AI Development with TensorFlow E-Learning Training

Bestel deze geweldige E-Learning Training AI Development with TensorFlow online cursus!
✔️ 1 jaar 24/7 toegang tot rijke interactieve video's, spraak, praktijkopdrachten, voortgangsbewaking door middel van rapportages en testen per onderwerp om je kennis direct te toetsen.
✔️ Na de cursus ontvang je een certificaat van deelname.

Waarom kiezen voor deze opleiding?

Kunstmatige Intelligentie (AI) is een snelgroeiend vakgebied en wordt steeds belangrijker in zowel de technologie- als de zakenwereld. TensorFlow, een open-source platform ontwikkeld door Google, is een van de meest populaire tools voor het bouwen van AI-modellen, van machine learning tot deep learning.

In deze cursus leer je hoe je AI-oplossingen kunt ontwikkelen met TensorFlow, en krijg je de kans om praktijkervaring op te doen door het bouwen van krachtige modellen voor verschillende toepassingen. Je zult de belangrijkste concepten van machine learning en deep learning begrijpen, en leren hoe je deze kunt toepassen in TensorFlow om complexe taken zoals beeldherkenning, voorspellingen en natuurlijke taalverwerking te automatiseren.

Wat je zult leren:

• Introductie tot TensorFlow: Leer de basisprincipes van TensorFlow en hoe je het kunt gebruiken voor het bouwen van machine learning-modellen.
• Opzetten van een AI-project: Leer hoe je TensorFlow gebruikt om data voor te bereiden, modellen te trainen, en deze te evalueren.
• Deep Learning met TensorFlow: Ontdek hoe je neurale netwerken kunt bouwen voor taken zoals beeldherkenning en spraakherkenning.
• TensorFlow API's en Keras: Leer hoe je TensorFlow en Keras effectief gebruikt om complexe AI-modellen te ontwikkelen.
• Toepassingen van AI: Krijg ervaring in het ontwikkelen van AI-oplossingen voor verschillende domeinen, van gezondheidszorg tot retail.

Wie zou moeten deelnemen?

Deze opleiding is ideaal voor:

• Softwareontwikkelaars die hun kennis willen uitbreiden met machine learning en deep learning.
• Data scientists die TensorFlow willen gebruiken om krachtige AI-modellen te bouwen.
• AI-enthousiastelingen die geïnteresseerd zijn in de praktische kant van AI-ontwikkeling.
• Bedrijfskundigen die AI-technologie willen begrijpen en toepassen in zakelijke processen.
• IT-professionals die zich willen specialiseren in AI en machine learning met TensorFlow.

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

Ga aan de slag met AI Development with TensorFlow!

✔️ Leer op je eigen tempo met interactieve video's, praktijkopdrachten en testen.
✔️ Test je kennis met gerichte toetsen per onderwerp en krijg voortgangsrapportages.
✔️ Bouw krachtige AI-modellen voor diverse toepassingen met TensorFlow.
✔️ Ontvang een certificaat van deelname na succesvolle afronding van de cursus.

Bestel nu jouw cursus en start met het ontwikkelen van geavanceerde AI-oplossingen met