TensorFlow Tutorial

TensorFlow Tutorials Roadmap

What is TensorFlow?
- Understanding TensorFlow as an open-source library for numerical computation using data flow graphs, primarily for machine learning.
- Key features and benefits (flexibility, scalability, large community, support for various platforms).
- History and evolution of TensorFlow.
- When to use TensorFlow.
Understanding Machine Learning and Deep Learning Concepts:
- Brief overview of supervised, unsupervised, and reinforcement learning.
- What is a neural network?
- Basic concepts like neurons, layers, activation functions.
Setting up Your Environment:
- Installing TensorFlow (using pip or conda).
- Understanding the difference between TensorFlow CPU and GPU versions.
- Using environments (virtual environments or conda environments).
- Introduction to Jupyter Notebooks or Google Colab for interactive coding.
Your First TensorFlow Program: Basic Operations
- Importing TensorFlow.
- Creating Tensors (constants, variables).
- Performing basic tensor operations (addition, multiplication).
- Understanding the concept of graphs (in older TensorFlow versions) and Eager Execution.

Understanding Tensors:
- What is a tensor? (Multi-dimensional array).
- Tensor ranks and shapes.
- Data types of tensors.
- Creating tensors (tf.constant, tf.Variable, tf.zeros, tf.ones, tf.random).
Variables:
- Understanding the need for variables (trainable parameters).
- Creating and managing variables (tf.Variable).
- Assigning values to variables.
Basic Tensor Operations:
- Mathematical operations (tf.add, tf.multiply, tf.matmul).
- Reshaping and transposing tensors.
- Indexing and slicing tensors.
Eager Execution:
- Understanding Eager Execution (default in TensorFlow 2.x).
- Executing operations immediately.

Introduction to Keras:
- Understanding Keras as a high-level API for building and training neural networks in TensorFlow.
- Sequential API vs. Functional API.
Building a Simple Sequential Model:
- Creating a tf.keras.Sequential model.
- Adding layers (Dense, Flatten, etc.).
- Understanding activation functions (ReLU, Sigmoid, Softmax).
Compiling the Model:
- Choosing an optimizer (Adam, SGD, etc.).
- Choosing a loss function (Categorical Crossentropy, Sparse Categorical Crossentropy, Mean Squared Error, etc.).
- Specifying metrics (Accuracy, etc.).
Training the Model:
- Using the model.fit() method.
- Understanding epochs, batch size, and validation data.
Evaluating the Model:
- Using the model.evaluate() method.
Making Predictions:
- Using the model.predict() method.

Loading and Preprocessing Data:
- Loading datasets from TensorFlow Datasets (TFDS).
- Loading data from files (CSV, images, etc.).
- Basic preprocessing steps (scaling, normalization, one-hot encoding).
Using the tf.data API:
- Creating datasets from arrays or tensors.
- Applying transformations to datasets (map, batch, shuffle, repeat).
- Building efficient data pipelines.

Introduction to CNNs:
- Understanding the need for CNNs for image data.
- Convolutional layers.
- Pooling layers (MaxPooling, AveragePooling).
Building a CNN Model using Keras.
Training and Evaluating a CNN on an Image Dataset (e.g., MNIST, CIFAR-10).
Understanding Image Augmentation.

Introduction to RNNs:
- Understanding the need for RNNs for sequential data (text, time series).
- Basic RNN cells.
- Vanishing Gradient Problem.
Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU):
- Understanding the architecture and benefits of LSTMs and GRUs.
Building an RNN/LSTM/GRU Model using Keras.
Training and Evaluating an RNN/LSTM/GRU on a Sequential Dataset (e.g., text classification, time series prediction).

Saving and Loading Models:
- Saving in TensorFlow SavedModel format.
- Saving weights only.
- Loading models and weights.
Introduction to TensorFlow Lite (for mobile and embedded devices).
Introduction to TensorFlow.js (for web browsers).
Introduction to TensorFlow Extended (TFX) for production pipelines (Optional).

Custom Layers and Models:
- Creating custom Keras layers.
- Subclassing tf.keras.Model.
Custom Training Loops:
- Using tf.GradientTape for automatic differentiation.
- Implementing custom training logic.
Callbacks:
- Using Keras Callbacks (EarlyStopping, ModelCheckpoint, TensorBoard).
- Creating custom callbacks.
Transfer Learning:
- Using pre-trained models (e.g., VGG, ResNet, BERT).
- Fine-tuning pre-trained models.
Generative Models (e.g., GANs, VAEs) - Introduction.
Reinforcement Learning with TensorFlow (e.g., using TF-Agents) - Introduction.