install Keras and Tensorflow for RStudio which works for most machines. However, if you have a PC which has a CPU purchased before 2012, the current versions(1.10-2.1) of Tensorflow could not be loaded loaded as AVX instructions set is needed from Tensorflow 1.6 and later. Please ensure your machine was purchased in 2012 or later. Step 1 Follow the document or video to install Anaconda3 and RStudio: this prompt: 1 conda install python=3.6 2 conda install -c conda-forge tensorflow 3 conda install -c r r-tensorflow 4 conda install -c conda-forge r-keras C) Open the RStudio and run the

tool. • Managing one-step installation of tools that are more challenging to install (such as TensorFlow or IRAF). • Allowing you to provide your environment to other people across different platforms conda. • Providing commonly used data science libraries and tools, such as R, NumPy, SciPy, and TensorFlow. These are built using optimized, hardware-specific libraries (such as Intel’s MKL or NVIDIA’s

tool. • Managing one-step installation of tools that are more challenging to install (such as TensorFlow or IRAF). • Allowing you to provide your environment to other people across different platforms conda. • Providing commonly used data science libraries and tools, such as R, NumPy, SciPy, and TensorFlow. These are built using optimized, hardware-specific libraries (such as Intel’s MKL or NVIDIA’s

tool. • Managing one-step installation of tools that are more challenging to install (such as TensorFlow or IRAF). • Allowing you to provide your environment to other people across different platforms conda. • Providing commonly used data science libraries and tools, such as R, NumPy, SciPy, and TensorFlow. These are built using optimized, hardware-specific libraries (such as Intel’s MKL or NVIDIA’s

tool. • Managing one-step installation of tools that are more challenging to install (such as TensorFlow or IRAF). • Allowing you to provide your environment to other people across different platforms conda. • Providing commonly used data science libraries and tools, such as R, NumPy, SciPy, and TensorFlow. These are built using optimized, hardware-specific libraries (such as Intel’s MKL or NVIDIA’s

tool. • Managing one-step installation of tools that are more challenging to install (such as TensorFlow or IRAF). • Allowing you to provide your environment to other people across different platforms conda. • Providing commonly used data science libraries and tools, such as R, NumPy, SciPy, and TensorFlow. These are built using optimized, hardware-specific libraries (such as Intel’s MKL or NVIDIA’s

tool. • Managing one-step installation of tools that are more challenging to install (such as TensorFlow or IRAF). • Allowing you to provide your environment to other people across different platforms conda. • Providing commonly used data science libraries and tools, such as R, NumPy, SciPy, and TensorFlow. These are built using optimized, hardware-specific libraries (such as Intel’s MKL or NVIDIA’s

tool. • Managing one-step installation of tools that are more challenging to install (such as TensorFlow or IRAF). • Allowing you to provide your environment to other people across different platforms conda. • Providing commonly used data science libraries and tools, such as R, NumPy, SciPy, and TensorFlow. These are built using optimized, hardware-specific libraries (such as Intel’s MKL or NVIDIA’s

tool. • Managing one-step installation of tools that are more challenging to install (such as TensorFlow or IRAF). • Allowing you to provide your environment to other people across different platforms conda. • Providing commonly used data science libraries and tools, such as R, NumPy, SciPy, and TensorFlow. These are built using optimized, hardware-specific libraries (such as Intel’s MKL or NVIDIA’s

tool. • Managing one-step installation of tools that are more challenging to install (such as TensorFlow or IRAF). • Allowing you to provide your environment to other people across different platforms conda. • Providing commonly used data science libraries and tools, such as R, NumPy, SciPy, and TensorFlow. These are built using optimized, hardware-specific libraries (such as Intel’s MKL or NVIDIA’s