AI Development with TensorFlow E-Learning Kurs

Explore the concept of machine

 

Course Overview
Introduction to Machine Learning Algorithms
Understanding Machine Learning
Understanding Deep Learning
Supervised and Unsupervised Learning
TensorFlow for Machine Learning
Tensors and Operators
Understanding How to Install TensorFlow
Installing TensorFlow on the Local Machine
Working with Constants
The Computation Graph with TensorBoard
Working with Variables and Placeholders
Variables and Placeholders on TensorBoard
Updating Variables in a Session
Feed Dictionaries
Named Scopes for Better Visualization
Eager Execution
Exercise: Machine Learning and TensorFlow
Exercise: Working with Computation Graph

Explore how to how to build and

 

Course Overview
Understanding Linear Regression
Gradient Descent and Optimizers
Explore the Boston Housing Prices Dataset
Creating Training and Test Datasets for Regression
Base Model with scikit-learn
Setting up the Linear Regression Computation Graph
Train and Visualize the Linear Regression Model
Visualize the Model with TensorBoard
The High-Level Estimator API
Linear Regression with Estimators
Prediction Using Estimators
Understanding Binary Classification
The Cross Entropy Loss Function and Softmax
Continuous and Categorical Data
Creating Training & Test Datasets for Classification
Binary Classification Using Estimators
Exercise: Working with Linear Regression
Exercise: Working with Binary Classification

Discover how to apply deep learning

 

ourse Overview
Neural Networks and Deep Learning
Basic Structure of a Neural Network
The Mathematical Function Applied By a Neuron
Linear Transformation and Activation Functions
Training a Neural Network Using Gradient Descent
Forward Pass and Backward Pass
Image Representations in Machine Learning
Set Up TensorFlow and Use Jupyter Notebooks
The MNIST Dataset
Training an Estimator for Image Classification
Predicting Image Labels
Drawbacks of Deep Neural Networks for Images
Exercise: Working with Neural Networks
Exercise: Working with Image Classification

Examine how to work with

 

Course Overview
Neural Networks and Deep Learning
Basic Structure of a Neural Network
The Mathematical Function Applied By a Neuron
Linear Transformation and Activation Functions
Training a Neural Network Using Gradient Descent
Forward Pass and Backward Pass
Image Representations in Machine Learning
Set Up TensorFlow and Use Jupyter Notebooks
The MNIST Dataset
Training an Estimator for Image Classification
Predicting Image Labels
Drawbacks of Deep Neural Networks for Images
Exercise: Working with Neural Networks
Exercise: Working with Image Classification

Explore how to model language and

 

Course Overview
One-Hot Encoding of Words
Frequency-Based Encoding
Prediction-Based Encoding
Word2vec and GloVe Embeddings
Recurrent Neurons
Unrolling a Recurrent Memory Cell
Training a Recurrent Neural Network
Long Memory Cells
Exercise: Working with Word Encoding
Exercise: Working with Recurrent Neural Networks

Discover how to construct neural

 

Course Overview
Configuring the TensorFlow Environment
Training Data
Data Pre-Processing
Unique Identifiers to Represent Words
Construct a Recurrent Neural Network
Training the Neural Network
Data Pre-Processing to Use Pre-Trained Word Vectors
Lookup Table to Map Unique Identifiers
Sentences Using Word Identifiers
Sentiment Analysis Using Pre-Trained Vectors
Exercise: Performing Sentiment Analysis

 

Course Overview
Supervised vs. Unsupervised Learning
Supervised Learning Characteristics
Unsupervised Learning Characteristics
Unsupervised Learning Use Cases
Objectives of Clustering Techniques
K-means Clustering
K-means Clustering Algorithm
Install TensorFlow and Work with Jupyter Notebooks
Generate Random Data for K-means Clustering
K-means Clustering Using Estimators
The Iris Dataset
Clustering the Iris Dataset
Exercise: Working with Unsupervised Learning
Exercise: Working with Clustering