Part 2 | Python | Training Word Embeddings | Word2Vec |

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  • Опубликовано: 25 дек 2024

Комментарии • 50

  • @Janeilliams
    @Janeilliams 4 месяца назад +1

    Real world applications of these like sentence transformers embeddings model, which we mostly use for embedding generation and semantic search, this tutorial is great and helps us to grasp and picture the workings behind a word embeddings models. This is a great demonstration , really great. Thank you for your time and making such content, especially these scratch ones. Thanks Man, please keep going.

    • @MachineLearningWithJay
      @MachineLearningWithJay  2 месяца назад +1

      Glad to help! I have started uploading videos on transformers, and more coming soon! Let me know how they are, would appreciate the feedback.

  • @freedmoresidume
    @freedmoresidume 2 года назад +2

    Am happy that I found this channel. You have a gift of teaching.

    • @MachineLearningWithJay
      @MachineLearningWithJay  2 года назад

      Thank you! It means a lot to me 😇

    • @akshaysaxena7920
      @akshaysaxena7920 Год назад

      ​@@MachineLearningWithJayHello, I found your video very informative and clear fundamentals. Just a quick question, what if I have a corpus of total 37million sentences and 150 thousand unique words. How do I train my model without going into complexity of having 150thousand nodes in input for one_hot encoding?

  • @Kapilwankhede22
    @Kapilwankhede22 7 месяцев назад +1

    Thanks jay, the whole playlist is awesome.. Thank you so much for the creating these wonderful videos and educating us...

  • @ribamarsantarosa4465
    @ribamarsantarosa4465 Год назад +2

    When I see 13k subscribers; 2.5k views and only 85 likes I see why good programmers and good data scientists are rare. You can't get a more concise hands on presentation that teaches you how to create a language model.

  • @sampathcse16
    @sampathcse16 5 месяцев назад

    Super good explanation. Very indepth insights given. Thank you for taking time and explaining it

  • @vigneshmathivanan6052
    @vigneshmathivanan6052 2 года назад +1

    Very Informative. Thanks for doing this.

  • @matousmacak7467
    @matousmacak7467 7 месяцев назад

    Thank you for the video as well as the whole code 😊 You could set a random seed to obtain always the same randomly initialized weights.

  • @ishwaragoudapatil9654
    @ishwaragoudapatil9654 4 месяца назад +1

    Crisp n Clear. Thanks

  • @karunesharora3302
    @karunesharora3302 Год назад +2

    good and understandable explanation. It would be great if you may upload a video wherein character level embeddings are also learnt where we may split a word in characters- and do the process.

  • @abcdedcba561
    @abcdedcba561 6 месяцев назад +2

    When creating the bigrams (for each of two adjacent words), the order should matter. But why you insert all the possible combinations in the bigrams list?
    I think the order of the words as it appear in the corpus is important to capture the relationship between adjacent words.

  • @mustafatuncer4780
    @mustafatuncer4780 Год назад

    I really liked. Wonderful tutorial.

  • @NkembehBenjaminSCC
    @NkembehBenjaminSCC 6 месяцев назад

    thanks i really enjoy the playlist

  • @ralphconstable1199
    @ralphconstable1199 14 дней назад +1

    Thank you!

  • @fersilvil
    @fersilvil Год назад

    Thank you for your teaching

  • @مرتضىباسممحمد-ك4ف
    @مرتضىباسممحمد-ك4ف 2 года назад +1

    thanks , that was very useful

  • @maryamyuusufi7703
    @maryamyuusufi7703 Месяц назад +1

    thank you

  • @pavanparvathanenii4471
    @pavanparvathanenii4471 2 года назад +1

    man you are doing really well. you stopped uploading videos on machine learning algorithms. when will u resume bro.

    • @MachineLearningWithJay
      @MachineLearningWithJay  2 года назад

      Hi Pavan, sorry for not uploading videos. I will upload the next video very soon in the upcoming few days. But might take some time to upload more other videos. Thanks for supporting the channel. And I am glad you find my channel useful 🙂

  • @Movies-iz5em
    @Movies-iz5em Год назад

    wonderful explanation, please attach your linkedin profile in YT about section

  • @aliathar891
    @aliathar891 Год назад

    hello bro, when we apply cosine similarity function?

  • @lilitkharatyan2372
    @lilitkharatyan2372 10 месяцев назад

    Is this feasible for a very large size vocabulary?

  • @DHAtEnclaveForensics
    @DHAtEnclaveForensics 9 месяцев назад +1

    unique_words = set(filtered_data.flat)

  • @alantan3004
    @alantan3004 Год назад

    How do you make a prediction after u trained your own model ?

  • @ernestmodise4953
    @ernestmodise4953 6 месяцев назад

    Hi Jay "Don't laugh at me, I have always been programing of a external compiler (Java / Matlab) - After launching your github template, the jupyter blank code is not in edit mode, what must I do

  • @michaelvangulik85
    @michaelvangulik85 2 года назад +1

    Hello, sir, and same to you. Thank you for this video, I show you the same. India is now Bhaarat Aatmanirbhar and have popular searching engines looking for talent to engage! Indian is in need of popular searching engines and any help is good.

  • @ameybikram5781
    @ameybikram5781 Год назад

    King is appearing as a target word twice so two vectors will be created for king ? And then we take average????

  • @MrJdude39
    @MrJdude39 8 месяцев назад

    You never stated what Python packages are needed to do the creation of the bigrams, the tokenization, and the removal of stopwords. Are you using NLTK?

    • @matousmacak7467
      @matousmacak7467 7 месяцев назад +1

      He coded his own function. See the whole code in Jupiter notebook provided in the description

  • @AnkitGupta-fm7pd
    @AnkitGupta-fm7pd Год назад +1

    You are using wrong weights for plotting. weights[0] refers to the weights of the first layer, weights[1] refers to the biases of the first layer, weights[2] refers to the weights of the second layer, and weights[3] refers to the biases of the second layer. You can confirm this by running the following code:
    for layer in model.layers:
    print(layer.get_config(), layer.get_weights())
    Alternatively, you can also use the following where the index [1] refers to the second layer (layer index starts from 0) and the second index [0] refers to weights.
    weights = model.layers[1].get_weights()[0]

  • @roopeshroope2026
    @roopeshroope2026 2 года назад +1

    is this video word2vec from scratch

  • @alidakhil3554
    @alidakhil3554 2 года назад +1

    Can you please give a private mentor support? Paid service

    • @MachineLearningWithJay
      @MachineLearningWithJay  2 года назад

      Hi Ali, I would love to… but currently I am jamm packed in my schedule, so won’t have time to give private mentorship.

    • @alidakhil3554
      @alidakhil3554 2 года назад +1

      @@MachineLearningWithJay it is just one our, questions about multivariate linear regression.

    • @MachineLearningWithJay
      @MachineLearningWithJay  2 года назад

      @@alidakhil3554 Alright, can you msg me on whatsapp/mail?

    • @alidakhil3554
      @alidakhil3554 2 года назад

      @@MachineLearningWithJay could you please share your contact info? Thanks

  • @amirrezacodelip6429
    @amirrezacodelip6429 Месяц назад

    title is wrong you should write that how to make pytorch from 🤣🤣🤣

  • @Saed7630
    @Saed7630 3 дня назад +1

    Well done!