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Hackers Realm
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Приєднався 16 кві 2019
Welcome to Hackers Realm, your ultimate destination for learning all things in tech! 💻🌐 From Python tutorials to machine learning and deep learning projects, web scraping, blockchain concepts, data structures and algorithms, and much more, we've got you covered! 💯
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🔔 Subscribe: bit.ly/hackersrealm
🗓️ 1:1 Consultation with Me: calendly.com/hackersrealm/consult
Remember, God is a Hacker, not an Engineer! 🙌
Follow on social media channels and website for more resources.
🌐 Website: www.hackersrealm.net
📷 Instagram: aswintechguy
🔣 Linkedin: www.linkedin.com/in/aswintechguy
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And if you want to support the channel, make a small donation to our UPI ID or PayPal. 🙏
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₿ Binance Pay ID: 238629676
Image Segmentation Tutorial | UNet | Oxford Pet Data | Keras Tensorflow
⭐️ Content Description ⭐️
In this video, I have explained about how to perform image segmentation using unet model with keras and tensorflow. I have used oxford iiit pet dataset to build the model pipeline to segment animals from the images.
GitHub Code Repo: bit.ly/dlcoderepo
Dataset link: www.kaggle.com/datasets/tanlikesmath/the-oxfordiiit-pet-dataset
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⚡️ Data Structures & Algorithms tutorial playlist: bit.ly/dsatutorial
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🕒 Timeline
00:00 Introduction to Image Segmentation
00:56 Import Modules & Load Dataset
06:51 Data Preprocessing
14:41 Exploratory Data Analysis
19:26 Build U-Net Model from Scratch
36:15 Train the Model
40:45 Visualize the Results
44:06 Test Predictions of Image Segmentation
#imagesegmentation #machinelearning #hackersrealm #deeplearning #datascience #model #project #artificialintelligence #beginner #analysis #python #tutorial #aswin #ai #dataanalytics #data #bigdata #programming #datascientist #technology #coding #datavisualization #computerscience #pythonprogramming #analytics #tech #dataanalysis #iot #programmer #statistics #developer #ml #business #innovation #coder #dataanalyst
In this video, I have explained about how to perform image segmentation using unet model with keras and tensorflow. I have used oxford iiit pet dataset to build the model pipeline to segment animals from the images.
GitHub Code Repo: bit.ly/dlcoderepo
Dataset link: www.kaggle.com/datasets/tanlikesmath/the-oxfordiiit-pet-dataset
🌐 Website: www.hackersrealm.net
🔔 Subscribe: bit.ly/hackersrealm
🗓️ 1:1 Consultation with Me: calendly.com/hackersrealm/consult
📷 Instagram: aswintechguy
🔣 Linkedin: www.linkedin.com/in/aswintechguy
🎯 GitHub: github.com/aswintechguy
🎬 Share: ua-cam.com/video/ceUvzxgyop0/v-deo.html
⚡️ Data Structures & Algorithms tutorial playlist: bit.ly/dsatutorial
😎 Hackerrank problem solving solutions playlist: bit.ly/hackerrankplaylist
🤖 ML projects tutorial playlist: bit.ly/mlprojectsplaylist
🐍 Python tutorial playlist: bit.ly/python3playlist
💻 Machine learning concepts playlist: bit.ly/mlconcepts
✍🏼 NLP concepts playlist: bit.ly/nlpconcepts
🕸️ Web scraping tutorial playlist: bit.ly/webscrapingplaylist
Make a small donation to support the channel 🙏🙏🙏:-
🆙 UPI ID: hackersrealm@apl
💲 PayPal: paypal.me/hackersrealm
🕒 Timeline
00:00 Introduction to Image Segmentation
00:56 Import Modules & Load Dataset
06:51 Data Preprocessing
14:41 Exploratory Data Analysis
19:26 Build U-Net Model from Scratch
36:15 Train the Model
40:45 Visualize the Results
44:06 Test Predictions of Image Segmentation
#imagesegmentation #machinelearning #hackersrealm #deeplearning #datascience #model #project #artificialintelligence #beginner #analysis #python #tutorial #aswin #ai #dataanalytics #data #bigdata #programming #datascientist #technology #coding #datavisualization #computerscience #pythonprogramming #analytics #tech #dataanalysis #iot #programmer #statistics #developer #ml #business #innovation #coder #dataanalyst
Переглядів: 70
Відео
Speech Emotion Recognition [99.6% Accuracy] | Wav2Vec2 Transformers | Python
Переглядів 1439 годин тому
⭐️ Content Description ⭐️ In this video, I have explained about how to create speech emotion recognition model using transfer learning with the help of wav2vec2 transformers model. The model got 99.6% accuracy with the test dataset and far exceeded the previous project. Previous Project Video: ua-cam.com/video/-VQL8ynOdVg/v-deo.html Dataset link: www.kaggle.com/datasets/ejlok1/toronto-emotional...
