TensorFlow Online Course


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Sale price€399.50

Various frameworks have been made to help developers and enterprises implement deep learning models since implementing deep learning models from scratch is a tough and time-consuming process. TensorFlow is one of the most popular frameworks for designing, training, and implementing machine learning and deep learning models. It is an open-source software library created by the Google Brain to make the computing load easier and faster for machine/deep learning applications. Python is one of the programming languages that can interact with this library. TensorFlow assists in every step of data science workflow from implementation to deployments, such as visualization, model development, and evaluation.

TechClass TensorFlow online course aims to provide a practical approach to machine learning and deep learning using TensorFlow with Python. It brings you hands-on experience building your predictive models, state-of-the-art image classifiers, and deep neural networks using TensorFlow and Keras. By the end of this course, you will gain hands-on experience in designing, training, and evaluating variations of neural networks and learn the techniques required for working with large real-world datasets.

Learning outcomes

  • Get familiar with TensorFlow and Keras, and their capabilities
  • Learn how to set up and get started with TensorFlow in the Google Colab environment
  • Learn the differences between implementing a simple linear regression model in TensorFlow and in NumPy
  • Gain hands-on experience building and training neural network models with Keras using Sequential and Functional APIs
  • Gain hands-on experience evaluating neural networks and making predictions using them
  • Learn how to implement callbacks in TensorFlow
  • Get familiar with L1 and L2 regularizations and how to employ them to avoid overfitting
  • Learn how to use early stopping, dropout, and batch normalization techniques to avoid overfitting
  • Get familiar with convolutional neural networks (CNNs), different layers of CNN, and popular CNN architectures
  • Gain hands-on experience implementing CNN in TensorFlow for computer vision tasks
  • Learn how to analyze the performance of CNN after training
  • Understand the concept of transfer learning and how transfer learning models are made
  • Learn how to implement transfer learning models in TensorFlow
  • Get familiar with TensorFlow Hub and how to use it for transfer learning applications

Table of contents

Chapter 1: Intro to Course 

  • 1.1. Welcome! 
  • 1.2. About TechClass Data Science Department 
  • 1.3. Learning Outcomes 
  • 1.4. Your Expectations, Goals, and Knowledge
  • 1.5. Abbreviations
  • 1.6. Copyright Notice

Chapter 2: Introduction

  • 2.1. Machine Learning Pipeline: Part I
  • 2.2. Machine Learning Pipeline: Part II
  • 2.3. How do Deep Learning Algorithms Work?
  • 2.4. Top Deep Learning Algorithms
  • 2.5. Deep Learning Frameworks
  • 2.6. What is TensorFlow?
  • 2.7. TensorFlow 1.x vs. TensorFlow 2.x
  • 2.8. Getting Started with Google Colab
  • 2.9. Setting up TensorFlow

Chapter 3: Python Overview

  • 3.1. Variables and Operators
  • 3.2. Data Structures
  • 3.3. Conditional Statements
  • 3.4. Loops
  • 3.5. Exercise

Chapter 4: Building Models using Keras

  • 4.1. Machine Learning Models in General
  • 4.2. Linear Regression and Neuron
  • 4.3. A Review on Cost Function and MSE
  • 4.4. Gradient Descent Algorithm
  • 4.5. Linear Regression: NumPy Implementation
  • 4.6. Linear Regression: TensorFlow Implementation
  • 4.7. What is Keras? Part I
  • 4.8. What is Keras? Part II
  • 4.9. Basic Neural Network Sequential Model: Part I
  • 4.10. Basic Neural Network Sequential Model: Part II
  • 4.11. Fitting, Evaluation, and Prediction: Part I
  • 4.12. Fitting, Evaluation, and Prediction: Part II
  • 4.13. Functional API Models: Part I
  • 4.14. Functional API Models: Part II
  • 4.15. Handwritten Digit Recognition using Neural Network: Preprocessing
  • 4.16. Handwritten Digit Recognition using Neural Network: Building the Model
  • 4.17. Callbacks: Part I
  • 4.18. Callbacks: Part II

    Chapter 5: Convolutional Neural Networks

    • 5.1. Why CNN?
    • 5.2. Why CNN Over Feed-Forward Neural Networks?
    • 5.3. CNN Layers: Part I
    • 5.4. CNN Layers: Part II
    • 5.5. Typical CNN Architecture
    • 5.6. Implementing CNN in TensorFlow: Sequential API
    • 5.7. Implementing CNN in TensorFlow: Functional API
    • 5.8. Popular CNN Architectures for Computer Vision
    • 5.9. Training and Evaluating CNN for CIFAR10 Dataset: Preprocessing
    • 5.10. Training and Evaluating CNN for CIFAR10 Dataset: Model Training

      Chapter 6: Handling Overfitting

      • 6.1. What is Overfitting?
      • 6.2. Prevent Overfitting Approaches
      • 6.3. Regularization: Basics
      • 6.4. L1 and L2 Regularizations
      • 6.5. Quiz
      • 6.6. Early Stopping
      • 6.7. Dropout
      • 6.8. Batch Normalization

        Chapter 7: Saving and Loading Models

        • 7.1. Introduction
        • 7.2. Whole-model Saving
        • 7.3. Whole-model Loading
        • 7.4. Saving the Architecture
        • 7.5. Loading the Architecture
        • 7.6. Saving Model Weights
        • 7.7. Loading Model Weights

        Chapter 8: Transfer Learning

        • 8.1. What is Transfer Learning?
        • 8.2. How to Use Transfer Learning?
        • 8.3. Loading Pre-trained Models in Keras
        • 8.4. Pre-trained Model as Classifier
        • 8.5. Pre-trained Model as Standalone Feature Extractor
        • 8.6. Pre-trained Model as Integrated Feature Extractor
        • 8.7. TensorFlow Hub

        Chapter 9: Final Tasks

        • 9.1. Project
        • 9.2. Self-study Essay

        Chapter 10: Finishing the Course

        • 10.1. What We Have Learned
        • 10.2. Where to Go Next?
        • 10.3. Your Opinion Matters
        • 10.4. Congrats! You did it!

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        Customer Reviews

        Based on 4 reviews
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        S
        Sam Jansen
        Great course material

        It was beneficial for me to be explained step by step in the implementations.

        J
        Jaxon Green
        Fall in love with deep learning with Tensorflow

        You excellently arranged the content of this course. It moved from simple to complex. At the end of this course, I learned everything you need to know about implementing CNN projects with the help of TensorFlow.

        L
        Levente Kiss
        Deep dive into Tensorflow

        I was looking to learn the Tensorflow framework, and I was able to meet my needs well with the contents of this course.

        P
        Pekka Haapala
        overall a great course

        The topics were well organised and the course platform is good. The final project was nice.

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