Deep Learning in TensorFlow #6 L6 - Keras Functional API: ResNet model for Image Classification
⭐️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.
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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 Classification
