Thank you so much for your valuable information.
I am so happy that someone replied me 
I am going to try IMDB in dl2,
On the other hand, I am receiving the below error for the lesson4:
---------------------------------------------------------------------------
NotImplementedError Traceback (most recent call last)
<ipython-input-24-357a8890c905> in <module>()
----> 1 learner.fit(3e-3, 4, wds=1e-6, cycle_len=1, cycle_mult=2)
~~fastai/courses/dl1/fastai/learner.py in fit(self, lrs, n_cycle, wds, **kwargs)
285 self.sched = None
286 layer_opt = self.get_layer_opt(lrs, wds)
--> 287 return self.fit_gen(self.model, self.data, layer_opt, n_cycle, **kwargs)
288
289 def warm_up(self, lr, wds=None):
~~fastai/courses/dl1/fastai/learner.py in fit_gen(self, model, data, layer_opt, n_cycle, cycle_len, cycle_mult, cycle_save_name, best_save_name, use_clr, use_clr_beta, metrics, callbacks, use_wd_sched, norm_wds, wds_sched_mult, use_swa, swa_start, swa_eval_freq, **kwargs)
232 metrics=metrics, callbacks=callbacks, reg_fn=self.reg_fn, clip=self.clip, fp16=self.fp16,
233 swa_model=self.swa_model if use_swa else None, swa_start=swa_start,
--> 234 swa_eval_freq=swa_eval_freq, **kwargs)
235
236 def get_layer_groups(self): return self.models.get_layer_groups()
~~fastai/courses/dl1/fastai/model.py in fit(model, data, n_epochs, opt, crit, metrics, callbacks, stepper, swa_model, swa_start, swa_eval_freq, **kwargs)
127 batch_num += 1
128 for cb in callbacks: cb.on_batch_begin()
--> 129 loss = model_stepper.step(V(x),V(y), epoch)
130 avg_loss = avg_loss * avg_mom + loss * (1-avg_mom)
131 debias_loss = avg_loss / (1 - avg_mom**batch_num)
~~fastai/courses/dl1/fastai/model.py in step(self, xs, y, epoch)
46 def step(self, xs, y, epoch):
47 xtra = []
---> 48 output = self.m(*xs)
49 if isinstance(output,tuple): output,*xtra = output
50 if self.fp16: self.m.zero_grad()
~~fastai/venv/lib/python3.6/site-packages/torch/nn/modules/module.py in __call__(self, *input, **kwargs)
489 result = self._slow_forward(*input, **kwargs)
490 else:
--> 491 result = self.forward(*input, **kwargs)
492 for hook in self._forward_hooks.values():
493 hook_result = hook(self, input, result)
~~fastai/venv/lib/python3.6/site-packages/torch/nn/modules/container.py in forward(self, input)
89 def forward(self, input):
90 for module in self._modules.values():
---> 91 input = module(input)
92 return input
93
~~fastai/venv/lib/python3.6/site-packages/torch/nn/modules/module.py in __call__(self, *input, **kwargs)
489 result = self._slow_forward(*input, **kwargs)
490 else:
--> 491 result = self.forward(*input, **kwargs)
492 for hook in self._forward_hooks.values():
493 hook_result = hook(self, input, result)
~~fastai/courses/dl1/fastai/lm_rnn.py in forward(self, input)
84 self.reset()
85
---> 86 emb = self.encoder_with_dropout(input, dropout=self.dropoute if self.training else 0)
87 emb = self.dropouti(emb)
88
~~fastai/venv/lib/python3.6/site-packages/torch/nn/modules/module.py in __call__(self, *input, **kwargs)
489 result = self._slow_forward(*input, **kwargs)
490 else:
--> 491 result = self.forward(*input, **kwargs)
492 for hook in self._forward_hooks.values():
493 hook_result = hook(self, input, result)
~~fastai/courses/dl1/fastai/rnn_reg.py in forward(self, words, dropout, scale)
174 if padding_idx is None: padding_idx = -1
175
--> 176 X = self.embed._backend.Embedding.apply(words,
177 masked_embed_weight, padding_idx, self.embed.max_norm,
178 self.embed.norm_type, self.embed.scale_grad_by_freq, self.embed.sparse)
~~fastai/venv/lib/python3.6/site-packages/torch/nn/backends/backend.py in __getattr__(self, name)
8 fn = self.function_classes.get(name)
9 if fn is None:
---> 10 raise NotImplementedError
11 return fn
12
NotImplementedError: