1 - Building Deep Learning Environment Building a common Deep Learning environment Get focused and into the code! Deep Learning environment setup in local Download and install Anaconda Installing Deep Learning libraries Deep Learning environment setup in the cloud Cloud platforms for deployment  Prerequisites Setup the GCP Automating the setup process Summary 2 - Training NN for Prediction using Regression Introduction Building a regression model for prediction using a multilayer perceptron - A deep neural network Exploring MNIST dataset Intuition and preparation Defining regression Defining project structure Let's code the implementation! Defining hyperparameters Model definition Build the training loop Overfitting and Underfitting  Building inference The conclusion to the project Summary 3 - Word representation using Word2VEC Learning Word Vectors Load all the dependencies Prepare the Text Corpus Defining Our Word2vec Model Let's Train The Model Analysing The Model Plotting The Word Cluster Using The t-SNE Algorithm Visualizing the Embedding Space - Plotting the model on Tensorboard Building language model using CNN + word2vec Exploring The CNN Model Understanding Data Format  Integrating word2vec with CNN Executing the Model  Deploy the model into production Summary 4 - Build NLP pipeline for building chatbots Basics of NLP pipeline Tokenisation Part of speech tagging Extracting Nouns Extracting Verbs Dependency Parsing Named Entity Recognition Building conversational bots What is TF-IDF? Preparing dataset Implementation Creating Vectorizer Process Query Rank Results Advance chatbots using NER Installing Rasa Preparing dataset Train the model Deploy the model Serving chatbots Summary 5 - Sequence-to-sequence models for building chatbots Introducing RNNs RNN Architectures Implementing basic RNN Importing all the dependencies Preparing dataset Hyperparameter Defining Basic RNN cell model Training the RNN Model Evaluation Of the RNN Model LSTM Architecture Implementing LSTM Model Defining LSTM model Training the LSTM Model Evaluation of the LSTM model Sequence-to-Sequence model Data Preparation Defining seq2seq model Hyperparameters Training the seq2seq model Evaluation of the seq2seq model Summary 6 Generative Language model for content creation LSTM For Text Generation Data pre-processing Defining The LSTM Model For Text Generation Training The Model Inference and Results Generate Lyrics using Deep (Multi-layer) LSTM Data pre-processing Defining the model Training the Deep Tensorflow based LSTM Model Inference Output Generate Music using Multi-layer LSTM Pre-processing data Define model and Train Generating music Summary 14 - Image translation using GANs for style transfer INTRODUCTION Let's Code the Implementation! Importing all the dependencies Exploring the data Preparing the data Type Conversion, Centering and Scaling Masking / Inserting Noise Reshaping MNIST Classifier Defining Hyperparameters for GAN Building the GAN Model Components Defining the Generator Defining the Discriminator Defining the DCGAN Training GAN Plot the Training  - 1 Plot the Training - 2 Training Loop Predictions CNN classifier predictions on the noised and generated images Scripts in Modular form Module 1  - train_mnist.py Module 2 - training_plots.py Module 3 - GAN.py Module 4 - train_gan.py The conclusion to the project Summary 15- Develop an autonomous Agents with Deep R Learning INTRODUCTION Let's get to the Code! Deep Q Learning Importing all the dependencies Exploring the Cart-Pole game Interacting with Cart-Pole game Loading the Game Resetting The Game Playing the Game Q - Learning Defining Hyperparameters for DQN Building the Model Components Defining the Agent Defining the Agent Action Defining the Memory Defining Performance Plot Defining Replay Training Loop Testing the DQN model Deep Q learning Scripts in Modular form Module 1  - hyperparameters_dqn.py Module 2 - agent_replay_dqn.py Module 3 - test_dqn.py Module 4 - train_dqn.py Deep SARSA Learning SARSA Learning Importing all the dependencies Loading the game Environment Defining the agent Training the agent Testing the agent Deep SARSA learning Script in Modular form The conclusion to the project Summary