AdvancedDeepLearning

DL with Tensorflow 2.4 and Keras

This is code following Deep Learning With Tensorflow 2 and Keras by Packt

All of the code is on google colab, a resource which allows the free access of GPUS and TPUS on the cloud

Most of the code is expirimental as I try experiments the book suggests

This repository includes: CNN \n Advanced CNNs \n Regression \n Tensorflow Basics \n RNNs and LSTMs \n GANs \n Reenforcement Learing \n Autoencoders and VAEs \n

Important Note* Some of the code in this repository is very similar to the repository of the book. I have written all of this code and now understand this subject to the fullest. It is not me copy-pasting the code from their repository, but rather me coding directly from the book, line by line. Some code are just some of my expirements the book may have not gone over.

Name		Name	Last commit message	Last commit date
Latest commit History 52 Commits
Bounding_Boxes_For_VGG16.ipynb		Bounding_Boxes_For_VGG16.ipynb
COCO_Object_Detection_with_TF_hub.ipynb		COCO_Object_Detection_with_TF_hub.ipynb
ColorizationGAN_(Made_on_my_own).ipynb		ColorizationGAN_(Made_on_my_own).ipynb
Colorization_AutoEncoder.ipynb		Colorization_AutoEncoder.ipynb
ConditionalGAN.ipynb		ConditionalGAN.ipynb
Cycle_GAN_for_Summer_and_Winter_in_Yosemite.ipynb		Cycle_GAN_for_Summer_and_Winter_in_Yosemite.ipynb
DCGAN_for_MNIST.ipynb		DCGAN_for_MNIST.ipynb
DQN_for_Atari.ipynb		DQN_for_Atari.ipynb
DQN_for_CartPole.ipynb		DQN_for_CartPole.ipynb
DeepDream.ipynb		DeepDream.ipynb
DeepLeNet-for-CIFAR-10.ipynb		DeepLeNet-for-CIFAR-10.ipynb
Denoising_Autoencoder.ipynb		Denoising_Autoencoder.ipynb
Embeddings_with_Gensim.ipynb		Embeddings_with_Gensim.ipynb
Estimator_for_FashionMnist.ipynb		Estimator_for_FashionMnist.ipynb
Face_Recognition_with_Siamese_networks.ipynb		Face_Recognition_with_Siamese_networks.ipynb
GAN_for_MNIST.ipynb		GAN_for_MNIST.ipynb
GPU_Distribution.ipynb		GPU_Distribution.ipynb
Generating_Text_LSTM.ipynb		Generating_Text_LSTM.ipynb
Git_cloning_to_use_bert.ipynb		Git_cloning_to_use_bert.ipynb
Image_captioning_model.ipynb		Image_captioning_model.ipynb
Item2Vec.ipynb		Item2Vec.ipynb
K_means_Clustering.ipynb		K_means_Clustering.ipynb
LeNet_for_MNIST.ipynb		LeNet_for_MNIST.ipynb
Linear_Regressor_for_real_house_prices.ipynb		Linear_Regressor_for_real_house_prices.ipynb
Logistic_regression.ipynb		Logistic_regression.ipynb
POS_tagging.ipynb		POS_tagging.ipynb
PPO_TF2.ipynb		PPO_TF2.ipynb
README.md		README.md
Restricted_Boltzmann_machine.ipynb		Restricted_Boltzmann_machine.ipynb
SMS_Spam_Detection.ipynb		SMS_Spam_Detection.ipynb
Seq2SeqTranslation.ipynb		Seq2SeqTranslation.ipynb
Seq2SeqTranslation_With_Attention.ipynb		Seq2SeqTranslation_With_Attention.ipynb
Seq2SeqTranslation_for_el_espanolllllll.ipynb		Seq2SeqTranslation_for_el_espanolllllll.ipynb
Sparse_AutoEncoder.ipynb		Sparse_AutoEncoder.ipynb
Speech_Commands.ipynb		Speech_Commands.ipynb
StyleTransfer.ipynb		StyleTransfer.ipynb
Text_Summarization_with_Transformers.ipynb		Text_Summarization_with_Transformers.ipynb
Transfer_Learning_Basics.ipynb		Transfer_Learning_Basics.ipynb
Transfer_Learning_on_MobileNetv2.ipynb		Transfer_Learning_on_MobileNetv2.ipynb
Transformer_for_cifar100.ipynb		Transformer_for_cifar100.ipynb
Using_Conv1D's_for_better_text_accuracy.ipynb		Using_Conv1D's_for_better_text_accuracy.ipynb
Using_pretrained_VGG.ipynb		Using_pretrained_VGG.ipynb
Vanilla_AutoEncoder.ipynb		Vanilla_AutoEncoder.ipynb

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