TensorFlow Tutorial

TensorFlow is a powerful open-source machine-learning framework developed by Google, that empowers developers to construct and train ML models. It is used to implement machine learning and deep learning applications, for the development and research of fascinating ideas in artificial intelligence. TensorFlow is designed with the Python programming language, which makes it an easily understandable framework.

Table of Content

• What is TensorFlow?
• Why Choose TensorFlow for your Machine Learning Projects?
• 1. TensorFlow Introduction
• 2. TensorFlow installations
• 3. Tensorflow Basics
• 4. Preprocessing, Model Construction, and Training in TensorFlow
• 5. TensorBoard
• 6. Multi-GPU and Distributed training using Tensorflow
• 7. TensorFlow for Computer vision Tasks
• 8. TensorFlow for NLP Tasks
• 9. Cutting-Edge Applications and Future Trends in TensorFlow
• Applications of TensorFlow
• FAQs – TensorFlow

What is TensorFlow?

TensorFlow is a software library that excels in numerical computations and large-scale data analysis. It underpins various Google products, including search and recommendations, demonstrating its real-world prowess. TensorFlow offers two primary interfaces for building ML models:

• Keras: A high-level API ideal for beginners and rapid prototyping. Keras abstracts away complexities, enabling you to focus on model design and experimentation.
• TensorFlow Core API: A low-level, more granular approach offering finer control over model architecture and computations. This is preferred for experienced developers or situations requiring extensive customization.

Why Choose TensorFlow for your Machine Learning Projects?

TensorFlow reigns supreme in the ML landscape due to several compelling factors:

• Flexibility: TensorFlow caters to diverse ML tasks, encompassing image recognition, natural language processing (NLP), and recommender systems.
• Scalability: Seamlessly handle large datasets and complex models on various hardware platforms, from single machines to powerful TPUs (Tensor Processing Units) in the cloud.
• Open-Source Community: Benefit from a vibrant community of developers, extensive documentation, and a plethora of tutorials and resources to expedite your learning.
• TensorBoard Visualization: TensorBoard, a built-in visualization suite, aids in monitoring model training progress, analyzing performance metrics, and debugging issues.

Prerequisites

• Python for Data Science
• Math for Data Science
• Data Analysis
• Machine Learning
• Deep Learning

1. TensorFlow Introduction

• What is neural network?
• What is the framework?
• What is TensorFlow?
• Architecture of TensorFlow
• Introduction to TensorFlow Lite
• Difference between TensorFlow and Keras

2. TensorFlow installations

• System requirements for TensorFlow
• Install TensorFlow using pip
• Install TensorFlow using Anaconda
• What is CUDA and cuDNN driver and How can we choose the most suitable driver for our system
• TensorFlow installations with GPU support
• TensorFlow installations in Linux
• TensorFlow installations in Ubuntu
• TensorFlow installations in macOS
• TensorFlow installations in Windows
• Install TensorFlow with Docker

The most straightforward method for tensorFlow installations using pip

TensorFlow installations using pip CPU-only version

!pip install tensorflow

TensorFlow installations using pip GPU version

!pip install tensorflow-gpu

Check TensorFlow installations

Output:

TensorFlow version - 2.15.0
TensorFlow is installed as CPU version.

