TensorFlow: Basic to Advanced - 100 Projects in 100 Days

Explore how tensors serve as the primary data structure in TensorFlow, detailing ranks from scalar to high rank, and explain shape, dtype, and values.

Explore tensor operations in TensorFlow, including arithmetic, mathematical functions, reductions, matrix operations, indexing, slicing, and broadcasting, to build and analyze machine learning models.

Explore constants, variables, and placeholders in TensorFlow, defining immutable tensors with tf.constant, mutable model parameters with a variable tensor, and the deprecation of placeholders in favor of eager execution.

Define operations and tensors to form a TensorFlow computational graph and execute it in a session. Visualize the graph with TensorBoard, log results with FileWriter, and optimize performance and portability.

Learn to create and run TensorFlow sessions, comparing graph mode with eager execution. Understand session usage in 1.x and 2.x, including sess.run, with statements, and resource management.

Create, manipulate, and execute TensorFlow graphs and sessions to build and train models, using default and multiple graphs, session options, and proper resource management.

Build a simple feedforward neural network with TensorFlow by defining architecture, input and output layers, hidden layers, activation functions, loss function, and the Adam optimizer, then train and evaluate.

Explore how activation functions introduce nonlinearity in neural networks to learn complex patterns, with linear, sigmoid, tanh, ReLU, leaky ReLU, and softmax in TensorFlow.

Explore essential components of neural network training: loss functions and optimizers, including mean squared error, binary cross entropy, categorical cross entropy, and sparse categorical cross entropy, plus common optimizers.

Learn the Keras API, a high level neural network interface for building, training, and deploying models with TensorFlow, Theano, or cntk, using layers, activations, losses, optimizers, metrics, and callbacks.

Build complex neural networks with Keras using sequential and functional APIs, combining conv2d, dense, pooling, and flatten layers trained with Adam and sparse categorical cross entropy for various tasks.

learn how to train and evaluate neural networks by preparing data, building a convolutional neural network with the mNIST dataset, compiling, training, evaluating, visualizing training history, and making predictions.

Explore convolutional neural networks and their role in image classification, object detection, and segmentation, using convolutional and pooling layers, relu activations, and transfer learning.

Build and train convolutional neural networks with TensorFlow using Keras, stacking conv2d, max pooling, flatten, and dense layers, then evaluate on MNIST with sparse categorical cross entropy.

Explore transfer learning with pre-trained CNNs that improve performance on small data tasks via feature extraction or fine-tuning, implemented in TensorFlow Keras.

Explore recurrent neural networks for sequential data, using a hidden state to model temporal dependencies. Learn LSTM and GRU variants, backpropagation through time, and gradient clipping to combat vanishing gradients.

Explore recurrent neural networks in language modeling and time series prediction. Apply them to text generation, machine translation, speech recognition, sentiment analysis, and forecasting for stock, weather, and healthcare.

Save and load TensorFlow models for production, using entire model or architecture and weights, and deploy with TensorFlow serving or TensorFlow Lite, choosing formats like HDF5, JSON, YAML, or protobuf.

deploy TensorFlow models using a flexible, high performance serving system that supports versioning, monitoring, and scalable production deployment via HTTP or gRPC.

Learn to deploy machine learning models on mobile and embedded devices using tensorflow lite, including model conversion, running inferences with interpreters, and optimization techniques for low latency and small footprint.

Explore how TensorFlow enables distributed computing across devices or machines to accelerate training and inference, using data, model, and pipeline parallelism, distributed execution, and tf.distribute.strategy.

Leverage TensorFlow's distributed execution framework to scale computations across devices and machines, including tf.distribute.strategy with MirroredStrategy, ParameterServerStrategy, and MultiWorkerMirroredStrategy, plus data/model/pipeline parallelism, distributed optimizers, fault tolerance, and deployment tools.

Scale TensorFlow models by deploying TensorFlow serving on Kubernetes, containerizing models, and configuring load balancing, monitoring, and versioning for robust, scalable production serving.

explore tfx, the end-to-end platform for building, validating, and deploying production machine learning pipelines at scale, with components like examplegen, transform, trainer, and evaluator.

Learn to build end-to-end ml pipelines with tfx, from data ingestion and pre-processing to model training, deployment, and monitoring, using components like examplegen, transform, trainer, and pusher.

Build robust ml pipelines by validating models with the evaluator, transforming data with the transform component, and serving models to production with the pusher across platforms.

Explore real world image classification applications across healthcare, automotive, retail, agriculture, and security, including medical imaging diagnosis, autonomous driving, product recognition, crop monitoring, and facial recognition.

Explore natural language processing with machine learning and deep learning to tackle text classification, sentiment analysis, machine translation, named entity recognition, and question answering across healthcare, finance, and customer service.

Explore how recommender systems use machine learning to analyze user preferences, behaviors, and history to deliver personalized product, content, and experience recommendations across industries.

Explore object detection with convolutional neural networks to locate and classify objects in images and video, with applications in autonomous vehicles, retail, and healthcare.

Build a sentiment analysis model with TensorFlow by preparing data, tokenizing text, and padding sequences. Train, evaluate using accuracy, precision, recall, and F1, and deploy REST APIs with TensorFlow serving.

Learn to build an image recognition system with TensorFlow, from dataset selection and preprocessing to CNN architectures, training, evaluation, deployment, and continuous improvement.

Develop a time series prediction model with TensorFlow Keras, covering data collection and preprocessing, RNN architectures (LSTM/GRU), training, evaluation with MSE/MAE, and deployment via TensorFlow serving or Flask.

Develop chatbots using natural language processing and encoder-decoder sequence models. Learn data collection, preprocessing, training, evaluation with bleu score, and interactive deployment.

Explore generative adversarial networks, with a generator and discriminator trained adversarially to produce realistic data, including variants like DCGAN, CGAN, WGAN, CycleGAN, and applications in image generation and image-to-image translation.

Explore reinforcement learning with TensorFlow, where an agent learns by interacting with an environment to maximize rewards using Q-learning, DQN, policy gradients, and PPO.

Explore how TensorFlow quantum enables hybrid classical quantum models, using qubits, gates, and variational circuits for tasks like quantum optimization, chemistry, and learning with quantum neural networks.

Explore TensorFlow documentation and tutorials, including API docs, official tutorials, GitHub resources, Colab notebooks, and community contributions, to learn, update your skills, and build projects.

Discover popular TensorFlow online courses and books that teach basics, CNNs, and NLP. Explore resources from Coursera, Udacity, edX, DataCamp, and key TensorFlow books.

Join the vibrant TensorFlow community across forums, GitHub, LinkedIn, and meetups. Share knowledge, seek support, contribute code, pull requests, and tutorials, and participate in events to advance learning.

Master TensorFlow fundamentals from tensors and computational graphs to neural networks, training, deployment, and extensions like TF and TFQ for real-world machine learning.

Advance your TensorFlow journey by mastering transfer learning, hyperparameter tuning, and advanced architectures like transformers and graph neural networks, while exploring add-ons, contributing to open source, and building real-world projects.