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Item number: 104396925

AI Development with TensorFlow Training

Item number: 104396925

AI Development with TensorFlow Training

289,00 349,69 Incl. tax

Training AI Developing and Machine Learning Solutions with Python - Online E-Learning Course. Order and start immediately for the best price.

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AI Development with TensorFlow E-Learning

Order this great E-Learning Training AI Development with TensorFlow online course  1 year 24/7 access to rich interactive videos, voice, practice assignments, progress monitoring through reports and tests per subject to test the knowledge directly. After the course you will receive a certificate of participation.

Course content

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
Language English
Qualifications of the Instructor Certified
Course Format and Length Teaching videos with subtitles, interactive elements and assignments and tests
Lesson duration 12 Hours
Progress monitoring Yes
Access to Material 365 days
Technical Requirements Computer or mobile device, Stable internet connections Web browsersuch as Chrome, Firefox, Safari or Edge.
Support or Assistance Helpdesk and online knowledge base 24/7
Certification Certificate of participation in PDF format
Price and costs Course price at no extra cost
Cancellation policy and money-back guarantee We assess this on a case-by-case basis
Award Winning E-learning Yes
Tip! Provide a quiet learning environment, time and motivation, audio equipment such as headphones or speakers for audio, account information such as login details to access the e-learning platform.

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