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Tensorflow Fundamentals

Course Summary

This introductory course is designed to provide a comprehensive overview of TensorFlow, a leading open-source machine learning framework developed by Google. This course will be conducted entirely in Google Colab, a free cloud-based platform that allows for the execution of Python code and TensorFlow operations without any local setup. The course aims to equip learners with the essential knowledge and skills to build, train, and deploy machine learning models using TensorFlow.

Purpose
Gain essential knowledge and skills to build, train, and deploy machine learning
models using TensorFlow.
Audience
Beginners in machine learning and deep learning.
Data scientists and analysts looking to expand their skill set.
Software developers and engineers interested in AI technologies.
Students and academics seeking practical experience in TensorFlow.
Role
Data Scientists/Analysts | Developers
Skill level
Beginner
Style
Lectures | Hands-on Activities
Duration
3 days
Related technologies
Python | Machine Learning | PyTorch

 

Productivity objectives
  • Learn how to implement statistical and deep learning models using PyTorch
  • Understand the Basics of TensorFlow and Google Colab
  • Master Tensors and TensorFlow Operations
  • Grasp Graphs and Eager Execution Concepts
  • Build and Train Neural Networks using Keras
  • Handle Data Preprocessing and Augmentation
  • Evaluate and Optimize Machine Learning Models

What you'll learn:

In this course, you'll learn:
  • Introduction to TensorFlow and Google Colab
    • Overview of TensorFlow and its significance in machine learning.
    • Navigating and setting up Google Colab.
    • Basics of Python programming for TensorFlow.
  • Understanding Tensors and Operations
    • Introduction to tensors, the core concept in TensorFlow.
    • Basic tensor operations and manipulations.
    • Understanding data types and shapes.
  • Graphs and Eager Execution
    • Understanding the computational graph in TensorFlow.
    • Transition from TensorFlow 1.x to 2.x.
    • Eager execution and its advantages.
  • Building Neural Networks with Keras
    • Introduction to the Keras API in TensorFlow.
    • Designing and implementing neural network architectures.
    • Layers, activation functions, and model compilation.
  • Data Preprocessing and Augmentation
    • Data handling and preprocessing techniques.
    • Image and text data processing.
    • Data augmentation methods in TensorFlow.
  • Model Training and Evaluation
    • Setting up training loops.
    • Evaluating model performance.
    • Overfitting, underfitting, and techniques to combat them.
  • Advanced Topics in TensorFlow (Optional)
    • Introduction to Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs).
    • Basics of TensorFlow Lite for mobile and edge devices.
  • Capstone Project
    • Applying learned skills to a real-world problem.
    • Guidance on project selection and implementation.
    • Peer-review and feedback.
real world content

Real-World Content

Project-focused demos and labs using your tool stack and environment, not some canned "training room" lab.
expert practitioners

Expert Practitioners

Industry experts that bring their battle scars into the classroom.
experiential learning

Experiential Learning

More coding than lecture, coupled with architectural and design discussions.
fully customized

Tailored Outlines

Once-size-fits-all doesn't apply to training teams. That's where we come in!

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