3. Tensorflow Basics

1. Tensorflow Data Structure
   • Tensor in TensorFLow
     • Tensor using different Data types
     • Shape, Rank, Axis, Size of Tensor
   • Basic Tensor Operations in Tensorflow
     • Tensor Indexing in Tensorflow
     • Tensor Reshaping in Tensorflow
     • Tensor Transpose in Tensorflow
     • Tensor Broadcasting in Tensorflow
     • Random number generation in Tensorflow
     • Tensor Slicing in Tensorflow
     • Bitwise operations in Tensorflow
       • Tensorflow bitwise.bitwise_or()
       • Tensorflow bitwise.invert()
       • Tensorflow bitwise.bitwise_and()
       • Tensorflow bitwise.bitwise_xor()
       • Tensorflow bitwise.right_shift()
       • Tensorflow bitwise.left_shift()
     • Tensor Concatenations in Tensorflow
       • tensorflow.concat()
   • Ragged tensors in Tensorflow
   • Sparse tensors in Tensorflow
   • String tensors in Tensorflow
   • Variables in Tensorflow
   • TensorArray in Tensorflow
2. Tensorflow Numerical functions
   • tf.math in Tensorflow
     • Tensorflow math.add_n()
     • tensorflow.math.subtract()
     • Tensorflow math.accumulate_n()
     • tensorflow.math.multiply()
     • tensorflow.math.multiply_no_nan()
     • tensorflow.math.scalar_mul()
     • tensorflow.math.top_k()
     • tensorflow.math.less()
     • tensorflow.math.less_equal()
     • tensorflow.math.reduce_max()
     • tensorflow.math.argmax()
     • tensorflow.math.squared_difference()
     • tensorflow.math.rsqrt()
     • tensorflow.math.conj()
     • tensorflow.math.l2_normalize()
     • tensorflow.math.floormod()
     • tensorflow.math.asin()
     • tensorflow.math.asinh()
     • tensorflow.math.sign()
     • tensorflow.math.softplus()
     • tensorflow.math.lbeta()
     • tensorflow.math.is_inf()
     • tensorflow.math.segment_max()
     • tensorflow.math.negative()
     • tensorflow.math.betainc()
     • tensorflow.math.unsorted_segment_mean()
     • tensorflow.math.bessel_i0()
   • tf.linalg
   • tf.random
3. Graphs and functions in TensorFLow
   • tf.function
   • tf.Graph
4. Gradient and automatic differentiation
   • tf.GradientTape
     • tensorflow.GradientTape.gradient()
     • tensorflow.GradientTape.watch()
     • tensorflow.GradientTape.reset()
     • tensorflow.GradientTape.stop_recording()
   • Custom gradients (tf.custom_gradient) in TensorFLow
   • Multiple tapes in TensorFlow
   • Higher-Order gradients in TensorFlow
   • Jacobians in TensorFlow
     • tensorflow.GradientTape.jacobian()
     • tensorflow.GradientTape.batch_jacobian()

4. Preprocessing, Model Construction, and Training in TensorFlow

• Preprocessing layers in Tensorflow
  • Numerical features preprocessing:
    • tf.keras.layers.Normalization
    • tf.keras.layers.Discretization
    • tf.keras.layers.CategoryEncoding
    • tf.keras.layers.Hashing
    • tf.keras.layers.StringLookup
    • tf.keras.layers.IntegerLookup
    • Tensorflow Data loading functions
    • Sequential vs Functional APIs in TensorFlow
    • Modules (tf.Module) in TensorFLow
    • Tensorflow FLatten Layer (tf.keras.layers.Flatten)
    • Tensorflow Dropout Layer (tf.keras.layers.Dropout)
    • Dense layer (tf.keras.layers.Dense)
    • Activations functions
      • tf.nn.relu() and tf.nn.leaky_relu()
      • tf.keras.layers.Softmax
      • tf.nn.softplus()
      • tf.keras.optimizers.experimental.SGD
      • tf.keras.optimizers.Adam
      • tf.keras.optimizers.experimental.RMSprop
      • tf.keras.optimizers.experimental.Adagrad
      • tf.keras.optimizers.experimental.Adadelta
      • tf.keras.optimizers.experimental.Adamax
      • tf.keras.optimizers.experimental.Nadam
      • Tensorflow Custom Optimizers
      • tf.keras.losses.CategoricalCrossentropy
      • tf.keras.losses.BinaryCrossentropy
      • tf.keras.losses.MeanSquaredError
      • Tensorflow Custom Metrics
      • Build a linear regression model with the TensorFlow Core APIs
      • Train and evaluate the model
        • Keras callbacks methods
          • tf.keras.callbacks.ModelCheckpoint
          • tf.keras.callbacks.EarlyStopping
          • tf.keras.callbacks.TensorBoard
          • keras.callbacks.Callback
          • Linear Regression using TensorFLow
          • Softmax Regression using TensorFlow
          • Logistic regression for binary classification using Tensorflow
          • Multilayer perceptrons for multi class classification using Tensorflow
          • Singular value decomposition using Tensorflow

          5. TensorBoard

          • What is TensorBoard?
            • Installation and Setup
            • Basic Logging and Visualization
            • TensorBoard callback
            • How to use TensorBoard callback
            • How to use TensorBoard in Google Colab?
            • Understanding TensorBoard Dashboard
              • TensorBoard scalars
              • TensorBoard images
              • TensorBoard graphs
              • TensorBoard Histograms and Distributions
              • TensorBoard with TensorFlow Estimators
              • TensorBoard with Keras
              • TensorBoard with TensorFlow Serving