Complete RAG Tutorial with Custom Knowledge (Documents) | Llama Index
Переглядів 9616 годин тому
⭐️ Content Description ⭐️ In this video, I have explained about retrieval augmented generation (RAG) to answer queries from documents (pdf, word, txt, etc.,) using llama index and openai. It involves other embedding models to retrieve relevant context from the documents to answer the user queries. Code Link: shorturl.at/67AAn 🌐 Website: www.hackersrealm.net 🔔 Subscribe: bit.ly/hackersrealm 🗓️ 1...
Motion Detection Tutorial using OpenCV | Python
Переглядів 8321 годину тому
⭐️ Content Description ⭐️ In this video, I have explained about how to detection motion in cctv surveillance footage and track the motion using bounding boxes. This is a very helpful beginner project for computer vision. GitHub Code Repo: github.com/aswintechguy/Python-Projects 🌐 Website: www.hackersrealm.net 🔔 Subscribe: bit.ly/hackersrealm 🗓️ 1:1 Consultation with Me: calendly.com/hackersreal...
Real time Driver Drowsiness Detection System | OpenCV | Python
Переглядів 127День тому
⭐️ Content Description ⭐️ In this video, I have explained about real time driver drowsiness detection using opencv. It can use webcam or video to detect drowsiness of the people while driving with eye and mouth aspect ratio with the help of face recognition. GitHub Code Repo: bit.ly/mlcoderepo 🌐 Website: www.hackersrealm.net 🔔 Subscribe: bit.ly/hackersrealm 🗓️ 1:1 Consultation with Me: calendly...
Creating a ChatGPT-style Chatbot with Streamlit & LLMs | Step-by-Step Guide
Переглядів 24714 днів тому
⭐️ Content Description ⭐️ In this video, I have explained on how to create a chatgpt style GUI with streamlit. It uses openai model to answer queries from the user. GitHub Code Repo: github.com/aswintechguy/Python-Projects 🌐 Website: www.hackersrealm.net 🔔 Subscribe: bit.ly/hackersrealm 🗓️ 1:1 Consultation with Me: calendly.com/hackersrealm/consult 📷 Instagram: aswintechguy 🔣 Link...
Custom Object Detection Tutorial using YOLOv8 | Python
Переглядів 31214 днів тому
⭐️ Content Description ⭐️ In this video, I have explained about how to train your own custom object detection model using YOLO. We transformed the dataset to YOLO format and trained the model from scratch to detect cars from the image. GitHub Code Repo: bit.ly/dlcoderepo Dataset link: www.kaggle.com/datasets/sshikamaru/car-object-detection/data 🌐 Website: www.hackersrealm.net 🔔 Subscribe: bit.l...
How to Fine Tune Llama 3.1 LLM (or) any LLM in Colab | PEFT | Unsloth
Переглядів 40628 днів тому
⭐️ Content Description ⭐️ In this video, I have explained about how to fine tune llama 3 8b large language model in google colab using unsloth. Unsloth provides a good framework, where it will use less memory and faster finetuning using optimization techniques. GitHub Code Repo: bit.ly/dlcoderepo 🌐 Website: www.hackersrealm.net 🔔 Subscribe: bit.ly/hackersrealm 🗓️ 1:1 Consultation with Me: calen...
