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All the code can be found in the chapter folders. You can run these code files on cloud platforms like Google Colab or your local machine. Note that some chapters require a GPU to run in a reasonable amount of time, so we recommend one of the cloud platforms as they come pre-installed with CUDA.

To run the notebook (.ipynb) files on a cloud platform, just click on one of the badges in the table below:

Chapter	Colab	Kaggle	Gradient	StudioLab
02 Regression and Classification logistic_regression_using_keras_API.ipynb multiple_linear_regression_using_keras_API.ipynb simple_linear_regression.ipynb simple_linear_regression_using_keras_API.ipynb
07 Unsupervised Learning DBN.ipynb PCA.ipynb k_means_using_tensorflow.ipynb restricted_boltzmann_machines.ipynb som.ipynb
08 Autoencoders ConvolutionAutoencoder.ipynb DenoisingAutoencoder.ipynb SparseAutoEncoder.ipynb VAE.ipynb VanillaAutoEncoder.ipynb sentence_vector_gen.ipynb
09 Generative Models CycleGAN_TF2.ipynb DCGAN.ipynb VanillaGAN.ipynb
11 Reinforcement Learning DQNCartPole.ipynb DQN_Atari_v2.ipynb Introduction_to_gym.ipynb random_agent_playing.ipynb
12 Probabilistic TensorFlow AleatoryUncertainity_using_TFP.ipynb Bayesian_networks.ipynb EpistemicUncertainity_using_TFP.ipynb Fun_with_tensorflow_probability.ipynb Introduction_to_TFP.ipynb
16 Other Useful Deep Learning Libraries H2o_classification.ipynb PyTorch.ipynb
19 TensorFlow 2 Ecosystem End_to_End_TFDS_pipeline.ipynb Image_Classification_TF_Hub.ipynb Introduction_to_TensorFlow_datasets.ipynb

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