also be quite helpful and simplify the data loading process. ✅ Support My Channel Through Patreon: 🤍 Flickr8k Dataset used in the video: 🤍 PyTorch Playlist: 🤍 Github repository: 🤍 OUTLINE: 0:00 - Introduction 2:05 - Overview of what we're going to do 4:05 - Imports 5:20 - Setup of Pytorch Dataset for loading Flickr 11:50 - Setup of Vocabulary and Numericalization 22:19 - Creating Collate for Padding of Batch 25:20 - Function for getting data loader 29:15 - Running code & fixing couple of errors 33:09 - Ending

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00:55:53

06.02.2021

hey everyone This 55 minute long video take you through how to create deep learning chatbot using keras liberary. Uses lstm neural network cells to create it. from Data preprocessing to building inference in just single video. So enjoy :) you me customise this chatbot by using different data set of whichever field you want to build chatbot of. chatbot are widely used nowadays specially in this covid era. so if you know how to build a chatbot you may use it for your own business purposes. in this era what we have used keras framework and LCYM cells. code in this video : 🤍 run it on kaggle : 🤍 so keep subscribed for more videos. In this series we are building a chatbot from scratch and step by step and using cornell movie dataset for that.we use seq2seq encoder decoder model and we will use keras functional approach to build this model. Playlist : 🤍 STAY TUNED FOR MORE VIDEOS. 🙂 timestamps 0:00 - What we'll going to build 2:06 - All about dataset 4:12 - loading dataset 6:48 - data preprocessing 12:30 - creating list of questions and answers 16:55 - more preprocess for qna 19:10 - cleaning texts 24:53 - creating VOCABULARY 27:40 - adding sos and eos 32:40 - creating encoder inputs 35:40 - creating decoder inputs 38:16 - creating decoder outputs 39:54 - understanding and creating encoder decoder model 46:50 - creating inference model 55:00 - quick testing * Readings embedding : 🤍 return sequences vs return state : 🤍 code on github : 🤍 code on kaggle : 🤍 dataset : 🤍 LIKE 👍🏼 + SHARE + SUBSCRIBE = support :) website : 🤍 LIKE + SHARE + SUBSCRIBE = support :) contact me : 📞 facebook : 🤍 twitter 🐦 : 🤍 github : 🤍 discord : 🤍 linkedin : 🤍 youtube ❤️: 🤍 Atlast if you have any queries or error in your installation feel free to ask them in below comment section. queries: chatbot using python machine learning, [22, chatbot python machine learning, {google:suggestsubtypes:[[], chatbot python machine learning,22,30], 30]], how to create a chatbot in python using machine learning, [], how to create a chatbot in python using machine learning github], 30], seq2seq,keras,chatbot,from scratch,cornell movie dataset,encoder decoder keras,chatbot seq2seq model,functional keras api,deep learning,sequence to sequence,neural networks,

499

9

2

01:53:54

23.05.2020

Deep Learning Adventures - TensorFlow In Practice - Session 6 Join us for our 6th adventure in Deep Learning! Just bring your curiosity and be ready to meet our growing community 😀We are taking Course 3 of TensorFlow in Practice Specialization available at: 🤍 Agenda: - Introductions / Social conversations - Course 3 Week 1 - Sentiment in text The first step in understanding sentiment in text, and in particular when training a neural network to do so is the tokenization of that text. This is the process of converting the text into numeric values, with a number representing a word or a character. Join us as we learn about the Tokenizer and pad_sequences APIs in TensorFlow and how they can be used to prepare and encode text and sentences to get them ready for training neural networks! - Course Week 2 - Word Embeddings Join us on this session to learn about Embeddings, where tokens (converted words) are mapped as vectors in a high dimension space. With Embeddings and labelled examples, these vectors can then be tuned so that words with similar meaning will have a similar direction in the vector space. This is needed in training a neural network to understand sentiment in text and we will begin by looking at movie reviews, training a neural network on texts that are labelled 'positive' or 'negative' and determining which words in a sentence drive those meanings. - Deep Learning YouTube recordings, please subscribe 😀 🤍 - Deep Learning Adventures - Presentation 4: 🤍 - Deep Learning Trivia Game #4, feel free to practice, our live Trivia night is scheduled for Friday 6/5: 🤍 - Spread the word about our meetup 🎉 #deeplearning #naturallanguageprocessing #nlp #sentiment #text #wordembeddings #tensorflow #coursera #fun #deeplearningtrivia 🤍lmoroney 🤍AndrewYNg

