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 (2389 reviews)

Udemy

platform

English

language

Data Science

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

Course Gallery

Deep Learning with TensorFlow 2.0 – Screenshot 1 — Screenshot 1 – Deep Learning with TensorFlow 2.0

Deep Learning with TensorFlow 2.0 – Screenshot 2 — Screenshot 2 – Deep Learning with TensorFlow 2.0

Deep Learning with TensorFlow 2.0 – Screenshot 3 — Screenshot 3 – Deep Learning with TensorFlow 2.0

Deep Learning with TensorFlow 2.0 – Screenshot 4 — Screenshot 4 – Deep Learning with TensorFlow 2.0

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Comidoc Review

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mathematical explanation, coding exercises, and high production qualities, some users may face challenges related to outdated content specific to the installation process or desired application focus and depth.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