How to use Llama 3.1 LLM (or) any LLM in Colab | Unsloth
Переглядів 69528 днів тому
⭐️ Content Description ⭐️ In this video, I have explained about how to use llama 3 llm in google colab using unsloth framework. The code also supports any open source llm for performing inference. GitHub Code Repo: bit.ly/dlcoderepo 🌐 Website: www.hackersrealm.net 🔔 Subscribe: bit.ly/hackersrealm 🗓️ 1:1 Consultation with Me: calendly.com/hackersrealm/consult 📷 Instagram: aswintech...
How to use LM Studio to run any LLM in Local Machine | GUI | Windows
Переглядів 198Місяць тому
⭐️ Content Description ⭐️ In this video, I have explained about how to use LM Studio to run any LLM in local machine with GUI. This is a best alternative to ollama where we can adjust the parameters of the model. It also supports a range of models which can be used locally. 🌐 Website: www.hackersrealm.net 🔔 Subscribe: bit.ly/hackersrealm 🗓️ 1:1 Consultation with Me: calendly.com/hackersrealm/co...
How to use Ollama to run any LLM in Local Machine | Windows
Переглядів 149Місяць тому
⭐️ Content Description ⭐️ In this video, I have explained about how to use ollama to use LLM in your local windows machine. The LLM can be accessed via command line, api and code which makes it a better alternative to the paid versions. 🌐 Website: www.hackersrealm.net 🔔 Subscribe: bit.ly/hackersrealm 🗓️ 1:1 Consultation with Me: calendly.com/hackersrealm/consult 📷 Instagram: aswin...
Toxic Comment Classification | Multi Label | NLP | Python
Переглядів 377Місяць тому
⭐️ Content Description ⭐️ In this video, I have explained about toxic comment classification which comes under natural language processing. This is a multi label classification problem which is different from binary and multi class classification. The project consists of Data Visualization, Data preprocessing, Model Training, Testing pipeline and Metrics Evaluation. GitHub Code Repo: bit.ly/mlc...
Air Passenger Data Analysis | Time Series Forecasting | SARIMAX | Python
Переглядів 467Місяць тому
⭐️ Content Description ⭐️ In this video, I have explained about time series forecasting using ARIMA with the help of air passenger dataset. The process consists of EDA to find the parameters of ARIMA for performing the forecast of 24 months in future using the historical data available. GitHub Code Repo: bit.ly/mlcoderepo Dataset link: www.kaggle.com/datasets/rakannimer/air-passengers 🌐 Website...
Extract Embedding Features from Audio Data | Wav2Vec2 | Python
Переглядів 108Місяць тому
⭐️ Content Description ⭐️ In this video, I have explained about how to extract embedding features from audio file using wav2vec2 model by facebook. The extracted features can be directly used for any downstream training task. GitHub Code Repo: bit.ly/datascienceconcepts 🌐 Website: www.hackersrealm.net 🔔 Subscribe: bit.ly/hackersrealm 🗓️ 1:1 Consultation with Me: calendly.com/hackersrealm/consul...
How to Deploy a Trained ML Model in Cloud | GCP
Переглядів 3813 місяці тому
⭐️ Content Description ⭐️ In this video, I have explained about how to deploy a trained machine learning model in a public cloud like GCP. Deploying the model in public cloud server will be very helpful to make it usable for anyone and it's a common step in production. GitHub Code Repo: bit.ly/mlcoderepo Deploy Model using Flask: ua-cam.com/video/2LqrfEzuIMk/v-deo.html 🌐 Website: www.hackersrea...
