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
Introduction to TensorFlow
- Installing TensorFlow using Docker
- Installing Matplotlib
- Hello World applicatin with TensorFlow
Basic Statistics
- Basic Statistics and Exploratory Analysis
- Descriptive summary statistics with Numpy
- Summarize continous and categorical data
- Outlier analysis
Machine Learning Introduction
- Machine learning essentials
- Data representation and features
- Distance metrics
- Supervised learning
- Unsupervised learning
- Reinforcement learning
- Theano, Caffe, Torch, CGT, and TensorFlow
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
ML Algorithm - Classification in TensorFlow
- Classification problems
- Using linear regression for classification
- Using logistic regression (including multi-dimensional input)
- Multiclass classifiers (such as softmax regression)
- Hands on Classificatin with TensorFlow
ML Algorithm - Clustering in TensorFlow
- Traversing files in TensorFlow
- K-means clustering
- Clustering using a self-organizing map
Simple Neural Networks in TensorFlow
- Introduction to Neural Networks
- Batch training
- Variational, denoising and stacked autoencoders
Reinforcement learning
- Concept of Reinforcement Learning
- Simple model applying Reinforcement Learning in TensorFlow
Convolutional and Recurrent Neural Networks
- Advantages and disadvantages of neural networks
- Convolutional neural networks
- The idea of contextual information
- Recurrent neural networks
- Real world predictive model - example
Case study - Stock Market Analsis with TensorFlow
- Case study - Stock Market Analsis
- Hands on Coding in TensorFlow