Artificial Intelligence - Development with TensorFlow - Deep Learning - Machine learning - Data Science

Na inschrijving van de Award Winning E-learning training AI Development with TensorFlow, ontvangt u per e-mail een link om in te loggen in uw leeromgeving waar u een persoonlijk wachtwoord aanmaakt. Eenmaal ingelogd in uw persoonlijke leeromgeving ziet u een overzicht van de cursusonderdelen. Via de inhoudsopgave schakelt u automatisch over naar elk gewenst onderdeel van de training.

Er zit tevens een Voortgangsbewaking bij om eenvoudig te zien hoe ver u bent binnen uw training. U kunt daarbij 1 jaar lang (365 dagen), 24/7 (elke dag en nacht) inloggen om verder te gaan met de training.

Deze Engelstalige training met ondertiteling heeft interactieve, eenvoudig te volgen video's in HD beeldkwaliteit met heldere audio kwaliteit. Daarnaast biedt de training Support en/of een Online Mentor aan als u problemen ondervindt. De training is beschikbaar in elke browser voor zowel PC, Mac, Tablet én Smartphone. Dus zelfs via uw mobiel kunt u handig de training volgen.

De training is inclusief lees- en/of praktijkopdrachten met trainingstest, mits noodzakelijk voor de training. Na afronding van de training krijgt u een Certificaat van Deelname en sluit u zich aan bij de reeds duizenden tevreden cursisten.

Cursusinhoud

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 Overview
• The Visual Cortex
• Convolution and Convolutional Layers
• 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

Specificaties

Taal: Engels
Kwalificaties van de Instructeur: Gecertificeerd
Cursusformaat en Lengte: Lesvideo's met ondertiteling, interactieve elementen en opdrachten en testen
Lesduur: 12 uur
Voortgangsbewaking: Ja
Toegang tot Materiaal: 365 dagen
Technische Vereisten: Computer of mobiel apparaat, Stabiele internetverbindingen Webbrowserzoals Chrome, Firefox, Safari of Edge.
Support of Ondersteuning: Helpdesk en online kennisbank 24/7
Certificering: Certificaat van deelname in PDF formaat
Prijs en Kosten: Cursusprijs zonder extra kosten
Annuleringsbeleid en Geld-Terug-Garantie: Wij beoordelen dit per situatie
Award Winning E-learning: Ja

Tip! Zorg voor een rustige leeromgeving, tijd en motivatie, audioapparatuur zoals een koptelefoon of luidsprekers voor audio, accountinformatie zoals inloggegevens voor toegang tot het e-learning platform.