How to Deploy a Trained Model using Docker
Переглядів 3373 місяці тому
How to Deploy a Trained Model using Docker
How to Create a Python Package with Pytest
Переглядів 743 місяці тому
How to Create a Python Package with Pytest
Early Stopping & Checkpoint Implementation | Keras Tensorflow | Python
Переглядів 1643 місяці тому
Early Stopping & Checkpoint Implementation | Keras Tensorflow | Python
How to use Pipeline Module for Model Development | Python
Переглядів 1563 місяці тому
How to use Pipeline Module for Model Development | Python
L1 & L2 Regularization Techniques | Lasso Ridge | Python
Переглядів 1553 місяці тому
L1 & L2 Regularization Techniques | Lasso Ridge | Python
Sampling Techniques (Random & Stratified) | Python
Переглядів 1123 місяці тому
Sampling Techniques (Random & Stratified) | Python
Data Augmentation for Text | NLP | Python
Переглядів 2273 місяці тому
Data Augmentation for Text | NLP | Python
Mastering Machine Learning with PySpark | Loan Prediction | Python
Переглядів 3,4 тис.Рік тому
Mastering Machine Learning with PySpark | Loan Prediction | Python
Revive Your Old Images and Videos with Colorization using DeOldify and Python Deep Learning
Переглядів 2,2 тис.Рік тому
Revive Your Old Images and Videos with Colorization using DeOldify and Python Deep Learning
Mastering OpenAI GPT-3 & 3.5: A Comprehensive Guide to Overview, API, Examples, and Fine Tuning
Переглядів 1,8 тис.Рік тому
Mastering OpenAI GPT-3 & 3.5: A Comprehensive Guide to Overview, API, Examples, and Fine Tuning
Enhance Your Images with Super Resolution using OpenCV and Python Deep Learning
Переглядів 17 тис.Рік тому
Enhance Your Images with Super Resolution using OpenCV and Python Deep Learning
How to install OpenCV with CUDA GPU in windows 10 | Python
Переглядів 17 тис.Рік тому
How to install OpenCV with CUDA GPU in windows 10 | Python
Anime Face Generation using DCGAN | Keras Tensorflow | Deep Learning | Python
Переглядів 7 тис.Рік тому
Anime Face Generation using DCGAN | Keras Tensorflow | Deep Learning | Python
234 - Play with words | Dynamic Programming | Hackerrank Solution | Python
Переглядів 792Рік тому
234 - Play with words | Dynamic Programming | Hackerrank Solution | Python
233 - The Longest Common Subsequence | Dynamic Programming | Hackerrank Solution | Python
Переглядів 1,5 тис.Рік тому
233 - The Longest Common Subsequence | Dynamic Programming | Hackerrank Solution | Python
thanks a ton
Where did u get the N
it's in the main function if you scroll down the page while solving.
Thank you. This was a really helpful video.
nice didnt work "Error in configuration process, project files may be invalid." i see you responding to all msgs just not these ones so no help at all
could you please share at which step you're getting this error?
@@HackersRealm in cmake following your guide ive even tried it in cmd cmake -G "Visual Studio 17 2022" ^ -A x64 ^ -D CMAKE_BUILD_TYPE=Release ^ -D CMAKE_INSTALL_PREFIX=D:/OpenCV-GPU/opencv-4.10.0/build ^ -D OPENCV_EXTRA_MODULES_PATH=D:/OpenCV-GPU/opencv_contrib-4.x/modules ^ -D WITH_CUDA=ON ^ -D CUDA_TOOLKIT_ROOT_DIR="C:/Program Files/NVIDIA GPU Computing Toolkit/CUDA/v11.2" ^ -D WITH_CUBLAS=ON ^ -D OPENCV_DNN_CUDA=ON ^ -D CMAKE_CXX_STANDARD=11 ^ -D CMAKE_CXX_STANDARD_REQUIRED=ON ^ -D WITH_CUDNN=ON ^ -D BUILD_opencv_world=ON ^ .. I still get an error
@@HackersRealm my comment got deleted or flagged un hide it
model.compile(loss=['binary_crossentropy', 'mae'], optimizer='adam', metrics=['accuracy', 'mae']) In this line you add additionaly mae to metrics in your github repository. when I try with following line in google colab It does not work, showing error. But in your above video, It works propperly, what is the reason for that. Can you explain? model.compile(loss=['binary_crossentropy', 'mae'], optimizer='adam', metrics=['accuracy'])
It might be due to version changes happening in tensorflow.
@@HackersRealm okey but I have another doubt... Why you add 'mae' to matrics..its already in the loss also...
and X = extract_features(df['image']) this line take too much time for executing(more than hours). what is the problem?