3288

57

3

00:56:12

05.11.2021

🔥Edureka Tensorflow Training: 🤍 This Edureka LSTM Explained video will help you in understanding why we need Recurrent Neural Networks (RNN) and what exactly it is. It also explains few issues with training a Recurrent Neural Network and how to overcome those challenges using LSTMs. 🔹Check our complete Deep Learning With TensorFlow playlist here: 🤍 🔹Check our complete Deep Learning With TensorFlow Blog Series: 🤍 🔴Do subscribe to our channel and hit the bell icon to never miss an update from us in the future: 🤍 Edureka Community: 🤍 Instagram: 🤍 Facebook: 🤍 Twitter: 🤍 LinkedIn: 🤍 Telegram: 🤍 SlideShare: 🤍 Meetup: 🤍 #Edureka #DeepLearningEdureka #LSTMExplained #NeuralNetworks #DeepLearningTraining #DeepearningTutorial #EdurekaTraining How it Works? 1. This is 21 hrs of Online Live Instructor-led course. Weekend class: 7 sessions of 3 hours each. 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 have to undergo a 2-hour LIVE Practical Exam based on which we will provide you a Grade and a Verifiable Certificate! - - - - - - - - - - - - - - About the Course Edureka's Deep learning with Tensorflow course will help you to learn the basic concepts of TensorFlow, the main functions, operations and the execution pipeline. Starting with a simple “Hello Word” example, throughout the course you will be able to see how TensorFlow can be used in curve fitting, regression, classification and minimization of error functions. This concept is then explored in the Deep Learning world. You will evaluate the common, and not so common, deep neural networks and see how these can be exploited in the real world with complex raw data using TensorFlow. In addition, you will learn how to apply TensorFlow for backpropagation to tune the weights and biases while the Neural Networks are being trained. Finally, the course covers different types of Deep Architectures, such as Convolutional Networks, Recurrent Networks and Autoencoders. Delve into neural networks, implement Deep Learning algorithms, and explore layers of data abstraction with the help of this Deep Learning with TensorFlow course. - - - - - - - - - - - - - - Who should go for this course? The following professionals can go for this course: 1. Developers aspiring to be a 'Data Scientist' 2. Analytics Managers who are leading a team of analysts 3. Business Analysts who want to understand Deep Learning (ML) Techniques 4. Information Architects who want to gain expertise in Predictive Analytics 5. Professionals who want to captivate and analyze Big Data 6. Analysts wanting to understand Data Science methodologies However, Deep learning is not just focused to one particular industry or skill set, it can be used by anyone to enhance their portfolio. - - - - - - - - - - - - - - Why Learn Deep Learning With TensorFlow? TensorFlow is one of the best libraries to implement Deep Learning. TensorFlow is a software library for numerical computation of mathematical expressions, using data flow graphs. Nodes in the graph represent mathematical operations, while the edges represent the multidimensional data arrays (tensors) that flow between them. It was created by Google and tailored for Machine Learning. In fact, it is being widely used to develop solutions with Deep Learning. For more information, please write back to us at sales🤍edureka.co or call us at IND: 9606058406 / US: 18338555775 (toll-free). Facebook: 🤍 Twitter: 🤍 LinkedIn: 🤍

