Tensor Flow

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Why TensorFlow Processing is Important
in Data Science and AI

TensorFlow is a powerful open-source library developed by Google for building and deploying scalable machine learning and deep learning models.

It supports both research and production-grade workflows, making it ideal for academic projects and industry use.

  • With TensorFlow, you can build neural networks, deep learning models, and even complex architectures like GANs and Transformers.

  • Its support for GPU/TPU acceleration ensures high-performance training and inference for large datasets.

  • TensorFlow offers an easy-to-use high-level API (tf.keras), making it accessible for beginners and scalable for professionals.

  • It integrates well with tools like TensorBoard, TFX, and TensorFlow Lite, enabling model tracking, deployment, and mobile AI.

  • It powers many real-world AI applications, from voice recognition and image classification to NLP and recommender systems.

  • TensorFlow’s auto-differentiation and graph computation make it suitable for gradient-based learning and optimization.

  • It supports cross-platform deployment—from cloud to edge devices—ensuring models run in diverse environments.

  • Mastering TensorFlow provides a solid foundation for building and deploying production-ready AI systems.

tensorflow
01

Module 1: Introduction to TensorFlow

  1. What is TensorFlow?

  2. History and ecosystem overview

  3. TensorFlow vs PyTorch

  4. Installing TensorFlow and setting up the environment

  5. First "Hello, World" in TensorFlow

02

Module 2: TensorFlow Fundamentals

  1. Understanding tensors (scalars, vectors, matrices)

  2. Tensor operations and broadcasting

  3. Tensor datatypes and shapes

  4. Creating tensors from arrays and constants

  5. Indexing, slicing, reshaping tensors

03

Module 3: TensorFlow Computation Graphs

  1. Eager Execution vs Graph Execution

  2. Building a computation graph

  3. Sessions and lazy execution (historical overview)

  4. tf.function and Autograph

04

Module 4: Automatic Differentiation and Gradients

  1. Introduction to gradients and backpropagation

  2. tf.GradientTape() for custom training

  3. Computing gradients for scalar and vector functions

  4. Chain rule in deep learning models

05

Module 5: Building Neural Networks with tf.keras

  1. Overview of Keras API in TensorFlow

  2. Sequential API:

    1. Dense layers, activation functions

  3. Functional API:

    1. Multi-input/output models

    2. Shared layers and complex architectures

  4. Model compilation and training (fit())

  5. Model evaluation and prediction

06

Module 6: Data Input Pipeline

  1. Working with NumPy arrays and Pandas in TensorFlow

  2. TensorFlow Datasets (tf.data.Dataset)

  3. Data loading and batching

  4. Data augmentation and preprocessing

  5. Shuffling, caching, prefetching

07

Module 7: Model Training and Optimization

  1. Loss functions and optimizers

  2. Learning rate scheduling

  3. Custom training loops:

    1. Writing training step manually

    2. Using @tf.function for performance

  4. Early stopping and checkpointing

08

Module 8: Convolutional Neural Networks (CNNs)

  1. Building CNNs with Conv2D, MaxPooling2D, Flatten

  2. Image classification pipeline

  3. Transfer learning with pre-trained models (VGG16, ResNet)

  4. Fine-tuning and freezing layers

  5. Visualizing CNN filters and activations

09

Module 9: Recurrent Neural Networks (RNNs)

  1. Sequence data handling with RNNs

  2. LSTM and GRU layers

  3. Text classification with RNNs

  4. Embedding layers and tokenizer

  5. Sequence padding and batching

10

Module 10: Natural Language Processing with TensorFlow

  1. Text preprocessing with TensorFlow and Keras

  2. Word embeddings (Word2Vec, GloVe, Keras Embedding)

  3. Text classification and sentiment analysis

  4. Building Seq2Seq models for translation

  5. Introduction to Transformers in TensorFlow

11

Module 11: Model Evaluation and Visualization

  1. Accuracy, precision, recall, F1 score

  2. ROC curves and confusion matrices

  3. TensorBoard setup and usage

  4. Logging metrics and visualizing training

12

Module 12: Saving and Loading Models

  1. Saving entire models, weights, and architecture

  2. Model serialization with HDF5 and SavedModel

  3. Model versioning

  4. Loading models for inference

13

Module 13: TensorFlow for Deployment

  1. Exporting models for TensorFlow Serving

  2. Creating REST APIs using Flask or FastAPI

  3. TensorFlow Lite for mobile and edge deployment

  4. TensorFlow.js for deploying in browsers

14

Module 14: Advanced Topic

  1. Custom layers and models using tf.keras.Layer

  2. Writing custom training loops with gradient tape

  3. GANs in TensorFlow

  4. Autoencoders

  5. Introduction to Reinforcement Learning using TensorFlow Agents

15

Module 15: Tools and Ecosystem

  1. TensorBoard

  2. TensorFlow Hub for pre-trained models

  3. TensorFlow Model Garden

  4. TensorFlow Extended (TFX) for ML pipelines

16

Module 16: Capstone Projects

  1. Image classifier for plant disease detection

  2. Sentiment analysis on movie or product reviews

  3. Build a chatbot using RNN or Transformer

  4. Deploy a trained model on mobile using TFLite

  5. Create a full ML pipeline using TF + Flask + Web UI

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