@@kirushanthuthayakumar7769 are you using gpu? gpu speeds up the process
@@HackersRealm yes gpu also taking this much of time
Hi , while fitting the model, we use X and y where y = ['Survived'] . Now X has has no NaN values but Y does . So it throws an error. Since y is our target varable , is it appropriate to fill the missing values with mode?
no, we can't use mode for the target, you could drop those rows from the dataset.
Thank you for the help!
Hey nice Video!! But I have one question to fill missing values for Item weight with mean values why didnt you simply use "median = df[''Item_Visibility'].median() df['Item_Weight'].fillna(median, inplace=True)" What is the point of the big code? Starting from Line 11
@HackersRealm
can somebody please explain from where we get 1.5 in the IQR method? why exactly 1.5?
i got error in configuration process, project files may be invalid
could you please share when you're getting this error? at which step?
is it possible to run the code in google colab
yes, you have to upload the dataset and change the path
@@HackersRealm ❤️
Can we use our own data for response, means I ask question the response should hit the database and it should send the response.
the video is coming soon next week!! Thanks for your patience!!!
@@HackersRealm thank you
Getting this error while training the model - assertion failed: [You are passing a RNN mask that does not correspond to right-padded sequences, while using cuDNN, which is not supported. With cuDNN, RNN masks can only be used for right-padding, e.g. `[[True, True, False, False]]` would be a valid mask, but any mask that isn\'t just contiguous `True`\'s on the left and contiguous `False`\'s on the right would be invalid. You can pass `use_cudnn=False` to your RNN layer to stop using cuDNN (this may be slower).] [[{{node functional_1_1/lstm_1/Assert/Assert}}]] [Op:__inference_one_step_on_iterator_423791] I have not changed anything in the code. Running your code only. Please suggest what to do?
Are you running this in kaggle?
I replicated it, but is performing poorly how can we improve the performance, will using random forest be useful? please tell some others ways also to improve performance
Keeping UA-cam channel and directly writing code is not matters, FIRST learn how to explain question to viewers after you start coding Bad explanation cheating....
I have received feedback for my initial 100 videos regarding short explanation.. I have improved the time taken for explanation in the latter videos based on it... Everyone has to start somewhere to learn... Thanks for taking the time to share the feedback, much appreciated!!!
Please could you explain, how can we train on our own dataset, I watched most of the videos on Fine-tuning llm using unsloth, almost everyone just using template to of huggingface dataset load , no one explain about how can create your own instruct - chat dataset and use that dataset, for example I have questions answers pairs each next question follow ups to previous answer, I want to train in context
Got it, so you want to train the model for chat which uses the previous message, is that correct? If that's the case, you could have additional section called chat history which contains the previous messages, so the format would be instruction, chat history, input, output... after creating the samples, you can train with the rest of the pipeline
@@HackersRealm yes correct,
please send source code link
it's in the description, check in iris dataset analysis folder
Can you provide a step by step roadmap for learning all this
Do you mean for learning data science from scratch?
@@HackersRealm yeah
@@HaHa-qi8jx got it, I will try to make a detailed video of it, if needed
bro please make more videos on data structures and try to make videos of problem solving of data structures in hackerrank
Hi there, I am planning to make DSA with python videos with code and pen&paper methods... I have stopped hackerrank series as I have covered the important problems to get a placement. Once I covered the ML problems, I will start the DSA series. Thanks for your patience.
Training data contains 1 samples, which is not sufficient to split it into a validation and training set as specified by `validation_split=0.1`. Either provide more data, or a different value for the `validation_split` argument. I am facing this error while training of model, how can i solve this? and i am using kaggle colaboratory
I think there is a problem in dataset and some class have less samples, please check that once as it might be causing the issues.
Worked perfectly 🎉 thanks
Thanks for sharing the solution!!
Why n//=2 I
Help me
The important thing to keep in mind is that If we approach this problem using normal map = dict(), it will KeyError for arr[i + 1] (because it may not be there in the map), However, if we use Counter(arr) to create the dictionary, then it will not give us KeyError (It will simply return 0 for any key not in the map).
