AI Lesson 18 – Intro to TensorFlow | Dataplexa

AI Course

I. AI Foundations
1. Introduction to AI 2. History of AI 3. AI vs ML vs DL 4. Search Algorithms 5. Intelligent Agents 6. Informed & Uninformed Search 7. Constraint Satisfaction 8. Adversarial Search 9. Logic in AI 10. Planning in AI 11. Knowledge Representation 12. Probabilistic AI 13. Bayesian Networks 14. Markov Models 15. AI Ethics 16. AI Applications 17. Intro to PyTorch 18. Intro to TensorFlow 19. AI Workflows 20. Data for AI
II. Machine Learning
21. Introduction to ML 22. ML Workflow 23. Supervised Learning 24. Unsupervised Learning 25. Reinforcement Learning 26. Linear Regression 27. Logistic Regression 28. SVM 29. Decision Trees 30. Random Forest 31. Gradient Boosting 32. XGBoost 33. K-Means 34. Hierarchical Clustering 35. Dimensionality Reduction 36. Feature Engineering 37. Feature Selection 38. Model Evaluation 39. Overfitting vs Underfitting 40. Cross Validation
III. Deep Learning
41. Intro to Deep Learning 42. Neural Networks 43. Activation Functions 44. Backpropagation 45. Loss & Optimizers 46. Regularization 47. CNN Basics 48. CNN Architectures 49. RNN, LSTM, GRU 50. Seq2Seq 51. Transformers 52. Attention 53. Positional Encoding 54. Training DL 55. Hyperparameter Tuning 56. Transfer Learning 57. Autoencoders 58. GAN Basics 59. GAN Applications 60. Model Serving
IV. Natural Language Processing
61. Intro to NLP 62. Text Processing 63. Tokenization 64. Stopwords & Lemmatization 65. BoW & TF-IDF 66. Word Embeddings 67. Sentence Embeddings 68. NLP Classification 69. Sequence Modeling 70. RNN in NLP 71. Transformers in NLP 72. BERT Basics 73. BERT Fine-tuning 74. RoBERTa & DistilBERT 75. Sentiment Analysis 76. NER 77. Machine Translation 78. Text Generation 79. NLP Evaluation 80. NLP Use Cases
V. Computer Vision
81. Intro to CV 82. Image Processing 83. Filters & Edges 84. Convolutions 85. Object Detection 86. Object Tracking 87. Image Segmentation 88. Face Recognition 89. Feature Detection 90. OCR 91. Image Augmentation 92. Generative Vision 93. Vision Transformers 94. Multimodal Models 95. CV Use Cases
VI. LLM Engineering
96. Intro to LLMs 97. Tokenization 98. LLM Architecture 99. Pretraining 100. Fine-tuning 101. RLHF 102. Prompt Engineering 103. Embedding Models 104. Vector Databases 105. LLM Agents 106. Guardrails & Safety 107. AI in Production 108. End-to-End Project

Introduction to TensorFlow

TensorFlow is one of the most widely used deep learning frameworks in the world. While PyTorch is often preferred for research and experimentation, TensorFlow is heavily used in production systems where scalability, deployment, and performance matter.

In this lesson, we will understand what TensorFlow is, why it exists, how it is used in real-world AI systems, and how to write a simple TensorFlow program.

What Is TensorFlow?

TensorFlow is an open-source machine learning and deep learning framework developed by Google. It provides tools to build, train, and deploy AI models across different platforms, including servers, mobile devices, and browsers.

The name “TensorFlow” comes from the idea of flowing tensors (multi-dimensional data) through a series of mathematical operations.

Real-World Connection

TensorFlow is commonly used in:

  • Recommendation systems at large scale
  • Image and video processing pipelines
  • Speech recognition systems
  • AI models deployed on mobile and edge devices

Many companies choose TensorFlow because it provides strong tools for deployment, optimization, and long-term maintenance.

Tensors in TensorFlow

Just like PyTorch, TensorFlow uses tensors as its core data structure. A tensor represents numerical data in one or more dimensions.

Tensors can store:

  • Single values
  • Lists of values
  • Images represented as pixel matrices
  • Large batches of training data

Simple TensorFlow Example

Let’s create a basic tensor and perform a simple operation using TensorFlow. 


import tensorflow as tf

# Create a tensor
x = tf.constant([1, 2, 3])

# Perform an operation
y = x * 2

print(y)
tf.Tensor([2 4 6], shape=(3,), dtype=int32)

Understanding the Code

Let’s break down what the code is doing:

  • tf.constant() creates an immutable tensor
  • TensorFlow supports mathematical operations directly on tensors
  • The multiplication happens element by element
  • The output includes the tensor values, shape, and data type

TensorFlow automatically manages memory and optimizes computations behind the scenes.

TensorFlow vs PyTorch (Conceptual View)

Both frameworks solve similar problems but are designed with different priorities.

  • PyTorch emphasizes flexibility and ease of debugging
  • TensorFlow emphasizes scalability and production readiness
  • TensorFlow provides built-in tools for deployment

In practice, learning both gives developers a strong advantage in real-world AI projects.

When to Use TensorFlow

TensorFlow is a strong choice when:

  • Models need to run at large scale
  • Deployment on mobile or edge devices is required
  • Long-term model maintenance is important
  • Integration with Google ecosystem is needed

Practice Questions

Practice 1: Which company developed TensorFlow?



Practice 2: What is the core data structure used in TensorFlow?



Practice 3: TensorFlow is widely used in which stage of AI systems?



Quick Quiz

Quiz 1: TensorFlow was created by which organization?





Quiz 2: TensorFlow is especially strong in which area?





Quiz 3: What flows through a TensorFlow computation graph?





Coming up next: AI Workflows — how data, models, training, evaluation, and deployment fit together in real AI systems.

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