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1.1 Introduction rbm and autoencoders
1.2 Deploying rbm for deep neural networks, using rbm for collaborative filtering
1.3 Autoencoders features and applications of autoencoders.
2.1 Constructing a convolutional neural network using TensorFlow
2.2 Convolutional, dense, and pooling layers of CNNs
2.3 Filtering images based on user queries
3.1 Automated conversation bots leveraging
3.2 Generative model, and the sequence to sequence model (lstm).
4.1 Parallel Training
4.2 Distributed vs Parallel Computing
4.3 Distributed computing in Tensorflow
4.4 Introduction to tf.distribute
4.5 Distributed training across multiple CPUs
4.6 Distributed Training
4.7 Distributed training across multiple GPUs
4.8 Federated Learning
4.9 Parallel computing in Tensorflow
5.1 Mapping the human mind with deep neural networks (dnns)
5.2 Several building blocks of artificial neural networks (anns)
5.3 The architecture of dnn and its building blocks
5.4 Reinforcement learning in dnn concepts, various parameters, layers, and optimization algorithms in dnn, and activation functions.
6.1 Understanding model Persistence
6.2 Saving and Serializing Models in Keras
6.3 Restoring and loading saved models
6.4 Introduction to Tensorflow Serving
6.5 Tensorflow Serving Rest
6.6 Deploying deep learning models with Docker & Kubernetes
6.7 Tensorflow Serving Docker
6.8 Tensorflow Deployment Flask
6.9 Deploying deep learning models in Serverless Environments
6.10 Deploying Model to Sage Maker
6.11 Explain Tensorflow Lite Train and deploy a CNN model with TensorFlow
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