In this workshop, you will learn how to get started with deep learning using one of the most popular frameworks for implementing deep learning – TensorFlow. You will also use another API – Keras, which is built on top of TensorFlow, to make deep learning more user-friendly and easier.

Topics

- Introduction to Neural Networks
- Deep Learning and Neural Networks
-- Perceptron and Neural Networks
-- Layers, Weights and Biases
-- Activation Functions
--- Softmax
--- ReLu
--- Leaky ReLu
-- Back Propagation
-- Loss Functions
--- Binary cross entrophy
--- Categorical cross entrophy
--- Mean-squared error
-- Optimizers - Gradient Descent, RMSprop, Adam
-- Evaluating Performance

- Common Types of Neural Networks
-- Convolutional Neural Network (CNN)
-- Recurrent Neural Network (RNN)

- What is TensorFlow?
-- What is a Tensor?
-- Basic TensorFlow Operations
-- Graph and Session
-- Mathematical OperationsMatrices
-- Variables and Constants
-- Placeholders
-- Visualizing your graph using TensorBoard
-- Building a Perceptron using TensorFlow
-- Using Keras with TensorFlow
-- Image Classifications
-- Text Classifications
-- Custom Image Recognizer
-- Transfer Learning
-- What is Transfer Learning?
-- Using pre-trained models
-- Fine-tuning pre-trained models

For Who:
You will benefit from this course if you are:

- A developer who wants to learn deep learning and build machine learning models
- A student planning to major in machine learning Prerequisites

Prerequisites

- Basic programming experience
- Understanding of basic object-oriented programming concepts

Hardware:

- Mac / Windows laptop

Software:

- Anaconda (Python 3.7)
- Visual Studio Code