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TensorFlow is a powerful open-source framework for building and deploying machine learning models. Learning TensorFlow enables you to create sophisticated neural networks for tasks like image recognition, natural language processing, and predictive analytics. By mastering TensorFlow, you gain valuable skills that can enhance your career prospects in the rapidly growing field of AI and machine learning. This article lists the top TensorFlow courses that can help you gain the expertise needed to excel in the field of AI and machine learning.
Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning
This course teaches you how to use TensorFlow to build scalable AI models, starting with a soft introduction to Machine Learning and Deep Learning principles. Throughout the course, you’ll progress from basic programming skills to solving complex computer vision problems, guided by videos, readings, quizzes, and programming assignments.
Intro to TensorFlow for Deep Learning
This course provides a hands-on introduction to deep learning with TensorFlow and Keras, covering neural networks, CNNs, transfer learning, and time series forecasting. It also delves into NLP with tokenization, embeddings, and RNNs and concludes with deploying models using TensorFlow Lite.
TensorFlow fundamentals
This course introduces the fundamentals of deep learning with TensorFlow, covering key concepts and practical knowledge for building machine learning models. Modules include building neural networks with Keras, computer vision, natural language processing, audio classification, and customizing models with lower-level TensorFlow code.
Introduction to Neural Networks with TensorFlow
This course teaches the fundamentals of neural networks using Python and TensorFlow, focusing on techniques like gradient descent and backpropagation. You’ll learn to build and train deep learning models, culminating in a project to create an image classifier.
Introduction to TensorFlow Lite
This course introduces TensorFlow Lite and covers its deployment on various platforms. The course provides an overview of TensorFlow Lite and instructions on deploying models on Android, iOS with Swift, and IoT devices.
Convolutional Neural Networks in TensorFlow
This course advances your skills in computer vision by teaching you to handle real-world images, visualize convolutions, and improve model performance with techniques like augmentation, dropout, and transfer learning. It covers various aspects, from using larger datasets to preventing overfitting and moving beyond binary classification.
Natural Language Processing in TensorFlow
This course focuses on building natural language processing systems using TensorFlow. You’ll learn to process text, tokenize and vectorize sentences, apply RNNs, GRUs, and LSTMs, and even train an LSTM to generate original poetry. The course includes videos, readings, quizzes, and programming assignments to deepen your understanding of NLP techniques and neural networks.
TensorFlow on Google Cloud
This course covers designing TensorFlow data pipelines, building and improving ML models with TensorFlow and Keras, and writing scalable and specialized ML models. You’ll learn about TensorFlow framework components, data processing for training, deep neural networks, and model scaling with Vertex AI.
IBM: Deep Learning with Tensorflow
This course covers foundational TensorFlow concepts, including main functions, operations, and execution pipelines. You’ll learn to apply TensorFlow for curve fitting, regression, classification, and error minimization and understand different deep architectures like Convolutional Networks, Recurrent Networks, and Autoencoders. The course also teaches backpropagation for tuning weights and biases during neural network training.
Deep Learning Specialization
This course teaches you to build and train various neural network architectures using TensorFlow, including CNNs, RNNs, LSTMs, and Transformers. You’ll master theoretical concepts and apply them to real-world tasks like speech recognition, chatbots, and NLP. It helps gain skills in training deep neural networks, optimizing models, and implementing advanced techniques in TensorFlow for various AI applications.
Sequences, Time Series, and Prediction
This course teaches you to build time series models using TensorFlow. You’ll learn to prepare time series data, use RNNs and 1D ConvNets for prediction, and apply these techniques to build a sunspot prediction model with real-world data.
TensorFlow 2 for Deep Learning Specialization
This specialization is for machine learning researchers and practitioners seeking practical TensorFlow skills. It covers building, training, and evaluating deep learning models, developing custom models with TensorFlow APIs, and creating probabilistic models using TensorFlow Probability. Prerequisites include Python 3, machine learning and deep learning concepts, and a solid foundation in probability and statistics.
TensorFlow: Advanced Techniques Specialization
This specialization teaches advanced TensorFlow techniques to software and machine learning engineers. You’ll learn to build non-sequential model types, optimize training, and tackle advanced computer vision tasks. The four courses cover the Functional API, optimization with GradientTape and Autograph, object detection, and generative deep learning, including Style Transfer, Auto Encoding, VAEs, and GANs.
TensorFlow: Data and Deployment Specialization
This course teaches you to deploy machine learning models across various devices and platforms using TensorFlow. You’ll learn to train and run models in browsers and mobile apps, use TensorFlow data services, and apply TensorFlow Serving, Hub, and TensorBoard in different deployment scenarios. The courses cover browser-based models with TensorFlow.js, device-based models with TensorFlow Lite, and data pipelines with TensorFlow Data Services.
Practical Machine Learning with Tensorflow
This course covers the basics and advanced topics in TensorFlow and machine learning. It includes lectures on TensorFlow overview, machine learning processes, gradient descent, model evaluation, deep learning, data pipelines, text and image classification, CNNs, RNNs, and transfer learning.
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