Artificial Intelligence AI Development with TensorFlow E-Learning Kurs
AI Development with TensorFlow E-Learning Kurs
€311,89
Artificial Intelligence

AI Development with TensorFlow E-Learning Kurs

EUR 311,89 exkl. MwSt.

KI-Entwicklung mit TensorFlow trainieren - Online-E-Learning-Kurs. Bestellen und sofort zum besten Preis starten.

  • E-Learning - Online-Zugang: 365 Tage
  • Englische Sprache
  • Teilnahmeurkundede
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Produktbeschreibung

AI Development with TensorFlow E-Learning Ausbildung

Bestellen Sie dieses großartige E-Learning-Ausbildung AI Development mit TensorFlow Online-Kurs, 1 Jahr 24/7 Zugriff auf umfangreiche interaktive Videos, Sprache, praktische Aufgaben, Fortschrittsüberwachung durch Berichte und Tests nach Testwissen direkt. Nach dem Kurs erhalten Sie eine Teilnahmebescheinigung.

Kursinhalt

TensorFlow: Introduction to Machine Learning

Course: 1 Hour, 41 Minutes

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

TensorFlow: Simple Regression and Classification Models

Course: 1 Hour, 38 Minutes

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

TensorFlow: Deep Neural Networks and Image Classification

Course: 1 Hour, 18 Minutes

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

TensorFlow: Convolutional Neural Networks for Image Classification

Course: 1 Hour, 21 Minutes

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

Tensorflow: Word Embeddings & Recurrent Neural Networks

Course: 40 Minutes

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

Tensorflow: Sentiment Analysis with Recurrent Neural Networks

Course: 58 Minutes
 
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

Tensorflow: K-means Clustering with TensorFlow

Course: 1 Hour

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

Tensorflow: Building Autoencoders in TensorFlow

Course: 47 Minutes

Course Overview
Efficient Representation of Data Using Encodings
Autoencoders
Principal Component Analysis
Performing Principal Component Analysis on Datasets
Principal Component Analysis with scikit-learn
Autoencoders for Principal Component Analysis
The Fashion MNIST Dataset
Autoencoders for Dimensionality Reduction
Exercise: Working with Autoencoders

Tensorflow: Word Embeddings & Recurrent Neural Networks

Course: 44 Minutes

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 Cells3
Exercise: Working with Word Encoding
Exercise: Working with Recurrent Neural Networks

TensorFlow: Convolutional Neural Networks for Image Classification

Course: 1 Hour, 23 Minutes

Course Overview2
The Visual Cortex
Convolution and Convolutional Layers7
Image as an Input Matrix
Convolution Kernel and Convolutional Layer
Edge Detection Using Convolution
Pooling and Pooling Layers
Zero-Padding and Stride Size
Convolutional Neural Network Architecture
Overfitting and the Bias-Variance Trade-Off
Preventing Overfitting
The CIFAR-10 Dataset
Training and Test Dataset for Image Classification
Placeholders and Variables for the CNN
CNN for Image Classification
Train and Predict Using a CNN
Exercise: Working with CNNs

TensorFlow: Deep Neural Networks and Image Classification

Course: 1 Hour, 18 Minutes

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

 
 
Eigenschaften
Unterrichtsdauer
Sprache Englisch
Online-Zugang 365 Tage
Teilnahmeurkunde Ja
Preisgekröntes Online-Training Ja
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