Deep Learning with TensorFlow 2.0
Build Deep Learning Algorithms with TensorFlow 2.0, Dive into Neural Networks and Apply Your Skills in a Business Case
4.66 (2385 reviews)
21 319
students
6 hours
content
May 2023
last update
$19.99
regular price
What you will learn
Gain a Strong Understanding of TensorFlow - Google’s Cutting-Edge Deep Learning Framework
Build Deep Learning Algorithms from Scratch in Python Using NumPy and TensorFlow
Set Yourself Apart with Hands-on Deep and Machine Learning Experience
Grasp the Mathematics Behind Deep Learning Algorithms
Understand Backpropagation, Stochastic Gradient Descent, Batching, Momentum, and Learning Rate Schedules
Know the Ins and Outs of Underfitting, Overfitting, Training, Validation, Testing, Early Stopping, and Initialization
Competently Carry Out Pre-Processing, Standardization, Normalization, and One-Hot Encoding
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Our Verdict
Deep Learning with TensorFlow 2.0 is an insightful course that equips learners with a strong understanding of deep learning algorithms by diving into technical details and practical implementation using TensorFlow. While it offers numerous strengths including in-depth mathematical explanation, coding exercises, and high production qualities, some users may face challenges related to outdated content specific to the installation process or desired application focus and depth.
What We Liked
- Covers the mathematical reasoning behind neural networks, providing a solid understanding of model performance optimization and explanation
- Excellent for gaining hands-on experience using TensorFlow 2.0 to build deep learning algorithms from scratch
- Clear, well-structured explanations help learners grasp complex topics like backpropagation and stochastic gradient descent
- High-quality content with helpful coding exercises, links to additional information, and production qualities that enhance the learning experience
Potential Drawbacks
- Some recent users encountered issues installing TensorFlow 2.0 and running course code; these may be resolved by using Anaconda Navigator or forcing the installation of version 2.0.0
- Minimal coverage of specific application cases, with heavier emphasis on feed-forward architecture in a business case scenario
- Lacks focus on interpretable machine learning techniques to address the black box issue of Neural Network models
- A few reviewers found the pacing too fast and would have liked more detail on data preprocessing techniques
Related Topics
1420956
udemy ID
04/11/2017
course created date
10/05/2019
course indexed date
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