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TensorFlow Developer - iNovAITec

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Just launched with all modern best practices for building  neural networks with TensorFlow and passing the TensorFlow Developer  Certificate exam!
Join a live online community  of over 900,000+ students and a course taught by a TensorFlow certified  expert. This course will take you from absolute beginner with TensorFlow, to creating state-of-the-art deep learning neural networks  and becoming part of Google's TensorFlow Certification Network.

The goal of this course is to teach you all the skills necessary for you  to go and pass this exam and get your TensorFlow Certification from  Google so you can display it on your resume, LinkedIn, Github and other  social media platforms to truly make you stand out.

This course will be very hands on and project based. You won't just  be staring at us teach, but you will actually get to experiment, do  exercises, and build machine learning models and projects to mimic real  life scenarios. Most importantly, we will show you what the TensorFlow  exam will look like for you. By the end of it all, you will develop skillsets needed to develop modern deep learning solutions that big tech companies encounter.

Here is what you will get with this course:

TensorFlow Fundamentals
  • Introduction to tensors (creating tensors)
  • Getting information from tensors (tensor attributes)
  • Manipulating tensors (tensor operations)
  • Tensors and NumPy
  • Using GPUs with TensorFlow

Neural Network Regression with TensorFlow
  • Build TensorFlow sequential models with multiple layers
  • Prepare data for use with a machine learning model
  • Learn the different components which make up a deep learning model (loss function, architecture, optimization function)
  • Learn how to diagnose a regression problem (predicting a number) and build a neural network for it

Neural Network Classification with TensorFlow
  • Learn how to diagnose a classification problem (predicting whether something is one thing or another)
  • Build, compile & train machine learning classification models using TensorFlow
  • Build and train models for binary and multi-class classification
  • Plot modelling performance metrics against each other
  • Match input (training data shape) and output shapes (prediction data target)

Computer Vision and Convolutional Neural Networks (CNN) with TensorFlow
  • Build convolutional neural networks with Conv2D and pooling layers
  • Learn how to diagnose different kinds of computer vision problems
  • Learn to how to build computer vision neural networks
  • Learn how to use real-world images with your computer vision models

Transfer Learning with TensorFlow Part 1: Feature Extraction
  • Learn how to use pre-trained models to extract features from your own data
  • Learn how to use TensorFlow Hub for pre-trained models
  • Learn how to use TensorBoard to compare the performance of several different models

Transfer Learning with TensorFlow Part 2: Fine-tuning
  • Learn how to setup and run several machine learning experiments
  • Learn how to use data augmentation to increase the diversity of your training data
  • Learn how to fine-tune a pre-trained model to your own custom problem
  • Learn how to use Callbacks to add functionality to your model during training

Transfer Learning with TensorFlow Part 3: Scaling Up (Food/Image Vision mini)
  • Learn how to scale up an existing model
  • Learn to how evaluate your machine learning models by finding the most wrong predictions
  • Beat the original Food-Image-101 paper using only 10% of the data

Milestone Project 1: Food/Image Vision
  • Combine everything you've learned in the previous 6 notebooks to build  Food Vision: a computer vision model able to classify 101 different  kinds of foods. Our model well and truly beats the original Food-Image-101  paper.

Natural Language Processing (NLP) Fundamentals in TensorFlow
  • Preprocess natural language text to be used with a neural network
  • Create word embeddings (numerical representations of text) with TensorFlow
  • Build neural networks capable of binary and multi-class classification using:
      • RNNs (recurrent neural networks)
      • LSTMs (long short-term memory cells)
      • GRUs (gated recurrent units)
      • CNNs (Convolutional Neural Networks)
  • Learn how to evaluate your NLP models

Milestone Project 2: Audio Recognition
  • Audio-Visual Automatic Speech Recognition has become the most  promising research area when the audio signal gets corrupted by noise.

Time Series fundamentals in TensorFlow
  • Learn how to diagnose a time series problem (building a model to make predictions based on data across time, e.g. predicting the stock price of AAPL tomorrow)
  • Prepare data for time series neural networks (features and labels)
  • Understanding and using different time series evaluation methods
      • MAE — mean absolute error
  • Build time series forecasting models with TensorFlow
      • RNNs (recurrent neural networks)
      • CNNs (convolutional neural networks)

Who this course is for:
  • Anyone who wants to pass the TensorFlow Developer exam so they can join  Google's Certificate Network and display their certificate and badges on  their resume, GitHub, and social media platforms including LinkedIn,  making it easy to share their level of TensorFlow expertise with the  world
  • Students, developers, and data scientists who want to demonstrate  practical machine learning skills through the building and training of  models using TensorFlow
  • Anyone looking to expand their knowledge when it comes to AI, Machine Learning and Deep Learning
  • Anyone looking to master building ML models with the latest version of TensorFlow

4 Weeks


  • Developers
  • Anyone interested in Artificial Intelligence (AI), Machine Learning  (ML) or Deep Learning (DL)
If you are interested in a company-specific custom development and  would like to find out more, please feel free to get in touch with us.

Give us a call on: +49 (0)  176 310 693 62
or send an email to:

Alternatively,  You can fill out our contact form here. We look forward to hearing from you.

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