8530

228

47

00:21:50

06.09.2020

In this NLP tutorial with Python we'll use TensorFlow's Keras to classify text with the help of GloVe Word Embeddings. GloVe (Global Vectors) is an unsupervised learning algorithm for obtaining vector representations for words. Training is performed on aggregated global word-word co-occurrence statistics from a corpus, and the resulting representations showcase interesting linear substructures of the word vector space. The main intuition underlying the model is the simple observation that ratios of word-word co-occurrence probabilities have the potential for encoding some form of meaning. The training objective of GloVe is to learn word vectors such that their dot product equals the logarithm of the words' probability of co-occurrence. We are going to use a 100 dimensional GloVe pre-trained corpus model to represent our words, trained on Twitter data (2B tweets, 27B tokens, 1.2M vocab). You can access the Jupyter notebook here (login required): 🤍 How To Remove StopWords, Punctuation, Emojis and HTML from Strings with Regex: 🤍 GloVe Project Page: 🤍 ✅ Subscribe and support us: 🤍 🌐 Let's connect: 🤍 📚 Data Science resources I strongly recommend: 🤍 If there are any other resources that you want us to add leave your comments below, thanks. - At DecisionForest, we work with business leaders to identify integrated AI strategies that they can leverage in their business. One of the biggest challenges facing businesses is knowing where and how to invest into AI and Machine Learning. We help them find opportunities and obtain a competitive edge through these business models of the future. 🤍 #DecisionForest

205

7

0

00:16:49

15.06.2020

This is part V of series of tutorials about Jupyter Notebooks and Jupyter Lab. This is a companion for the Notebook Tutorial: "Introduction to Jupyter Notebooks: set-up, user-guide, and best practices" 🤍 Which is part I of mine: "Introduction to Scientific Computing with Python - Mini-workshop" 🤍 In this tutorial we will learn: - Jupyter Notebook Weaknesses Why: - Out of order execution is hard - Modularization is hard - Refactoring and debugging is hard My Twitter account : 🤍 My GitHub account: 🤍 #Python #JupyterNotebooks #IDE #DataScience

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1797

199

00:08:05

15.05.2020

Through this series so far you’ve been learning the basics of NLP using TensorFlow. You saw how to tokenize and then sequence text, preparing it to train neural networks. You saw how sentiment in text can be represented with embeddings, and how the semantics of text over long stretches might be learned using recurrent neural networks and LSTMs. In this video we’ll put all of that together into a fun scenario creating a model and training it on the lyrics to traditional Irish songs. Irish songs generator Colab → 🤍 Predict Shakespeare with Cloud TPUs and Keras → 🤍 NLP Zero to Hero playlist → 🤍 Subscribe to the TensorFlow channel → 🤍

3563

73

2

00:18:04

22.04.2020

الكود 🤍 هذه المحاضرة هي جزء من سلسلة محاضرات التعلم العميق Deep Learning يمكنك مشاهدة جميع الحلقات هنا 🤍 و يمكنك متابعتنا علي الصفحة الخاصة بنا علي الفيس بوك 🤍 كما يمكنك الانضمام للمجموعة الخاصة بنا هنا 🤍

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146

45

00:29:50

20.01.2021

AI applications with deep neural networks for NLP. Github link: 🤍 For citation as reference in a research paper, use: Reference: Rai BK, (2019). “Advanced Deep Learning with R: Become an expert at designing, building, and improving advanced neural network models using R”, Packt Publishing. #DellInsideCircle #ArtificialIntelligence #MachineLearning #DeepLearning Timestamps: 00:00 Recurrent Neural Networks 00:22 Examples of application involving sequences 00:39 LSTM Networks 02:23 IMDb 03:54 LSTM with R 04:18 Library and Data 08:48 Padding and Truncation 11:54 Recurrent Neural Network 15:06 Compile 15:22 Fit Model 16:25 Prediction and Confusion Matrix 17:12 Improving RNN Model 22:12 LSTM Model 25:18 Improving LSTM Model 27:33 Bidirectional LSTM Model NVIDIA-Powered Data Science Workstation: 🤍 Deep Learning with TensorFlow: 🤍 This video uses Dell's Mobile Data Science Workstation Precision 7750 with NVIDIA Quadro RTX 5000. I'll like to thank Dell and Nvidia for loaning this powerful laptop to support my deep learning and artificial intelligence related projects.

