Deep Learning in TensorFlow #6 L1 - Keras Functional API: Introduction & How to use Functional API

⭐️About this Course This Deep Learning in TensorFlow Specialization is a foundational program that will help you understand the principles and Python code of Machine Learning and Deep Learning and prepare you to participate in the development of leading-edge AI and data scientist technology. In this Specialization, you will build and train neural network architectures with some hands-on project, such as Vanilla Neural Networks, Convolutional Neural Networks, Recurrent Neural Networks, LSTMs, and learn the advance techniques on how to make them better with strategies. 🌟🌟🌟 Earn a Certificate [MEMBERS only] When you finish every course and complete the hands-on project and a final project assessment, you'll earn a Certificate that you can share with prospective employers. ============================================================================ ⭐️⭐️⭐️Join this channel to get access to Members-only resources: youtube.com/channel/UCtfTf1nNJQ4PbUDqj-Q48rw/join ⭐️⭐️⭐️Support FREE content: www.buymeacoffee.com/eMasterClass ============================================================================ What you will learn from this course: Course 1 Numpy Basics - Introduction to Tensors for Deep Learning with NumPy - NumPy Structure - NumPy Properties & Attributes - NumPy Array Creation - NumPy Indexing and Slicing - NumPy Shape Manipulation - NumPy Element-wise VS Broadcasting - NumPy Aggregate and Statistical Functions - NumPy Dot Product and Matrix Multiplication Course 2 Neural Networks in TensorFlow - A Gentle Introduction of AI, ML and NN - Logistic Regression - From Logistic Regression to Neural Network - Build your first Neural Network Hands-on Project - Image Classification Course 3 Neural Networks in TensorFlow - Advanced Techniques - Introduction & Sequential Model - Sequential Model - Attributes - Sequential Model - Save and load models - Sequential Model - Compile() - Frequently Used Optimizers - Frequently Used Loss Functions - Frequently Used Metrics - Sequential Model - Fit() - Usage of Returns - Usage of Callbacks - ModelCheckPoint - TensorBoard - EarlyStopping - Usage of Batch Size - Sequential Model - Evaluate() - Sequential Model - Predict() Course 4 Convolutional Neural Networks in TensorFlow - Introduction & Basic Architecture - Build your first Convolutional Neural Network - Convolutional Layer - Kernel, Strides, Padding - Activation - Pooling Layer - Maximum Pooling - Average Pooling - Flatten & Dense Layer Course 5 Recurrent Neural Networks in TensorFlow - Introduction - Mathematical Representations - Build your first Recurrent Neural Network - Recurrent Neural Networks - Vanishing and Exploding Gradients - Solutions - Long Short-Term Memory (LSTM) Networks - Introduction - Core Concept of LSTMs - How LSTMs work - Summary Hands-on Project - LSTM model for Image Classification Course 6 Keras Functional API - Introduction - Build a Neural Network with Functional API - Features - Use the same graph of layers to define multiple models - Callable model - Manipulate complex graph topologies - Shared layers - Extract and reuse nodes Hands-on Project - ResNet model for Image Classificat