Deep Learning with TensorFlow || Chrono Learn

Course Details

Data Science
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Deep Learning with TensorFlow

By Chronolearn
19/Jul/2025
0 Students

Course Description

Unlock the future of AI with the Deep Learning with TensorFlow Certified Program — a self-paced, project-based course crafted for aspiring AI engineers, machine learning practitioners, and tech-driven problem solvers. Using the power of TensorFlow 2.x, you'll gain hands-on experience building, training, and optimizing deep learning models for real-world applications. From foundational concepts like tensors and perceptrons to advanced architectures like CNNs, RNNs, and Transfer Learning, this course bridges theory with practical implementation.

What You Will Learn

· TensorFlow fundamentals — tensors, variables, and computation graphs

· Creating and visualizing deep learning models with TensorBoard

· Designing Artificial Neural Networks (ANNs) from scratch

· Mastering activation functions (ReLU, Sigmoid, Softmax) and where to use them

· Building real-time CNNs for image recognition and RNNs for sequence data

Course Curriculum

Foundations of TensorFlow
- Installing TensorFlow (CPU/GPU options)
- Understanding Tensors, Variables, and Operations
- Building Computation Graphs
- Linear Regression in TensorFlow
- TensorBoard for Visual Debugging

Introduction to Neural Networks
- What are ANNs and how do they work?
- Building simple ANN models in TensorFlow
- Layer architecture and training walkthroughs

Activation Functions Explained
- Role of activation functions in deep learning
- Implementing:
  - Step Function
  - Sigmoid
  - ReLU (🔥 Trending in 2025)
  - Softmax (for multi-class output)
  - Gaussian and Linear functions
- Choosing the right function for your use case

Advanced Deep Learning Architectures
- Core principles of deep learning
- Building Convolutional Neural Networks (CNNs)
- Implementing Recurrent Neural Networks (RNNs)
- Techniques:
  - Dropout & Regularization (Essential for reducing overfitting)
  - Batch Normalization (2025 Addition)

Real-World Applications of Deep Learning
- AI in Computer Vision, Healthcare, and Natural Language Processing
- Recommendation Engines with deep learning
- Transfer Learning using pre-trained models for faster development

Gradient Computation & Optimization
- Deep dive into backpropagation
- GradientTape in TensorFlow
- Loss functions & Optimizers
- Debugging vanishing/exploding gradients

Perceptrons & Network Depth
- What is a perceptron?
- Comparing single-layer and multi-layer perceptrons (MLPs)
- Understanding shallow vs deep learning models

Chronolearn

Developer

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