3187

42

3

00:55:30

21.10.2021

🔥Edureka Tensorflow Training: 🤍 This Edureka LSTM Explained video will help you in understanding why we need Recurrent Neural Networks (RNN) and what exactly it is. It also explains few issues with training a Recurrent Neural Network and how to overcome those challenges using LSTMs. Topics covered in this session: 00:00:00 Introduction 00:00:21 Agenda 00:00:42 Introduction to NLP 00:01:44 Ways to Process Text Data 00:02:55 Recurrent Neural Networks 00:22:00 Long Short-term Memory 00:51:35 LSTM Use Cases 00:53:39 Real Time Applications of LSTM 🔹Check our complete Deep Learning With TensorFlow playlist here: 🤍 🔹Check our complete Deep Learning With TensorFlow Blog Series: 🤍 🔴Do subscribe to our channel and hit the bell icon to never miss an update from us in the future: 🤍 Edureka Community: 🤍 Instagram: 🤍 Facebook: 🤍 Twitter: 🤍 LinkedIn: 🤍 Telegram: 🤍 SlideShare: 🤍 Meetup: 🤍 #Edureka #DeepLearningEdureka #LSTMExplained #NeuralNetworks #DeepLearningTraining #DeepearningTutorial #EdurekaTraining How it Works? 1. This is 21 hrs of Online Live Instructor-led course. Weekend class: 7 sessions of 3 hours each. 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 have to undergo a 2-hour LIVE Practical Exam based on which we will provide you a Grade and a Verifiable Certificate! - - - - - - - - - - - - - - About the Course Edureka's Deep learning with Tensorflow course will help you to learn the basic concepts of TensorFlow, the main functions, operations and the execution pipeline. Starting with a simple “Hello Word” example, throughout the course you will be able to see how TensorFlow can be used in curve fitting, regression, classification and minimization of error functions. This concept is then explored in the Deep Learning world. You will evaluate the common, and not so common, deep neural networks and see how these can be exploited in the real world with complex raw data using TensorFlow. In addition, you will learn how to apply TensorFlow for backpropagation to tune the weights and biases while the Neural Networks are being trained. Finally, the course covers different types of Deep Architectures, such as Convolutional Networks, Recurrent Networks and Autoencoders. Delve into neural networks, implement Deep Learning algorithms, and explore layers of data abstraction with the help of this Deep Learning with TensorFlow course. - - - - - - - - - - - - - - Who should go for this course? The following professionals can go for this course: 1. Developers aspiring to be a 'Data Scientist' 2. Analytics Managers who are leading a team of analysts 3. Business Analysts who want to understand Deep Learning (ML) Techniques 4. Information Architects who want to gain expertise in Predictive Analytics 5. Professionals who want to captivate and analyze Big Data 6. Analysts wanting to understand Data Science methodologies However, Deep learning is not just focused to one particular industry or skill set, it can be used by anyone to enhance their portfolio. - - - - - - - - - - - - - - Why Learn Deep Learning With TensorFlow? TensorFlow is one of the best libraries to implement Deep Learning. TensorFlow is a software library for numerical computation of mathematical expressions, using data flow graphs. Nodes in the graph represent mathematical operations, while the edges represent the multidimensional data arrays (tensors) that flow between them. It was created by Google and tailored for Machine Learning. In fact, it is being widely used to develop solutions with Deep Learning. For more information, please write back to us at sales🤍edureka.co or call us at IND: 9606058406 / US: 18338555775 (toll-free). Facebook: 🤍 Twitter: 🤍 LinkedIn: 🤍

21

4

0

01:46:35

29.12.2020

I have been trying to learn Natural Language Processing for a while now but didn't find the discipline. So starting this live stream of watching Stanford's NLP course. Learn along with me! || O R I G I N A L C O U R S E || Video: 🤍 Materials: 🤍 || C O M M E N T || Got any questions? Want to share your thoughts? Write a comment to start a discussion or reach out to me on Social Media. || F O L L O W || 🤍 || M U S I C || Infraction [No Copyright Music] 🤍 || T A G S || #deeplearning #nlp #stanford #learning

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209

33

00:13:08

10.12.2020

Как работают блоки GRU (Gated Recurrent Units) рекуррентных нейронных сетей. Подробный анализ их архитектуры. Пример реализации слоя GRU в пакете Keras для задачи сентимент-анализа коротких высказываний. Управляемые рекуррентные блоки. Телеграм-канал: 🤍 Инфо-сайт: 🤍 lesson 25. GRU sentiment analysis.py: 🤍 Фильтр Калмана: 🤍 GRU: 🤍

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