Getting Started with TensorFlow 2.0 for Deep Learning

Deep Learning Basics 7 lectures 20:00 The Course Overview This video will give you an overview about the course. Introducing Deep Learning and Setting Up the Environment Learn the basic concepts including AI, ML, and DL. Also, learn neural networks and perceptron. • Distinguish between AI, ML, and DL • Learn about NN • Perceptron versus multilayer perceptron Introducing the Universal Workflow of Deep Learning Define the universal workflow for any Deep Learning project. • Introduce seven steps for any Deep Learning project • Define the problem, get a dataset, and prepare the datasets • Choose success metrics, evaluation criteria, and hyperparameter tuning Mathematics Refresher Get familiar with the maths behind Deep Learning. • Learn about tensors in Python • Practical implementation • Summarize the concepts Training, Validation, and Test Sets Explore what train, validation, and test set are in a Deep Learning project. • Define the train, validation, and test sets in the simplest of terms • Download and manipulate Keras dataset • Split the dataset into three sets programmatically Data Preprocessing and Feature Engineering for Deep Learning Models Learn what data preprocessing and feature engineering are. • Learn about vectorization, normalization, and handling missing values • Learn about feature engineering Demonstrating Overfitting and Underfitting of Data Distinguish between overfitting and underfitting. • Explore the definitions of the core concepts of Deep Learning • Define the goal of Deep Learning engineers • Demonstrate with graphs Deep Learning Basics - Quiz Deep Learning Basics 7 lectures 20:00 The Course Overview This video will give you an overview about the course. Introducing Deep Learning and Setting Up the Environment Learn the basic concepts including AI, ML, and DL. Also, learn neural networks and perceptron. • Distinguish between AI, ML, and DL • Learn about NN • Perceptron versus multilayer perceptron Introducing the Universal Workflow of Deep Learning Define the universal workflow for any Deep Learning project. • Introduce seven steps for any Deep Learning project • Define the problem, get a dataset, and prepare the datasets • Choose success metrics, evaluation criteria, and hyperparameter tuning Mathematics Refresher Get familiar with the maths behind Deep Learning. • Learn about tensors in Python • Practical implementation • Summarize the concepts Training, Validation, and Test Sets Explore what train, validation, and test set are in a Deep Learning project. • Define the train, validation, and test sets in the simplest of terms • Download and manipulate Keras dataset • Split the dataset into three sets programmatically Data Preprocessing and Feature Engineering for Deep Learning Models Learn what data preprocessing and feature engineering are. • Learn about vectorization, normalization, and handling missing values • Learn about feature engineering Demonstrating Overfitting and Underfitting of Data Distinguish between overfitting and underfitting. • Explore the definitions of the core concepts of Deep Learning • Define the goal of Deep Learning engineers • Demonstrate with graphs Deep Learning Basics - Quiz The Course Overview This video will give you an overview about the course. The Course Overview This video will give you an overview about the course. The Course Overview This video will give you an overview about the course. The Course Overview This video will give you an overview about the course. This video will give you an overview about the course. This video will give you an overview about the course. Introducing Deep Learning and Setting Up the Environment Learn the basic concepts including AI, ML, and DL. Also, learn neural networks and perceptron. • Distinguish between AI, ML, and DL • Learn about NN • Perceptron versus multilayer perceptron Introducing Deep Learning and Setting Up the Environment Learn the basic concepts including AI, ML, and DL. Also, learn neural networks and perceptron. • Distinguish between AI, ML, and DL • Learn about NN • Perceptron versus multilayer perceptron Introducing Deep Learning and Setting Up the Environment Learn the basic concepts including AI, ML, and DL. Also, learn neural networks and perceptron. • Distinguish between AI, ML, and DL • Learn about NN • Perceptron versus multilayer perceptron Introducing Deep Learning and Setting Up the Environment Learn the basic concepts including AI, ML, and DL. Also, learn neural networks and perceptron. • Distinguish between AI, ML, and DL • Learn about NN • Perceptron versus multilayer perceptron Learn the basic concepts including AI, ML, and DL. Also, learn neural networks and perceptron. • Distinguish between AI, ML, and DL • Learn about NN • Perceptron versus multilayer perceptron Learn the basic concepts including AI, ML, and DL. Also, learn neural networks and perceptron. • Distinguish between AI, ML, and DL • Learn about NN • Perceptron versus multilayer perceptron Introducing the Universal Workflow of Deep Learning Define the universal workflow for any Deep Learning project. • Introduce seven steps for any Deep Learning project • Define the problem, get a dataset, and prepare the datasets • Choose success metrics, evaluation criteria, and hyperparameter tuning Introducing the Universal Workflow of Deep Learning Define the universal workflow for any Deep Learning project. • Introduce seven steps for any Deep Learning project • Define the problem, get a dataset, and prepare the datasets • Choose success metrics, evaluation criteria, and hyperparameter tuning Introducing the Universal Workflow of Deep Learning Define the universal workflow for any Deep Learning project. • Introduce seven steps for any Deep Learning project • Define the problem, get a dataset, and prepare the datasets • Choose success metrics, evaluation criteria, and hyperparameter tuning Introducing the Universal Workflow of Deep Learning Define the universal workflow for any Deep Learning project. • Introduce seven steps for any Deep Learning project • Define the problem, get a dataset, and prepare the datasets • Choose success metrics, evaluation criteria, and hyperparameter tuning Define the universal workflow for any Deep Learning project. • Introduce seven