              6. Multi-GPU and Distributed training using Tensorflow

              • Data parallelism vs Model parallelism
              • DTensor in Tensorflow
              • Types of Distributed training strategies in Tensorflow
              • Data parallel training using Tensorflow

              7. TensorFlow for Computer vision Tasks

              • Image preprocessing
                • tf.keras.layers.Resizing
                • tf.keras.layers.Rescaling
                • tf.keras.layers.CenterCrop
                • Image data augmentation with TensorFLow
                  • tf.keras.preprocessing.image.ImageDataGenerator
                  • tf.keras.layers.RandomCrop
                  • tf.keras.layers.RandomFlip
                  • tf.keras.layers.RandomTranslation
                  • tf.keras.layers.RandomRotation
                  • tf.keras.layers.RandomZoom
                  • tf.keras.layers.RandomContrast
                  • tf.keras.preprocessing.image.random_brightness
                  • Convolutions layers
                    • tf.keras.layers.Conv1D
                    • tf.keras.layers.Conv2D
                    • tf.keras.layers.Conv3D()
                    • tf.keras.layers.DepthwiseConv1D
                    • tf.keras.layers.DepthwiseConv2D
                    • tf.keras.layers.Conv1DTranspose
                    • tf.keras.layers.Conv2DTranspose
                    • tf.keras.layers.Conv3DTranspose
                    • tf.keras.layers.SeparableConv1D
                    • tf.keras.layers.SeparableConv2D
                    • tf.keras.layers.SeparableConv3D
                    • tf.keras.layers.MaxPooling1D
                    • tf.keras.layers.MaxPooling2D
                    • tf.keras.layers.MaxPooling3D
                    • tf.keras.layers.AveragePooling1D
                    • tf.keras.layers.AveragePooling2D
                    • tf.keras.layers.AveragePooling2D
                    • tf.keras.layers.GlobalAveragePooling3D
                    • tf.keras.layers.GlobalMaxPooling3D
                    • Image Classifications using TensorFlow
                    • Object Detection using TensorFlow
                    • FaceMask Detection using TensorFlow
                    • Image segmentation using TensorFlow
                    • Deep Convolutional GAN for Image generations in TensorFlow
                    • Human Pose Detection using MoveNet with Tensorflowhub

                    8. TensorFlow for NLP Tasks

                    • Text preprocessing Layers
                      • tf.keras.preprocessing.text.Tokenizer
                      • tf.keras.preprocessing.text.hashing_trick
                      • tf.keras.preprocessing.text.one_hot
                      • tf.keras.preprocessing.text.text_to_word_sequence
                      • tf.keras.preprocessing.text.tokenizer_from_json
                      • tf.keras.layers.SimpleRNN
                      • tf.keras.layers.RNN
                      • tf.keras.layers.LSTM
                      • tf.keras.layers.LSTMCell
                      • tf.keras.layers.GRU
                      • tf.keras.layers.GRUCell
                      • tf.keras.layers.Attention
                      • tf.keras.layers.MultiHeadAttention
                      • ROUGE-L
                      • Text classifications
                        • SMS Spam Detection using TensorFlow
                        • Detecting Spam Emails Using Tensorflow
                        • Text generation with an LSTM
                        • Text Generation using Gated Recurrent Unit
                        • Machine Translation with Attention Mechanism Seq2Seq model
                        • Machine translation with a Transformer and Keras
                        • Image captioning with visual attention using Transformer with TensorFlow

                        9. Cutting-Edge Applications and Future Trends in TensorFlow

                        • TensorFlow Probability for probabilistic reasoning and statistical analysis
                        • XLA (Accelerated Linear Algebra)
                        • TensorFlow Model Optimization Toolkit
                        • TFX pipeline for ML model deployement productions
                        • TensorFlow Hub: A pre-trained ML model repository
                        • TensorFlow Decision Forests (TF-DF)
                          • Build, train and evaluate models with TensorFlow Decision Forests
                          • Train a Deep Q Network with TF-Agents

                          Applications of TensorFlow

                          TensorFlow’s versatility extends across a vast array of real-world applications:

                          • Image Recognition: Develop image classification models to identify objects, faces, or scenes in images.
                          • Natural Language Processing (NLP): Construct models for sentiment analysis, machine translation, or text summarization.
                          • Recommender Systems: Craft personalized recommendations for products, movies, or music based on user preferences and behavior.
                          • Time Series Forecasting: Predict future trends in time-based data, such as stock prices or weather patterns.