Tensorflow Basics
TensorFlow Basics encompass the fundamental concepts and functionalities of the TensorFlow library. These fundamentals are the cornerstones for effectively understanding and using TensorFlow. Students study fundamental tensor operations, understand the meaning of variables, and grasp the graph execution model of TensorFlow. These fundamentals open the door to using TensorFlow effectively for a variety of machine learning applications. Gaining expertise in building, training, and deploying machine learning models with TensorFlow can only be attained through mastery of the TensorFlow Basics.. Under TensorFlow Basics, you will typically cover the following topics:
- 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
- Tensor Concatenations in Tensorflow
- Ragged tensors in Tensorflow
- Sparse tensors in Tensorflow
- String tensors in Tensorflow
- Variables in Tensorflow
- TensorArray in Tensorflow
- Tensor in TensorFLow
- 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
- tf.math in Tensorflow
- Graphs and functions in TensorFLow
- tf.function
- tf.Graph
- Gradient and automatic differentiation
- tf.GradientTape
- Custom gradients (tf.custom_gradient) in TensorFLow
- Multiple tapes in TensorFlow
- Higher-Order gradients in TensorFlow
- Jacobians in TensorFlow
TensorFlow Tutorial
TensorFlow has evolved as a popular deep learning framework, allowing developers and academics to quickly design and deploy machine learning models. In this complete TensorFlow Tutorial, we’ll explore TensorFlow with python from its fundamentals to advanced approaches, equipping with the knowledge and skills to harness the full potential of this powerful framework.
Table of Content
- TensorFlow for Neural Network
- 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
- Conclusion
- Frequently Asked Questions on TensorFlow for Neural Network