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The full collection of all codes for my Youtube Channel segregated as per topic.

Home Page: https://www.youtube.com/channel/UC0_a8SNpTFkmVv5SLMs1CIA/videos

Python 0.40% Jupyter Notebook 93.07% HTML 6.54%
machine-learning machine-learning-algorithms deep-learning deep-neural-networks neural-network data-science python tensorflow tensorflow2 tensorflow-tutorials

machinelearning-deeplearning-code-for-my-youtube-channel's Introduction

This Repo has all the codes for my YouTube Channel

Youtube Link

Author: Rohan Paul

Find me here..



Natural Language Processing | NLP




Finance and Trading with Machine Learning and Deep Learning



Computer Vision & Deep Learning Algorithms


PyTorch Techniques for Deep Learning


Image Processing, Image Augmentation and OpenCV


Computer Vision Paper Discussion

  • Youtube Link 26 ๐—ฃ๐—ฎ๐—ฝ๐—ฒ๐—ฟ ๐—˜๐˜…๐—ฝ๐—น๐—ฎ๐—ถ๐—ป๐—ฒ๐—ฑ - ๐—˜๐˜†๐—ฒ๐˜€ ๐—ง๐—ฒ๐—น๐—น ๐—”๐—น๐—น - ๐——๐—ฒ๐˜๐—ฒ๐—ฐ๐˜ ๐—™๐—ฎ๐—ธ๐—ฒ ๐—”๐—œ ๐—ด๐—ฒ๐—ป๐—ฒ๐—ฟ๐—ฎ๐˜๐—ฒ๐—ฑ ๐—ณ๐—ฎ๐—ฐ๐—ฒ๐˜€ ( ๐—–๐—ผ๐—บ๐—ฝ๐˜‚๐˜๐—ฒ๐—ฟ ๐—ฉ๐—ถ๐˜€๐—ถ๐—ผ๐—ป / ๐—š๐—”๐—ก๐˜€ )

  • Youtube Link 14. GFP GAN Paper with Python code to restore resolution of old Photos

Tensorflow


Kaggle Competitions / Data Science Project


Neural Network


Python_Challenges


Machine Learning Algo Implementations from Scratch with pure Python


Statistics


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machinelearning-deeplearning-code-for-my-youtube-channel's People

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machinelearning-deeplearning-code-for-my-youtube-channel's Issues

## DCGAN

please can share DCGAN implementation for an image size of 224,224,3

test data as validation data is a mistake

here https://github.com/rohan-paul/MachineLearning-DeepLearning-Code-for-my-YouTube-Channel/blob/master/Finance_Stock_Crypto_Trading/Bitcoin_Price_Prediction_with_LSTM.ipynb

You are passing in validation_data (which is the cross validation data used for training) the same test data that you will further use to make the predictions.

Result: Predictions are completely misleading and they seem way better than they really should be, you are leaking test data values to your model in the training process.

Please let me know if you don't agree with me.

getting 38% hamming score using this code for multilabel classification of my 24000 dataset into 26 classes. please suggest something so that casn go upto 90%

0it [00:00, ?it/s]
Epoch: 0, Loss: 0.607304036617279
102it [01:51, 1.04s/it]
Epoch: 0, Loss: 0.42913737893104553
202it [03:40, 1.04s/it]
Epoch: 0, Loss: 0.31689316034317017
278it [05:05, 1.10s/it]
0it [00:00, ?it/s]
Epoch: 1, Loss: 0.25217771530151367
102it [01:52, 1.04s/it]
Epoch: 1, Loss: 0.2359163910150528
202it [03:41, 1.04s/it]
Epoch: 1, Loss: 0.20691940188407898
278it [05:06, 1.10s/it]
0it [00:00, ?it/s]
Epoch: 2, Loss: 0.18726885318756104
102it [01:52, 1.05s/it]
Epoch: 2, Loss: 0.18049341440200806
202it [03:41, 1.05s/it]
Epoch: 2, Loss: 0.1675453633069992
278it [05:06, 1.10s/it]
0it [00:00, ?it/s]
Epoch: 3, Loss: 0.1804620772600174
102it [01:52, 1.04s/it]
Epoch: 3, Loss: 0.15942974388599396
202it [03:41, 1.04s/it]
Epoch: 3, Loss: 0.1614547073841095
278it [05:06, 1.10s/it]
0it [00:00, ?it/s]
Epoch: 4, Loss: 0.16714756190776825
102it [01:52, 1.04s/it]
Epoch: 4, Loss: 0.18206024169921875
202it [03:41, 1.04s/it]
Epoch: 4, Loss: 0.18216757476329803
278it [05:06, 1.10s/it]
0it [00:00, ?it/s]
Epoch: 5, Loss: 0.14875689148902893
102it [01:52, 1.03s/it]
Epoch: 5, Loss: 0.14692261815071106
202it [03:40, 1.04s/it]
Epoch: 5, Loss: 0.1404387503862381
278it [05:04, 1.10s/it]
0it [00:00, ?it/s]
Epoch: 6, Loss: 0.16587977111339

Error Use Bayesian Optimization with Hyperopt

when excute:
best_params_use, best_clusters_use, trials_use = utils.bayesian_search(embeddings_st1,
space=hspace,
label_lower=int(label_lower),
label_upper=int(label_upper),
max_evals=int(max_evals))

TypeError: 'numpy.float64' object cannot be interpreted as an integer. I didn't modify any part of this notebook

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