DEEP LEARNING - TENSORFLOW

Introduction to Deep Learning

What is a neural network?

Supervised Learning with Neural Networks - Python

How Deep Learning is different from Machine Learning

Overview of Machine Learning Concepts

What is Machine Learning?

Supervised Machine Learning algorithms

K-Nearest Neighbors (KNN) concept and application

Naive Bayes concept and application

Logistic Regression concept and application

Classification Trees concept and application

Unsupervised Machine Learning algorithms

Clustering with K-means concept and application

Hierarchial Clustering concept and application

TensorFlow Essentials

Representing tensors

Creating operators and excuting with sessions

Introduction Jupyter notebook for TensorFlow coding

TensorFlow variables

Visualizing data using TensorBoard

ML Algorithm - Linear Regression in TensorFlow

Regression problems

Linear regression applications

Regularization

Available datasets

Coding Linear Regression with TensorFlow - Case study

Deep Neural Networks in TensorFlow

Basic Neural Nets

Single Hidden Layer Model

Multiple Hidden Layer Model

Convolutional Neural Networks

Introduction to Convolutional Neural Networks

Input Pipeline

Introduction to RNN, LSTM, GRU

Reinforcement Learning in Tensorflow

Concept of Reinforcement Learning

Simple model applying Reinforcement Learning in TensorFlow

Hands on Deep Learning Application with TensorFlow

Example Application - Case study

Hands on building the Deep Learning application with TensorFlow