Question was not written accurately. They first tell us to find SUB-ARRAY, then in the Test Case, they expect us to find SUB-SEQUENCE.
how do you make tough questions look so simple. Great job thala!
Glad it was helpful!!!
Upar ke modules execute ni hore ????kya kare?
which part is not executed?
Accuracy bar bar 100 aarha h
In last code I got 95%accuracy instead of 93 is this correct?
@@aarohigupta4286 yeah, the train test split might change each time if we run, so it's not an issue, to replicate same results, you can use random state in train_test_split.
why we need to convert entire string into lowercase
Hi I am getting issue class:an error occurred:name "model" is not defined
please check whether the model variable is initialized
Thank you so much!!!. A question: in what type of distributions can the box plot be used? For example, if the data follows a uniform distribution, does it make sense to find outliers? What do you recommend me?
You can use box plot and check if there are any outlier for any distribution. If there is some outliers, do the processing, if not ignore it.
@@HackersRealm thanks for your answer
Amazing tutorial. Bro, you made my day. Lots of love from Pakistan.
Glad to hear that!!!
You are from Pakistan !! Amazing !😀
can you please make a roadmap on Machine learning + resources
if you follow the other playlist concepts and try out projects from this playlist, that would be enough to become master ML
i am not getting the program to be run correctly maxsum=-99 for i in range(4): for j in range(4): top=sum(arr[i][j:j+3]) mid=arr[i+1][j+1] bot=sum(arr[i+2][j:j+3]) hourglass=top+mid+bot maxsum= max(hourglass, maxsum) return maxsum this is the code
What when position is 0 ?
I have a scenario of extracting most relatable ( having more words matching ) audios using an input audio. What's the best way to approach this problem ? If it is embeddings then how I'll go about it and if not, then tell me which one would be better.
if you have embeddings, you can use cosine similarity or use some search index to find the related audios.
As far as I know, initially we use smaller learning rate because at the start of the training our model usually takes larger steps to reach the minima, but as it progresses it becomes slow so if we are dealing with a large dataset we can simply use higher batch size like 8192 etc and by using warming up the learning rate (which will gradually increase the learning rate during training) so that our model can converge not only more faster but will yield more accurate results (generalization) as a lower batch sizes of 32, 64 usually yields.
Can you please send the code
Thanks for the video. I am currently working on EfficientNet and Topological data analysis. I have to extract features from the TDA and then from EfficientNet and then combine it and run it on SVM. Can it work there too?? Thanks again
yes, you just have to change the model and use it. Use concat function to combine the features.
Can you make separate course for time series.
Do you have a list of topics needs to be covered? because it has many sections
Time series analysis. Some classical time series forecasting methods. Machine learning time series methods. How to use Facebook prophet to forecasting.
@@JonSnow-gs1hg fbprophet model, I have already covered, please search for traffic forecast analysis to check that. Remaining topics, I will try to cover soon
Ok thank you.
such a great video! i want ask what if i want to enchance a bunch of image in 1 directory
You could check the video in the project playlist which is how to enhance images using super resolution
Its so nice and very helpful. Thank you so much ❤
Glad it was helpful!!!
Second question, at the end you are testing your model with images that comes from X, and X has been used to train the model so aren't you testing the model on images that it already saw?
you could also try with new image doing the same preprocessing techniques, and not all the images were used for training as there is validation split used for evaluation. I will make a note and use new images in future video, thanks.
Hello! Thank you for the video. I was just wondering why didn't we look at the accuracy for the age?
age is a continuous value, we cannot identify accuracy for that, we can only calculate the error difference
Wow sir hats of to your logics
Thank you so much for this. I have been trying to install opencv with cuda for a few days now and there was always some new error that I could not resolve. This is the first tutorial that worked without any issues. Thank you so much!!
glad it helps!!!
Hey Hackers, From minute 26-28, the video is screen was frozen during recording while the audio is available, you can see the code in the notebook after that. Thanks for your Patience!!!
what is this yan(n) here?
bro thanks a lot but initially i got so many errors that it had taken my 2 days but i used chatgpt, google, youtube to resolve all that issues and At the End i got it.
How can i deploy a virus in my local callcenter ?
Sorry I don't have expertise in that area