steps for any Deep Learning project • Define the problem, get a dataset, and prepare the datasets • Choose success metrics, evaluation criteria, and hyperparameter tuning Define the universal workflow for any Deep Learning project. • Introduce seven steps for any Deep Learning project • Define the problem, get a dataset, and prepare the datasets • Choose success metrics, evaluation criteria, and hyperparameter tuning Mathematics Refresher Get familiar with the maths behind Deep Learning. • Learn about tensors in Python • Practical implementation • Summarize the concepts Mathematics Refresher Get familiar with the maths behind Deep Learning. • Learn about tensors in Python • Practical implementation • Summarize the concepts Mathematics Refresher Get familiar with the maths behind Deep Learning. • Learn about tensors in Python • Practical implementation • Summarize the concepts Mathematics Refresher Get familiar with the maths behind Deep Learning. • Learn about tensors in Python • Practical implementation • Summarize the concepts Get familiar with the maths behind Deep Learning. • Learn about tensors in Python • Practical implementation • Summarize the concepts Get familiar with the maths behind Deep Learning. • Learn about tensors in Python • Practical implementation • Summarize the concepts Training, Validation, and Test Sets Explore what train, validation, and test set are in a Deep Learning project. • Define the train, validation, and test sets in the simplest of terms • Download and manipulate Keras dataset • Split the dataset into three sets programmatically Training, Validation, and Test Sets Explore what train, validation, and test set are in a Deep Learning project. • Define the train, validation, and test sets in the simplest of terms • Download and manipulate Keras dataset • Split the dataset into three sets programmatically Training, Validation, and Test Sets Explore what train, validation, and test set are in a Deep Learning project. • Define the train, validation, and test sets in the simplest of terms • Download and manipulate Keras dataset • Split the dataset into three sets programmatically Training, Validation, and Test Sets Explore what train, validation, and test set are in a Deep Learning project. • Define the train, validation, and test sets in the simplest of terms • Download and manipulate Keras dataset • Split the dataset into three sets programmatically Explore what train, validation, and test set are in a Deep Learning project. • Define the train, validation, and test sets in the simplest of terms • Download and manipulate Keras dataset • Split the dataset into three sets programmatically Explore what train, validation, and test set are in a Deep Learning project. • Define the train, validation, and test sets in the simplest of terms • Download and manipulate Keras dataset • Split the dataset into three sets programmatically Data Preprocessing and Feature Engineering for Deep Learning Models Learn what data preprocessing and feature engineering are. • Learn about vectorization, normalization, and handling missing values • Learn about feature engineering Data Preprocessing and Feature Engineering for Deep Learning Models Learn what data preprocessing and feature engineering are. • Learn about vectorization, normalization, and handling missing values • Learn about feature engineering Data Preprocessing and Feature Engineering for Deep Learning Models Learn what data preprocessing and feature engineering are. • Learn about vectorization, normalization, and handling missing values • Learn about feature engineering Data Preprocessing and Feature Engineering for Deep Learning Models Learn what data preprocessing and feature engineering are. • Learn about vectorization, normalization, and handling missing values • Learn about feature engineering Learn what data preprocessing and feature engineering are. • Learn about vectorization, normalization, and handling missing values • Learn about feature engineering Learn what data preprocessing and feature engineering are. • Learn about vectorization, normalization, and handling missing values • Learn about feature engineering Demonstrating Overfitting and Underfitting of Data Distinguish between overfitting and underfitting. • Explore the definitions of the core concepts of Deep Learning • Define the goal of Deep Learning engineers • Demonstrate with graphs Demonstrating Overfitting and Underfitting of Data Distinguish between overfitting and underfitting. • Explore the definitions of the core concepts of Deep Learning • Define the goal of Deep Learning engineers • Demonstrate with graphs Demonstrating Overfitting and Underfitting of Data Distinguish between overfitting and underfitting. • Explore the definitions of the core concepts of Deep Learning • Define the goal of Deep Learning engineers • Demonstrate with graphs Demonstrating Overfitting and Underfitting of Data Distinguish between overfitting and underfitting. • Explore the definitions of the core concepts of Deep Learning • Define the goal of Deep Learning engineers • Demonstrate with graphs Distinguish between overfitting and underfitting. • Explore the definitions of the core concepts of Deep Learning • Define the goal of Deep Learning engineers • Demonstrate with graphs Distinguish between overfitting and underfitting. • Explore the definitions of the core concepts of Deep Learning • Define the goal of Deep Learning engineers • Demonstrate with graphs Deep Learning Basics - Quiz Deep Learning Basics - Quiz Deep Learning Basics - Quiz Deep Learning Basics - Quiz TensorFlow 2.0 for Deep Learning. 5 lectures 14:32 TensorFlow 2.0 Benefits and New Features Learn what is new in TensorFlow 2.0. • Explore PyTorch vs TensorFlow • Explore PyTorch vs TensorFlow 2.0 • Learn about Keras in TensorFlow 2.0 Neural Networks in TensorFlow 2.0 Create a neural network in TensorFlow. • Import libraries • Get and preprocess datasets • Train and evaluate the trained model Getting and Preprocessing MNIST Dataset Get, preprocess, and train a model for MNIST dataset Write a reusable script to load the entire dataset from videos. • Load and shuffle the dataset • Split the dataset into train and test sets Write a reusable script to load the entire dataset from videos. • Load and shuffle the...