Practical Solutions with Top TensorFlow Courses
Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning
This course provides a soft introduction to Machine Learning and Deep Learning principles, guiding you from basic programming skills to solving complex computer vision problems.
Intro to TensorFlow for Deep Learning
This hands-on course covers deep learning with TensorFlow and Keras, including neural networks, CNNs, transfer learning, and time series forecasting. It also explores NLP and deployment using TensorFlow Lite.
TensorFlow fundamentals
This course introduces the fundamentals of deep learning with TensorFlow, covering modules on 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
Learn the fundamentals of neural networks using Python and TensorFlow, focusing on techniques like gradient descent and backpropagation, 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, providing an overview and instructions for deploying models on Android, iOS with Swift, and IoT devices.
Convolutional Neural Networks in TensorFlow
Advance your computer vision skills by learning to handle real-world images, visualize convolutions, and improve model performance with techniques like augmentation, dropout, and transfer learning.
Natural Language Processing in TensorFlow
Focus on building natural language processing systems using TensorFlow, covering text processing, tokenization, RNNs, GRUs, LSTMs, and even training an LSTM to generate original poetry.
TensorFlow on Google Cloud
Covering TensorFlow data pipelines, building and improving ML models with TensorFlow and Keras, and writing scalable and specialized ML models, this course provides insights into TensorFlow framework components and model scaling with Vertex AI.
IBM: Deep Learning with Tensorflow
This course covers foundational TensorFlow concepts, including main functions, operations, and execution pipelines, along with deep architectures like Convolutional Networks, Recurrent Networks, and Autoencoders, and backpropagation for tuning weights and biases.
Deep Learning Specialization
Build and train various neural network architectures using TensorFlow, including CNNs, RNNs, LSTMs, and Transformers, and apply them to real-world tasks like speech recognition, chatbots, and NLP.
Sequences, Time Series, and Prediction
Learn to build time series models using TensorFlow, 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, covering building, training, and evaluating deep learning models, developing custom models with TensorFlow APIs, and creating probabilistic models using TensorFlow Probability.
TensorFlow: Advanced Techniques Specialization
This specialization teaches advanced TensorFlow techniques to software and machine learning engineers, including non-sequential model types, optimization with GradientTape and Autograph, object detection, and generative deep learning.
TensorFlow: Data and Deployment Specialization
This course teaches deploying machine learning models across various devices and platforms using TensorFlow, covering 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 basics and advanced topics in TensorFlow and machine learning, including lectures on TensorFlow overview, machine learning processes, gradient descent, deep learning, data pipelines, text and image classification, CNNs, RNNs, and transfer learning.
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